essay about scientific progress

We Need a New Science of Progress

Humanity needs to get better at knowing how to get better.

In 1861, the American scientist and educator William Barton Rogers published a manifesto calling for a new kind of research institution. Recognizing the “daily increasing proofs of the happy influence of scientific culture on the industry and the civilization of the nations,” and the growing importance of what he called “Industrial Arts,” he proposed a new organization dedicated to practical knowledge. He named it the Massachusetts Institute of Technology.

Rogers was one of a number of late-19th-century reformers who saw that the United States’ ability to generate progress could be substantially improved. These reformers looked to the successes of the German university models overseas and realized that a combination of focused professorial research and teaching could be a powerful engine for advance in research. Over the course of several decades, the group—Rogers, Charles Eliot, Henry Tappan, George Hale, John D. Rockefeller, and others—founded and restructured many of what are now America’s best universities, including Harvard, MIT, Stanford, Caltech, Johns Hopkins, the University of Chicago, and more. By acting on their understanding, they engaged in a kind of conscious “progress engineering.”

Progress itself is understudied. By “progress,” we mean the combination of economic, technological, scientific, cultural, and organizational advancement that has transformed our lives and raised standards of living over the past couple of centuries. For a number of reasons, there is no broad-based intellectual movement focused on understanding the dynamics of progress, or targeting the deeper goal of speeding it up. We believe that it deserves a dedicated field of study. We suggest inaugurating the discipline of “Progress Studies.”

Before digging into what Progress Studies would entail, it’s worth noting that we still need a lot of progress. We haven’t yet cured all diseases; we don’t yet know how to solve climate change; we’re still a very long way from enabling most of the world’s population to live as comfortably as the wealthiest people do today; we don’t yet understand how best to predict or mitigate all kinds of natural disasters; we aren’t yet able to travel as cheaply and quickly as we’d like; we could be far better than we are at educating young people. The list of opportunities for improvement is still extremely long.

Read: The 50 greatest breakthroughs since the wheel

Those are major challenges. A lot of progress can also come from smaller advances: Thousands of lesser improvements that together build upon one another can together represent an enormous advance for society. For example, if our discoveries and inventions improve standards of living by 1 percent a year, children will by adulthood be 35 percent better off than their parents. If they improve livelihoods at 3 percent a year, those same children will grow up to be about 2.5 times better off.

Whether viewed in terms of large or small improvements, progress matters a lot.

Looking backwards, it’s striking how unevenly distributed progress has been in the past. In antiquity, the ancient Greeks were discoverers of everything from the arch bridge to the spherical earth. By 1100, the successful pursuit of new knowledge was probably most concentrated in parts of China and the Middle East. Along the cultural dimension, the artists of Renaissance Florence enriched the heritage of all humankind, and in the process created the masterworks that are still the lifeblood of the local economy. The late 18th and early 19th century saw a burst of progress in Northern England, with the beginning of the Industrial Revolution. In each case, the discoveries that came to elevate standards of living for everyone arose in comparatively tiny geographic pockets of innovative effort. Present-day instances include places like Silicon Valley in software and Switzerland’s Basel region in life sciences.

These kinds of examples show that there can be ecosystems that are better at generating progress than others, perhaps by orders of magnitude. But what do they have in common? Just how productive can a cultural ecosystem be? Why did Silicon Valley happen in California rather than Japan or Boston? Why was early-20th-century science in Germany and Central Europe so strong? Can we deliberately engineer the conditions most hospitable to this kind of advancement or effectively tweak the systems that surround us today?

This is exactly what Progress Studies would investigate. It would consider the problem as broadly as possible. It would study the successful people, organizations, institutions, policies, and cultures that have arisen to date, and it would attempt to concoct policies and prescriptions that would help improve our ability to generate useful progress in the future.

Read: Is ‘progress’ good for humanity?

Along these lines, the world would benefit from an organized effort to understand how we should identify and train brilliant young people, how the most effective small groups exchange and share ideas, which incentives should exist for all sorts of participants in innovative ecosystems (including scientists, entrepreneurs, managers, and engineers), how much different organizations differ in productivity (and the drivers of those differences), how scientists should be selected and funded, and many other related issues besides.

Plenty of existing scholarship touches on these topics, but it takes place in a highly fragmented fashion and fails to directly confront some of the most important practical questions.

Imagine you want to know how to most effectively select and train the most talented students. While this is an important challenge facing educators, policy makers, and philanthropists, knowledge about how best to do so is dispersed across a very long list of different fields. Psychometrics literature investigates which tests predict success. Sociologists consider how networks are used to find talent. Anthropologists investigate how talent depends on circumstances, and a historiometric literature studies clusters of artistic creativity. There’s a lively debate about when and whether “10,000 hours of practice” are required for truly excellent performance. The education literature studies talent-search programs such as the Center for Talented Youth. Personality psychologists investigate the extent to which openness or conscientiousness affect earnings. More recently, there’s work in sportometrics, looking at which numerical variables predict athletic success. In economics, Raj Chetty and his co-authors have examined the backgrounds and communities liable to best encourage innovators. Thinkers in these disciplines don’t necessarily attend the same conferences, publish in the same journals, or work together to solve shared problems.

When we consider other major determinants of progress, we see insufficient engagement with the central questions. For example, there’s a growing body of evidence suggesting that management practices determine a great deal of the difference in performance between organizations. One recent study found that a particular intervention—teaching better management practices to firms in Italy—improved productivity by 49 percent over 15 years when compared with peer firms that didn’t receive the training. How widely does this apply, and can it be repeated? Economists have been learning that firm productivity commonly varies within a given sector by a factor of two or three , which implies that a priority in management science and organizational psychology should be understanding the drivers of these differences. In a related vein, we’re coming to appreciate more and more that organizations with higher levels of trust can delegate authority more effectively, thereby boosting their responsiveness and ability to handle problems. Organizations as varied as Y Combinator, MIT’s Radiation Lab, and ARPA have astonishing track records in catalyzing progress far beyond their confines. While research exists on all of these fronts, we’re underinvesting considerably. These examples collectively indicate that one of our highest priorities should be figuring out interventions that increase the efficacy, productivity, and innovative capacity of human organizations.

Similarly, while science generates much of our prosperity, scientists and researchers themselves do not sufficiently obsess over how it should be organized. In a recent paper, Pierre Azoulay and co-authors concluded that Howard Hughes Medical Institute’s long-term grants to high-potential scientists made those scientists 96 percent more likely to produce breakthrough work. If this finding is borne out, it suggests that present funding mechanisms are likely to be far from optimal, in part because they do not focus enough on research autonomy and risk taking.

Read: Small teams of scientists have fresher ideas

More broadly, demographics and institutional momentum have caused enormous but invisible changes in the way we support science. For example, the National Institutes of Health (the largest science-funding body in the U.S.) reports that, in 1980, it gave 12 times more funding to early-career scientists (under 40) than it did to later-career scientists (over 50). Today, that has flipped: Five times more money now goes to scientists of age 50 or older. Is this skew toward funding older scientists an improvement? If not, how should science funding be allocated? We might also wonder: Do prizes matter? Or fellowships, or sabbaticals? Should other countries organize their scientific bodies along the lines of those in the U.S. or pursue deliberate variation? Despite the importance of the issues, critical evaluation of how science is practiced and funded is in short supply, for perhaps unsurprising reasons. Doing so would be an important part of Progress Studies.

Progress Studies has antecedents, both within fields and institutions. The economics of innovation is a critical topic and should assume a much larger place within economics. The Center for Science and the Imagination at Arizona State University seeks to encourage optimistic thinking about the future through fiction and narrative: It observes, almost certainly correctly, that imagination and ambition themselves play a large role. Graham Allison and Niall Ferguson have called for an “ applied history ” movement, to better draw lessons from history and apply them to real-world problems, including through the advising of political leaders. Ideas and institutions like these could be more effective if part of an explicit, broader movement.

In a world with Progress Studies, academic departments and degree programs would not necessarily have to be reorganized. That’s probably going to be costly and time-consuming. Instead, a new focus on progress would be more comparable to a school of thought that would prompt a decentralized shift in priorities among academics, philanthropists, and funding agencies. Over time, we’d like to see communities, journals, and conferences devoted to these questions.

Such shifts have occurred before. A lot of excellent climate-science research—in environmental science, physics, chemistry, oceanography, mathematics and modeling, computer science, biology, ecology, and other fields—was being pursued before we recognized “climate science” as a discipline unto itself. Similarly, the designation of “Keynesian economics” helped economists focus on fiscal policy as a tool for recession fighting, just as the name “monetarism” created a focal interest in questions surrounding the money supply.

An important distinction between our proposed Progress Studies and a lot of existing scholarship is that mere comprehension is not the goal. When anthropologists look at scientists, they’re trying to understand the species. But when viewed through the lens of Progress Studies, the implicit question is how scientists (or funders or evaluators of scientists) should be acting. The success of Progress Studies will come from its ability to identify effective progress-increasing interventions and the extent to which they are adopted by universities, funding agencies, philanthropists, entrepreneurs, policy makers, and other institutions. In that sense, Progress Studies is closer to medicine than biology: The goal is to treat , not merely to understand.

We know that, to some readers, the word progress may sound too normative. However, it is the explicit bedrock upon which Vannevar Bush made his case for postwar funding of science, a case that led to the establishment of the National Science Foundation. In an era where funding for good projects can be hard to come by, or is even endangered, we must affirmatively make the case for the study of how to improve human well-being. This possibility is a fundamental reason why the American public is interested in supporting the pursuit of knowledge, and rightly so.

If we look to history, the organization of intellectual fields, as generally recognized realms of effort and funding, has mattered a great deal. Areas of study have expanded greatly since the early European universities were formed to advance theological thinking. Organized study of philosophy and the natural sciences later spawned deeper examination of—to name a few—mathematics, physics, chemistry, biology, and economics. Each discipline, in turn with its subfields, has spawned many subsequent transformative discoveries. Our point, quite simply, is that this process has yet to reach a natural end, and that a more focused, explicit study of progress itself should be one of the next steps.

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• > Journals
• > Philosophy of Science
• > Volume 91 Issue 3
• > Justifying Scientific Progress

Article contents

• Introduction
• Scientific progress as change-in-justification
• Change in what?
• Truth as convenient benediction
• No view from no-who

Justifying Scientific Progress

Published online by Cambridge University Press:  15 September 2023

I defend a novel account of scientific progress centered around justification. Science progresses, on this account, where there is a change in justification. I consider three options for explicating this notion of change-in-justification. This account of scientific progress dispels with a condition for scientific progress that requires accumulation of truth or truth-likeness, and it emphasizes the social nature of scientific justification.

1. Introduction

I was surprised to learn that the philosophical literature on scientific progress has neglected a compelling contender. The contender that I consider here holds that science progresses when there is a change in scientific justification. Justification is central to scientific practice and a pillar of knowledge—hence my surprise.

Understanding scientific progress became important after Kuhn. Kuhn’s work was seen as a threat to the rationality of science. If science undergoes revolutions, and if a scientific paradigm after a revolution cannot be compared on its epistemic merits to the scientific paradigm prior to the revolution, then, some followers of Kuhn thought, it is hard to see how science makes progress across scientific revolutions. Scientific revolutions as depicted by Kuhn motivated relativism, skepticism, antirealism, and the 1990s science wars.

Though most academics have worked off the hangover from post-Kuhnian extravagance, today we observe widespread public distrust of science. A compelling account of scientific progress could help constrain further deterioration of trust in science, at least when such trust is warranted. An account of scientific progress that is too demanding entails that science makes little progress and thus plausibly should receive little trust. An account of scientific progress that is not demanding enough entails that too many unreliable practices count as scientifically progressive, and thus we would place our trust in unreliable practices.

Existing accounts of scientific progress are too demanding or not demanding enough. The reason why many accounts of scientific progress are too demanding, as I argue in section 4 , is that they have a truth requirement or something similar: they hold that for science to progress, it must accumulate more truths or more truth-like conclusions. Bird’s ( Reference Bird 2022 ) “epistemic account” of scientific progress, for example, holds that science progresses when it accumulates knowledge, and knowledge requires truth. Dellsén’s ( Reference Dellsén 2016 ) “noetic account” holds that science progresses when scientific understanding increases; by scientific understanding, Dellsén means the ability to make accurate explanations and predictions, and because accuracy is a factive notion, this account has a truth requirement. What Bird dubs “semantic accounts” are truth-centered: Rowbottom’s ( Reference Rowbottom 2008 ) account holds that science progresses when it accumulates true scientific beliefs, and Niiniluoto’s ( Reference Niiniluoto 2014 ) “verisimilitude account” holds that science progresses when it accumulates truths or its theories become more truth-like. The account of scientific progress that I explore here is less demanding, though as I will explain, it is just as demanding as reliable scientific work itself, which is demanding enough.

The reason why other accounts of scientific progress are not demanding enough is that they do not require justification and make scientists seem like tinkerers. Most philosophical discussions of scientific progress seem to assume that if an account of scientific progress dispels with truth, then it must be something like a problem-solving account. Kuhn ( Reference Kuhn 1963 ) and Laudan ( Reference Laudan 1977 ), and more recently, Shan ( Reference Shan 2019 ), hold that science makes progress as its ability to solve problems increases, and success at solving problems is judged by standards internal to a scientific discipline (or paradigm, or disciplinary matrix, or research tradition, etc.), rather than by a truth standard (for discussion of the main existing views of scientific progress, see Rowbottom Reference Rowbottom 2023 ). Yet all sorts of problems can be solved without the progress of science. Defenders of such accounts could say that the problems that are relevant when thinking about scientific progress are necessarily empirical or theoretical in nature, and thus their solution amounts to scientific progress, though if that were so, progress would occur because such solutions would be justificatory.

There is space for an account of scientific progress that sits between the overly demanding truth-centered accounts and the underdemanding problem-solving accounts. My aim in section 2 is to articulate and defend such an account of scientific progress. I work out some nuances of this account in section 3 . To address what is probably the most obvious question one might ask about this account, I argue in section 4 that scientific progress does not require the accumulation of truths or approximation to truths. In short, this is a novel and compelling account of scientific progress with justification at the center.

Scientific justification is special: it is communal and intersubjective. A complete theory of scientific progress requires that scientific findings have community uptake. Some existing accounts of scientific progress appear to neglect this, though these accounts may implicitly accept that scientific progress is a property of communities, and Bird ( Reference Bird 2022 ) explicitly (and convincingly) defends a social account of group belief in science, which could be applied to the notion of scientific progress to uphold a requirement of community uptake. I close the article in section 5 by defending a requirement of community uptake. The justification account of scientific progress is better than existing accounts, as it is consonant with science itself—this is an account of scientific progress faithful to the spirit of the scientific attitude and to the real achievements of science. This is scientific scientific progress.

2. Scientific progress as change-in-justification

Here is the justification account of scientific progress: science makes progress if and only if there is a change in justification. For now the formulation is incomplete because it is silent on what receives justification and what constitutes a change in justification—I address this in section 3 . Here I defend the general plausibility of a justification account of scientific progress. Can an account of scientific progress have justification as its centerpiece?

Pretty much every philosopher writing about scientific progress seems to think that the answer is no. Rowbottom ( Reference Rowbottom 2008 ), for example, suggests that justification is only instrumental for scientific progress. Dellsén ( Reference Dellsén 2023 ) agrees. Though Bird ( Reference Bird 2008 , 280) requires justification, he is explicit in his claim that nothing short of knowledge constitutes progress, and he specifically claims that justification, although necessary for knowledge and thus progress, is, without truth, insufficient for progress (see also Bird Reference Bird 2007 ). I address this question more thoroughly in section 4 , arguing that truth is not required for scientific progress. I note now only that much of science is epistemic in the truest sense of the word, namely, not about truth at all but about evidence and the evidence–hypothesis relation, trying to determine what hypotheses or theories are justified based on available evidence and to improve those justifications. Scientific practice justly just is about justification.

I address the sufficiency of justification (absent truth or knowledge) as constitutive of scientific progress in section 4 . Dellsén ( Reference Dellsén 2016 ) argues against the necessity of justification for progress. His example is Einstein’s 1905 explanation of Brownian motion. Because on Dellsén’s noetic account of scientific progress, the ability of a theory to explain phenomena is a marker of progress, Einstein’s explanation of Brownian motion counted as progress. Yet, this explanation was based on the kinetic theory of heat, which at the time was speculative and so, claims Dellsén, unjustified. And thus, concludes Dellsén, justification is not necessary for scientific progress. However, precisely because the kinetic theory of heat was able to explain Brownian motion, the kinetic theory of heat received some degree of justification, because in general, the ability of a theory to explain a phenomenon provides some justification to that theory. So this is an example of progress, but contrary to Dellsén’s claim, this example involved justification.

Another argument Dellsén ( Reference Dellsén 2023 ) gives that strikes me as compelling involves imagining a scientific discipline with a track record of consistently generating false theories. That dismal track record gives scientists in that discipline reasons to think that any current theories are probably false, akin to the pessimistic meta-induction. Now suppose that the discipline develops strong evidence for a theory. This seems like progress, but, claims Dellsén, those scientists would be unjustified in believing that the theory is true (because of the dismal track record in that discipline), and thus, concludes Dellsén, the justification requirement for progress is too demanding. However, note that it is the requirement that beliefs in the truth of the theory be justified that is too demanding. This case involves a change in justification for the theory precisely because the case involves the acquisition of confirmatory evidence for the theory. There can be an increase in justification for some hypothesis without there being sufficient grounds to believe that hypothesis. So if one has the intuition that Dellsén does about this case, namely, that it involves progress, the change-in-justification account of progress accommodates that.

Refereeing the debate between the view that scientific progress is the accumulation of knowledge and the view that scientific progress is the accumulation of true scientific beliefs, Mizrahi and Buckwalter ( Reference Mizrahi and Buckwalter 2014 ) tested the intuitions of a large number of subjects and found that justification is important for intuitive judgments about what constitutes scientific progress. This provides some support for holding justification as a necessary component in an account of scientific progress.

An accumulation of new evidence can increase justification, and that would amount to scientific progress. The justification account of scientific progress is more general than the noetic, epistemic, and semantic accounts because it allows for nontheoretical progress. Dellsén ( Reference Dellsén 2018 , 2), for example, claims that scientific progress is strictly about “improvement in our theories, hypotheses, or other representations of the world, rather than other improvements of or within science.” (So on Dellsén’s account, accumulation of more evidence would not count as progress, and nor would an increase in the confirmation of a hypothesis necessarily count as progress, because the mere increase in confirmation of a hypothesis does not need to involve an improvement of that hypothesis.) The noetic, epistemic, and semantic accounts of scientific progress are too theory-centric (for similar points, see Douglas Reference Douglas 2014 ; Shan Reference Shan 2019 ; Massimi Reference Massimi 2022 ).

Shan ( Reference Shan 2019 ) recently updated the problem-solving account of scientific progress. In this insightful update, the articulation of scientific problems is deemed just as progressive as the proposal of solutions to those problems. I agree that articulation of problems is important. However, without a change in justification, neither the articulation of a scientific problem nor a proposed solution to a scientific problem should be seen as constituting scientific progress. The mere articulation of a scientific problem is like posing a rhetorical question without answering it. Having an articulated scientific problem can be important for the development of some research programs, though it is not necessary. Lucky discoveries can occur without articulated problems, as, for example, occurred with Fleming’s discovery of penicillin. That said, it is plausible to think that having articulated problems can contribute to scientific progress. (Bird and others rightly argue that contributing to progress does not necessarily constitute progress—just as a large grant for research may contribute to scientific progress but not constitute it.)

Justification according to what standard? One can perhaps simply adopt any favored account of justification on offer from epistemology. Yet I believe that two options are unattractive. One standard of justification could be strictly internalist by holding that beliefs are justified by the evidence immediately available to an individual scientist. This would be unsatisfying as an account of scientific progress, as it would render determinations of progress highly individualistic and idiosyncratic. Another standard could be that of an ideal epistemic community at the end of inquiry. That option would render justification epistemically inaccessible to virtually all practicing scientists, thereby sapping it of any practical, methodological bite for practicing scientists and of one of its advantages relative to a truth requirement for progress (section 4 ). An account of justification that sits somewhere between these two options is better. This could appeal to whatever principles and practices a scientific community establishes that serve to minimize epistemic risk, thereby enhancing the objectivity and reliability of its findings (Koskinen Reference Koskinen 2020 ).

3. Change in what?

What changes in a change of justification? I describe three options. The first, based on the number of justified beliefs, is my least favorite. The second, based on a notion of graded justification, and the third, based on a notion of change in confirmation, are, to my mind, both plausible, and they are perhaps interchangeable. Because the formal apparatus of the third option is well developed, it allows me to explore a range of nuances, and thus my treatment of the third option is more extensive than the first two.

3.1 Number of justified beliefs

One option to explicate change-in-justification, taking its cue from other recent accounts of scientific progress, would be to understand a change in justification in terms of a change in the number of justified beliefs that science accumulates. Just as Bird ( Reference Bird 2022 ) argues that scientific progress is the accumulation of knowledge (which entails the accumulation of justified true beliefs), and just as Rowbottom ( Reference Rowbottom 2008 ) argues that scientific progress is the accumulation of true scientific beliefs, one could hold that a justification-centered account of scientific progress would be based on a change in the number of justified scientific beliefs.

Yet, I believe any account of scientific progress based on counting beliefs is implausible. Here is a problem for explicating scientific progress based on the number of beliefs (whether justified or true or both): in any scenario in which there is a justified true belief in x , and then more scientific work is performed that further establishes the plausibility of x , a belief-counting approach entails that no progress has been made, because there was no increase (or decrease) in the number of beliefs that are justified or true or both (because x was already justified prior to the additional scientific work and x is, by assumption, here true). A plausible example of this is the detection of the Higgs boson in 2012. Prior to the Large Hadron Collider experiments, the Standard Model of particle physics had a huge amount of empirical and theoretical support. On both a coarse-grained hypothesis like “the Standard Model is true” and a fine-grained hypothesis like “the Higgs boson exists,” belief in these hypotheses before 2012 was justified. Yet the Large Hadron Collider experiments that detected the Higgs boson surely must count as scientific progress.

Like the other recent accounts of scientific progress mentioned earlier, a counting-beliefs approach would adopt an ungraded view of beliefs, about which we should be suspicious. In general, there are good reasons not to hold an ungraded account of belief (such as the lottery paradox). A graded view of doxastic states is also more consistent with scientific practice, insofar as science cultivates a fallible attitude toward its products. A long list of luminaries, including Merton ( Reference Merton 1942 ), Popper ( Reference Popper 1963 ), and Longino ( Reference Longino 1990 ), have emphasized the importance of organized skepticism about and criticism of scientific work and its results. One need not adopt Popper’s aversion to confirmation to accept the importance of this critical attitude for science.

Finally, as Dang and Bright ( Reference Dang and Bright 2021 ) have recently argued, scientists need not believe claims that they assert as scientifically justified, particularly when many scientists work collaboratively on a project. (Lackey [ Reference Lackey 2007 ] has made a more general argument that belief is not a norm of assertion.) Science is not an institution that simply gathers a set of claims that scientists sign up for either believing or disbelieving. In general, I believe it is a mistake for philosophers of science to follow epistemologists by developing a belief-centric epistemology of science; rather, scientific epistemology should be centered around confirmation of scientific hypotheses or theories, which can be based on credences of individuals but can involve much more.

3.2 Change in degree of justification

Another option to explicate change-in-justification, taking its cue from recent work in epistemology, would be to understand a change in justification as a change in the degree of justification for a belief or scientific claim. Some thinkers have argued that justification is a gradable property (see, e.g., Gerken Reference Gerken 2022 ), which seems plausible.

A change in degree of justification can occur when a scientist generates new evidence, or when a new hypothesis is introduced, or when scientists improve the reliability of methods. A clear instance of a change in degree of justification occurs when newly acquired evidence increases the confirmation of an existing hypothesis (I discuss this change-in-confirmation approach to justification in the following section). When this happens, science makes progress. Such progress might be modest, or it might be dramatic, as occurred with Eddington’s observation of the bending of light.

The degree of justification of a hypothesis or theory can be influenced by the so-called theoretical virtues, such as simplicity, scope, or accuracy, or other nonempirical features. For example, Dawid, Hartmann, and Sprenger ( Reference Dawid, Hartmann and Sprenger 2015 ) argue that the “no-alternatives argument” can provide some justification for theories (however, for a discussion of a problem with appealing to theoretical virtues as a basis for theory choice, see Okasha Reference Okasha 2011 ; Stegenga Reference Stegenga 2015 ).

The example of the detection of the Higgs boson, which was a problem for the belief-counting approach to change-in-justification, is a little less of a problem for this account. The 2012 detection of the Higgs boson was, of course, evidence for the existence of the Higgs boson, and evidence for the Standard Model. Yet before 2012, the existence of the Higgs boson and belief in the Standard Model were well justified. And because those claims were already well justified, the work at the Large Hadron Collider that led to the detection could add little justification, which might strike some as counterintuitive.

One potential concern for this approach is that we do not have a very well developed account of graded epistemic justification (for a recent argument that the landscape of graded justification is muddy, see Hawthorne and Logins Reference Hawthorne and Logins 2021 ). Nevertheless, it is intuitive that epistemic justification is indeed graded, and one plausible way to articulate such a notion is by using the tools of confirmation theory.

3.3 Change in confirmation

One way to explicate change-in-justification, taking its cue from recent work in formal epistemology, would be to understand a change in justification in terms of a change in confirmation.

The best-developed account of scientific confirmation is based on the tools of probability. Sprenger and Hartmann ( Reference Sprenger and Hartmann 2019 ) lay out the basics of Bayesian confirmation theory and then apply it to several topics in philosophy of science, including the tacking paradox, the grue paradox, and the paradox of the ravens. I extend this approach to give an account of scientific progress in which confirmation is central.

An obvious case of progress is when new evidence is generated that adds confirmation to a hypothesis. Yet both increases and decreases in confirmation can constitute scientific progress. A decrease in confirmation can occur in an instance of failed replication: scientists might have a relatively high degree of confirmation for some hypothesis because an initial experiment provided evidence supporting the hypothesis, yet if a subsequent experiment attempting to replicate that initial experiment provides evidence disconfirming that hypothesis, this can count as progress. Indeed, replication failures have become especially important recently due to the so-called replication crisis in psychology. For example, Baumeister et al. ( Reference Baumeister, Bratslavsky, Muraven and Tice 1998 ) published evidence supporting the existence of “ego depletion,” the putative phenomenon in which subjects’ self-control is a limited resource that can be used up; later, larger experiments did not observe ego depletion (e.g., Vohs et al. Reference Vohs, Schmeichel, Lohmann, Gronau, Finley, Ainsworth and Alquist 2021 ), and such a replication failure should count as scientific progress.

When a scientist introduces a new hypothesis that can explain existing evidence better than already available hypotheses, that new hypothesis can undergo a huge increase in confirmation (from zero or undefined to substantial) while the already existing hypotheses undergo a decrease in confirmation (Lipton Reference Lipton 2004 ). That is progress. A nice example of this was provided by Einstein, whose 1915 general theory of relativity was able to explain the precession of the perihelion of Mercury’s orbit, a by-then well-established empirical phenomenon that could not be explained by existing physical theory.

A distinction that goes back to Carnap is between absolute confirmation, the degree to which some hypothesis H is supported by evidence E, and incremental confirmation, the extent to which the support of H changes upon getting E. Absolute confirmation is represented by Bayesians as the posterior probability, or the probability of H given E, P(H|E). There are various measures of incremental confirmation, each with distinct formal representations. Two prominent measures include the difference measure, which is the difference between the posterior probability and the prior probability, P(H|E) − P(H), and the likelihood ratio measure, which is the ratio between the likelihood of E given H divided by the likelihood of E given a contrast hypothesis H′, P(E|H)/P(E|H′) (see Fitelson Reference Fitelson 1999 ). Because progress implies change, one might think that the approach here is based on incremental confirmation, though a justified change in confirmation implies a change in absolute confirmation, so one can make sense of this account of scientific progress according to either absolute or incremental confirmation. As is standard, C(H,E) represents the incremental confirmation that E provides to H without specifying a particular confirmation measure, and C i (H,E) represents the incremental confirmation that E provides to H by confirmation measure i .

For Bayesian accounts of confirmation like that of Sprenger and Hartmann ( Reference Sprenger and Hartmann 2019 ), probabilities are representations of an agent’s credence. To address the worry that such a subjective foundation cannot be the basis for characterizing central features of science, Sprenger and Hartmann respond by claiming that the agents they are modeling are ideal, rational, and responsive to evidence. Change in confirmation is represented using the formal measures noted earlier, and the probabilities represent credences of a rational scientist who responds appropriately to evidence such that their resulting credences are justified by their evidence.

Whether there is a unique way to appropriately respond to evidence is a controversial question in epistemology—for what it is worth, I do not believe that there is, yet arguing that point would take me astray (for differing views on the so-called uniqueness thesis, see White Reference White 2005 ; Kelly Reference Kelly, Steup, Turri and Sosa 2014 ). If there is a unique way to appropriately respond to evidence, then the formal measures of incremental confirmation are simply representations of that uniquely justified way to respond to evidence. If there is not a uniquely justified way to respond to evidence, then anyway, there are plausible constraints on justified responses to evidence.

Consider this example. Maria and Sasha want to evaluate hypothesis H, which says that “this drug does blah blah.” Both Maria and Sasha have the same prior, P(H). A randomized trial is performed that gives evidence (E) suggesting that the drug does blah blah. If uniqueness is true—if there is a unique way to appropriately respond to evidence—then when given E, both Maria and Sasha will assign the same degree of confirmation to H. If uniqueness is false, then their assessment of the confirmation provided to H could differ. Perhaps Maria thinks that randomized trials are not as epistemically important as they are often made out to be (having read Worrall Reference Worrall 2002 ), whereas Sasha thinks that randomized trials are more reliable than the alternative (having read Larroulet Philippi Reference Larroulet Philippi 2022 ). Yet for both Maria and Sasha, their posterior, P(H|E), must be greater than their prior, P(H), because E offers at least some confirmation of H (assuming the plausible “positive relevance” definition of evidence). Thus both Maria and Sasha conclude that H receives some incremental confirmation: for both Maria and Sasha, C(H,E) > 0. Thus, for both Maria and Sasha, according to the confirmation account of scientific progress, this episode involves scientific progress. (When given some other evidence E′, Maria and Sasha might disagree about which of E or E′ provides more confirmation to H and thus about which evidence contributes more scientific progress, but such disagreements are faithful to real scientific disputes.)

One challenge to this approach is that if both increases and decreases in confirmation can count as progress, then there can be a hypothesis that receives first an increase in confirmation and then a decrease of the same amount, and then an increase, and then a decrease, and so on, and that does not really look like progress. Consider confirmation of H by a sequence of experiments that generates E 1 − E N accordingly, and we measure confirmation with the difference measure C d . We can have

and so on to N . At the end of this sequence, the posterior probability of the hypothesis would be the same as its prior was before the sequence of experiments began. It might seem unintuitive to count this as scientific progress. Yet it is consistent with the confirmation account of scientific progress.

If an episode in science went through a small number of such iterations, then I would have no problem calling that scientific progress. There are real cases that involve the plausibility of a hypothesis waxing and waning and then waxing again. For example, Margaret Mead ( Reference Mead 1928 ) shocked the world with her description of sexually permissive teenagers in Samoa; Derek Freeman ( Reference Freeman 1983 ) then argued that Mead’s evidence was unreliable, and thus the teenage sexual permissiveness hypothesis was disconfirmed; subsequently, Paul Shankman ( Reference Shankman 2009 ) argued that Freeman had exaggerated his criticisms of Mead, and thus the teenage sexual permissiveness hypothesis was plausible, which is roughly where things now stand.

However, I doubt that many real cases in science involve more than a handful of such iterations—I cannot think of any, though if that is just a result of my ignorance, then I await edification.

Another possible challenge to this approach would be any instance in which there is scientific progress with no change in confirmation. Yet I cannot think of any examples, and I am tempted to think that this is because of the analytic relationship between scientific progress and change in confirmation. Consider first an example in which one has a prior of zero for some hypothesis and acquires evidence about that hypothesis, none of which is confirmatory; there has been an accumulation of evidence but no change in confirmation. Is it progress? I do not think so. That is because hypotheses for which we have zero priors are like “Santa Claus exists” or “my body is composed of fewer than seven atoms.” Gathering evidence that provides no confirmation for such hypotheses is not scientific progress, and mutatis mutandis for priors of one (acquiring evidence that confirms the hypothesis “Santa Claus does not exist” is not scientifically progressive). If a posterior differs from the prior, and so there is a change in confirmation, there must be some newly developed confirmatory or disconfirmatory element, such as acquisition of evidence for the hypothesis or a refinement of the hypothesis, and such developments are progressive for science.

Evidence can, obviously, provide confirmation or disconfirmation to a hypothesis. One might be tempted to ask what the nature of evidence itself is. Addressing this question in any detail here would take this article astray. It is enough simply to say that evidence is that which provides confirmation or disconfirmation to hypotheses. The austere positive relevance definition of evidence holds that some evidence E is confirming evidence for a hypothesis H if and only if P(H|E) > P(H). For the present purpose of explicating scientific progress, that should be enough to say about evidence.

Nevertheless, consider Williamson’s ( Reference Williamson 2000 ) E = K thesis, which holds that one’s evidence is constituted by what one knows. On that account, gathering new evidence amounts to an accumulation of new knowledge, and if this is so, then one might think that the account of scientific progress based on a change in confirmation or justification by new evidence is, after all, a knowledge-based account; Bird ( Reference Bird 2022 ), for example, pursues such an approach to scientific progress. Yet we have already seen ways in which a change in justification can occur without gathering new evidence, such as by introducing new explanatory theories or refining existing theories, solidifying background assumptions, and appealing to theoretical features like simplicity or other nonevidential considerations, such as the no-alternatives argument. So even on such an account of evidence, a justification account of scientific progress does not reduce to a knowledge account.

I have defined scientific progress as a change in justification or confirmation. One might think that progress should be defined in terms of increase rather than mere change. That, however, would face the challenge mentioned earlier of explaining how a failed replication could count as progressive. More substantively, I believe that an account of scientific progress in terms of change in confirmation and an account in terms of increase in confirmation are equivalent. Suppose we are considering some hypothesis X and we get evidence E that provides some incremental dis confirmation to X; we can conceive an alternative hypothesis, Y, that says “not X,” and Y, then, gets an increase in confirmation due to E. There is a change in confirmation (specifically, a decrease in confirmation) of X but an increase in confirmation of Y. So increase-in-confirmation and change-in-confirmation are formally interchangeable as an account of scientific progress.

Some increments in confirmation may be minuscule. And sequences of increments in confirmation can involve diminishing returns. Suppose I want to know if a coin is biased to heads. I toss the coin. Heads. I toss again. Heads. I toss again. Tails. Ten tosses, eight heads. One hundred tosses, seventy-seven heads. One thousand tosses, 789 heads. So I am now thinking that this coin is biased roughly to 0.8 heads. The hypothesis “this coin is biased to 0.8 heads” received a lot of confirmation in the first ten tosses, but the amount of incremental confirmation received by the ten tosses between the 700th toss and the 710th toss is much, much less. I raise this point here because it will make sense of an important example in section 4 .

3.4 Summary

I have considered three options for explicating the notion of a change in justification. There may be other viable options, though these three seem like plausible contenders.

Because the justification account of scientific progress dispels with the truth requirement, it might be seen as a close cousin of the problem-solving account of scientific progress most thoroughly developed by Laudan ( Reference Laudan 1977 ), as that account also dispels with the truth requirement. However, Laudan was positively allergic to thinking about scientific progress in terms of justification or confirmation. Whether a theory is “well or poorly confirmed,” claimed Laudan, is irrelevant to assessing progress (22–23). All that matters on his account is if a theory can solve a problem. Problems, according to Laudan, can be empirical phenomena, and a solution can involve a theory providing an explanation of those phenomena, regardless of its confirmation (see Laudan Reference Laudan 1977 , 25). However, precisely because a theory receives some confirmation when it can explain an empirical phenomenon, Laudan perhaps should not have had such an allergy to a confirmation account of scientific progress. Yet, many instances of changes in confirmation are important and constitute progress but do not contribute to the solution of a problem. The problem-solving account is incomplete, and that is vivid when compared to the justification account.

So there is a lot to like about the justification account of scientific progress. It makes sense of so much scientific work, routine scientific work, such as generating new evidence—science progresses with the accumulation of new evidence, not just with the refinement of existing theory or the introduction of new theory, and so the justification account is more general and, I think, more intuitive than theory-centered accounts of scientific progress. It makes sense of the great value of introducing a new hypothesis that explains existing evidence. It makes sense of the importance of experiments aimed at replicating existing findings and the interest generated when such attempts fail. It emphasizes the importance of justification. It is given foundational legs by our best general philosophical theory of scientific confirmation and the epistemology of reasoning. It entails that scientific progress is epistemically accessible to scientists. What might be seen as its main shortcoming, namely, its lack of reference to truth, is in fact one of its merits, as I now argue.

4. Truth as convenient benediction

The justification account of scientific progress dispels with the necessity of accumulation of truths or related factive notions for scientific progress. Yet, a widespread belief is that the aim of science is truth or a related notion, such as knowledge. If the aim of science is truth or knowledge, then it is a natural thought that science makes progress as it accumulates truths or knowledge. We saw earlier that several prominent accounts of scientific progress have a truth requirement. My aim in this section is to offer three arguments against a truth requirement for scientific progress.

Ascriptions of scientific hypotheses as true are not typically part of routine scientific practice; rather, ascriptions of truth are typically retrospective benedictions. Such benedictions are convenient, as they provide a simple summary of the messy details of scientific work, for allocating credit, teaching students, distributing research funds, and communicating to the public. Truth is convenient benediction. This is not to say that truth is not important, or is not disquotational, or does not correspond; I believe that truth in general has those properties. My point is more modest—to call truth in science a benediction is to emphasize that ascertainment of truth can take a long time and, obviously and typically, occurs in retrospect.

In real time, scientists are able to ascertain justified changes in confirmation. In real time, scientists are not able to ascertain the achievement of truth. Benedictions of truth take time (Massimi Reference Massimi 2016 ). When Watson and Crick finished building their model of the double helix structure of DNA, they were confident enough of their achievement to walk across the street to the Eagle pub in Cambridge to celebrate. They had a clear-eyed assessment of how well confirmed their model was. Yet their one-page 1953 paper in Nature was shot through with caution; they claimed that their model was a postulate, based on numerous assumptions, and that alternatives to their model, though unlikely, were possible. They were not giving their own finding a benediction. That benediction came nine years later, when they were awarded the Nobel Prize. So in some episodes of scientific progress, benedictions can be made soon after the scientific work itself. However, in other episodes of scientific progress, benedictions can take a very long time. For example, it took generations of scientists to properly establish Copernican theory (Westman Reference Westman 2011 ).

Laudan ( Reference Laudan 1977 ) argued that real-time epistemic accessibility of scientific progress is a desideratum for an account of scientific progress—a scientist or a scientific community should be able to ascertain that by doing x , they are making progress. Just as a mountaineer should be able to determine if they are getting nearer to the summit, and just as a baker should be able to determine if the bread is rising, I find this epistemic accessibility requirement for scientific progress plausible. Laudan famously argued for antirealism (based on the pessimistic meta-induction); if antirealism is true, and if one held a truth requirement for scientific progress, then it would follow that science cannot make progress—Laudan took that as an argument against accounts of progress that have a truth requirement. Bird ( Reference Bird 2022 ) and others push back against Laudan by directly targeting the argument for antirealism. Yet one can adopt the epistemic accessibility desideratum without adopting antirealism. Here is the general point: the fact that it can take a long time after scientific work occurs for the truth of the findings of that work to receive benediction means that any account of scientific progress that maintains a truth requirement must violate the epistemic accessibility desideratum. (It also follows that truth cannot be a “norm of assertion” for science, contrary to Price’s [ Reference Price 2003 ] claim that truth is a norm for all assertoric discourse and to Bird’s [ Reference Bird 2022 ] claim that knowledge is a norm of correctness for science.)

Similarly, it can take a very long time to learn that one’s theories are false and not even approaching the truth. This raises the next problem for maintaining a truth requirement for scientific progress, what I call the Ptolemaic challenge . Ptolemaic astronomers toiled for centuries to tally the planets and stars and their positions over time. They developed an Earth-centered model of the solar system based on the geometry of epicycles (a smaller circle placed on the circumference of a larger circle). Their epicyclic models were very successful at explaining their observations, and when they observed anomalous celestial phenomena, they refined their models by adding more epicycles (For a detailed discussion of Ptolemaic astronomy, I recommend Kuhn’s 1957 book The Copernican Revolution ). It was a research program that lasted for many centuries, based on rigorous observations that were accounted for by mathematical theorizing that became more and more sophisticated. Yet all those models were false, and they were not, over all those centuries, getting any closer to the truth, as they were all models of the solar system placing Earth at the center. To maintain a truth requirement for scientific progress requires one to hold that Ptolemaic astronomy made no progress. Not a drop .

I find it odd to think that Ptolemaic astronomy made no progress. More than odd. Such a view is offensive to those ancient late-night observers of the starry sky, those scientists of the oldest science, those curious heirs to Babylon and those diligent students of Aristotle, those scientists who spent centuries in the cold, dark nights of northern Africa to record the movements of stars and planets on clay tablets and who devised intricate theories based on models of epicycles on epicycles, those scientists whose forebears designed the pyramids of Egypt to align with the stars and who calculated Earth’s circumference to nearly its true value, those scientists who could at least offer a putative explanation of the westward motion of the sky and the eastward motion of the moon relative to the stars and the retrograde motion of planets by layering epicycles on epicycles, and who could predict astronomical observations to within the limits of what could be observed with the naked eye one thousand years into the future using epicycles on epicycles—epistemic feats surely more impressive than that which could be achieved today by most lovers of science.

Here we have the Ptolemaic challenge. If an account of scientific progress maintains a truth requirement, it must say that Ptolemaic astronomy made no progress. But Ptolemaic astronomy did make progress. The semantic, epistemic, and noetic accounts of scientific progress face the Ptolemaic challenge. For that reason, they are too demanding.

The Ptolemaic challenge also applies to the verisimilitude version of an epistemic account of scientific progress, as there is no sense in which subsequent iterations of Earth-centered models of the solar system were more truth-like. Niiniluoto ( Reference Niiniluoto 2014 ) distinguishes between real progress and estimated progress, where real progress is based on increasing verisimilitude and estimated progress is based on merely an apparent increase in verisimilitude. He would say that Ptolemaic astronomers merely seemed like they were making progress but that they were not in fact making progress. This, too, faces the Ptolemaic challenge.

One might think that after centuries of adding epicycles on epicycles, Ptolemaic astronomy was no longer making progress. Indeed, Lakatos’s ( Reference Lakatos 1978 ) attempt to articulate a demarcation criterion for scientific research programs had precisely this sort of concern in mind. Lakatos held that a research program, by which he meant the development and testing of a sequence of theories, is progressive if the sequence of theories makes more and more predictions and if more and more of those predictions turn out to be true; and if a research program is not progressive, then, Lakatos claimed, it is “degenerating.” On this account, later Ptolemaic astronomy was not progressive; it was degenerating. Yet, Lakatos’s demarcation criterion placed too much emphasis on novel predictions; philosophers of science have pretty much reached a consensus that while predicted evidence can be more confirming than merely accommodated evidence, that is not always the case, and accommodated evidence can provide some confirmation to a theory (though arguing this point would take me astray; see, e.g., Barnes Reference Barnes 2008 ; Frisch Reference Frisch 2015 ). Moreover, a research program can make little or no progress but need not be “degenerating.” Ptolemaic astronomy in the late Middle Ages was plausibly in a phase of “diminishing justificatory returns,” as mentioned in section 3 —though some incremental confirmation could be gained by adding a 37th epicycle, it was very little.

Laudan ( Reference Laudan 1984 ) describes truth as a utopian aim for science, because, impressed by the pessimistic meta-induction, he claims that we can never achieve the aim, and even if we could, we could not know if the aim had been achieved. Bird ( Reference Bird 2022 ) rightly complains that this is an excessively skeptical position. With Bird, I agree that we can often come to know that science has achieved truth (though, as earlier, in science, that can take a long time). Yet sometimes we cannot know that we have achieved truth, or are approaching truth, and importantly, sometimes we cannot know that what we now take to be true is in fact false. That was the plight of the Ptolemaic astronomers for many centuries. Here my position is somewhere between Bird and Laudan. Truth is not a utopian norm, rather, it is a nirvana norm . With great diligence, some people may be able to achieve nirvana, just as with great diligence, science can achieve truth. Yet one might be approaching nirvana and not know it, and conversely, one might not be approaching nirvana but think otherwise.

Moreover, nirvana norms are not action guiding and can be used for assessment only retrospectively. Traffic laws guide action—they influence behavior in real time—and of course a police officer can appeal to a traffic law as the basis for pulling you over for speeding. A norm that says “seek truth” tells scientists little about how to behave, just as a norm that says “seek nirvana” tells me little about how to behave. Such abstract norms need supplementary, concrete, action-guiding norms. In Buddhism, action-guiding norms are articulated in the Noble Eightfold Path. Each path is constituted by concrete, action-guiding norms; the “right speech” path, for example, says: no lying, no rude speech, no idle chitchat; the “right livelihood” path says: do not earn money by selling weapons, living beings, meat, or alcohol. Telling a person to seek nirvana is to tell them nothing—it is a nirvana norm. Telling a person to follow the Noble Eightfold Path is to give them very concrete guidance on action. The equivalent concrete norms for science would be whatever principles and practices one has good reason to think minimize epistemic risks and thus are reliabilityenhancing and ground claims to justification (Koskinen Reference Koskinen 2020 ).

I have given three arguments against maintaining a truth requirement for an account of scientific progress: the epistemic accessibility argument (truth as benediction), the Ptolemaic challenge, and the truth-is-a-nirvana-norm argument. To repeat, I am not suggesting that truth is unimportant or that science cannot attain truth. I am arguing only that scientific progress is to be judged by reference to changes in justification rather than achievement of truths or approximations to truth (which is, thus, a version of pragmatism, insofar as some pragmatists dispense with a truth norm but emphasize justification; see Rorty Reference Rorty 1998 ). Science can come to discover truths about the world precisely by engaging in its justificatory practices, perhaps by adopting what Nagel ( Reference Nagel 1986 ) called a “view from nowhere.” However, science cannot adopt a view from no-who.

5. No view from no-who

Suppose Sasha is searching for the holy grail of science, the ultimate theory of everything, and after years of work, she makes a breakthrough discovery, a theory that unifies all physical laws and explains all existing anomalies. She writes up her finding. But she worries about her discovery being used to develop terrible weapons. She burns her manuscript, moves to Nepal and joins a Buddhist monastery, never speaks with anyone about her discovery, and lives out her final years in quiet solitude.

Sasha accumulated knowledge, a true finding that could solve many problems and that was, on traditional personalist grounds, justified. Some existing accounts of scientific progress would appear to maintain that Sasha made scientific progress. Yet she did not. I noted previously that scientific justification is communal and intersubjective. For a scientific achievement to contribute to scientific progress, there must be not only an in-principle possibility of community uptake but also some actual community uptake. Such uptake can take time, as occurred with the Copernican model of the solar system, but eventually, such uptake must occur. A finding that is observed by no one other than the scientist responsible for the finding can hardly be deemed scientific, let alone a contribution to scientific progress. Science cannot make progress with a view from no-who.

The cognitive achievements central to each of the accounts of scientific progress are nothing without community uptake. The existing literature on scientific progress has focused on these cognitive achievements, asking which kind of cognitive achievement is the fundamental kind for scientific progress. Yet Sasha’s story shows that this is incomplete.

As Merton ( Reference Merton 1942 ), Longino ( Reference Longino 1990 ), Massimi ( Reference Massimi 2022 ), and many others have emphasized, science is a social institution. Many scientists and philosophers of science have held that science is fundamentally public and that its methods and evidence must be intersubjectively accessible (e.g., Shapin and Schaffer Reference Shapin and Schaffer 1985 ; Popper Reference Popper 1959 ; though for pushback against this publicity requirement, see Goldman Reference Goldman 1997 ). Moreover, some philosophers argue that scientific communities are themselves epistemic agents (e.g., Bird Reference Bird 2022 ). A scientific finding must be made public in one way or another, at some time or other, for that finding to contribute to scientific progress, and the relevant scientific community must engage with that finding, and hold that finding to its standards, to determine if a change in confirmation pertaining to that finding is justified; if it is justified, the community can do further work on the hypothesis, refining it or relying on it to discover new findings; if it is not justified, further work can be done on it, or the finding can be discarded. (Ultimately, the community can decide whether the finding receives the benediction of truth, but as I argued in section 4 , progress itself occurs at the moment of justified change in confirmation, not at the moment of benediction.)

Let us call this the community uptake requirement for scientific progress. One consideration in favor of the community uptake requirement is the simple fact that for future scientific work to develop based on an earlier finding, that finding must be available to other scientists. Another consideration in favor of the community uptake requirement is based on the lesson Longino ( Reference Longino 1990 ) taught us about the importance of criticism in science—if scientific findings are not shared in one way or another, they cannot be criticized, and criticism is a hallmark of objectivity. No one was able to critically evaluate Sasha’s discovery. Still another consideration in favor of the community uptake requirement is that science education requires the content of science to be available. Still another consideration in favor of the community uptake requirement is Bird’s ( Reference Bird 2022 ) argument that scientific communities themselves can be the bearers of scientific knowledge. Finally, benediction can occur only if the community uptake requirement is satisfied (For an extended and compelling argument that scientific justification is fundamentally communal and intersubjective, I recommend chapter 3 of Gerken [ Reference Gerken 2022 ] and chapter 4 of Bird [ Reference Bird 2022 ], the latter of which develops an account of “social knowing.”).

One might respond by holding that the work that goes into satisfying the community uptake requirement is not itself epistemic. This response could say that it is the cognitive achievement alone that matters for scientific progress. What is subsequently done with that cognitive achievement, goes this response, such as publication or discussion in the public sphere or education, does not add anything to the cognitive achievement itself. Many contingent, noncognitive (sociological) reasons could limit the uptake of scientific findings, but these should not speak against a scientific achievement counting as a contribution to progress. Yet this response is too insensitive to the social structure and function of science. (Making a related point, Harris [ Reference Harris 2021 ] argues to the effect that it is the doxastic states of communities of scientists rather than of individual scientists that matter for scientific progress.)

An interesting example of uptake not occurring can be seen in mathematics today in a dispute over whether the so-called abc conjecture has been proven. The abc conjecture is a fundamental conjecture in number theory, and were it true, many other famous theorems in number theory would follow, such as Fermat’s last theorem. In 2012 the mathematician Shinichi Mochizuki posted on his own website a putative proof of the abc conjecture that ran for six hundred pages and was based on novel mathematical theory that he alone had developed over years. Mochizuki told no colleagues about his proof, but he had little need to, as rumors were already circulating. Yet when fellow mathematicians began to discuss the preprint, they noted that “it involves ideas which are completely outside the mainstream” and that it was like a “paper from the future, or from outer space” (Ellenberg Reference Ellenberg 2012 ). Most mathematicians today consider the conjecture still unproven, and some have noted specific flaws in the putative proof. Mochizuki claims that the failure is with other mathematicians and not the proof. I find it compelling to think that at this point, the Mochizuki proof has offered little progress; if, in the future, mathematicians come to accept the validity of the proof, then progress will be made, but importantly, the work that went into the proof itself will constitute only part of that progress.

6. Conclusion

I have offered a new account of scientific progress that is superior to existing accounts. Existing accounts of scientific progress are either too demanding, as with the accounts that have a truth requirement, or not demanding enough, as with the problem-solving accounts of scientific progress. Science makes progress, on my account, when there is a change in justification. This account of scientific progress is more in line with scientific practice than are competing accounts, as scientific practice is fundamentally centered around practices of justification, and the fallibilism and organized skepticism of science are better suited to a justification-centered account of scientific progress. Finally, an account of scientific progress can be complete only by taking into account the social structure of science.

Acknowledgments

I’m grateful to Adria Segarra, Arthur Harris, Benjamin Chin-Yee, and Ina Jäntgen for detailed written commentary and discussion; to three anonymous reviewers for valuable comments; and to Alexander Bird, Florence Robinson Adams, and Oliver Holdsworth for helpful discussion.

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• Volume 91, Issue 3
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• DOI: https://doi.org/10.1017/psa.2023.118

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• Books & Arts
• Published: 11 April 2012

In retrospect: The Structure of Scientific Revolutions

• David Kaiser 1  

Nature volume  484 ,  pages 164–165 ( 2012 ) Cite this article

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David Kaiser marks the 50th anniversary of an exemplary account of the cycles of scientific progress.

The Structure of Scientific Revolutions: 50th Anniversary Edition

• Thomas S. Kuhn

Fifty years ago, a short book appeared under the intriguing title The Structure of Scientific Revolutions . Its author, Thomas Kuhn (1922–1996), had begun his academic life as a physicist but had migrated to the history and philosophy of science. His main argument in the book — his second work, following a study of the Copernican revolution in astronomy — was that scientific activity unfolds according to a repeating pattern, which we can discern by studying its history.

Kuhn was not at all confident about how Structure would be received. He had been denied tenure at Harvard University in Cambridge, Massachusetts, a few years before, and he wrote to several correspondents after the book was published that he felt he had stuck his neck “very far out”. Within months, however, some people were proclaiming a new era in the understanding of science. One biologist joked that all commentary could now be dated with precision: his own efforts had appeared “in the year 2 B.K.”, before Kuhn. A decade later, Kuhn was so inundated with correspondence about the book that he despaired of ever again getting any work done.

By the mid-1980s, Structure had achieved blockbuster status. Nearly a million copies had been sold and more than a dozen foreign-language editions published. The book became the most-cited academic work in all of the humanities and social sciences between 1976 and 83 — cited more often than classic works by Sigmund Freud, Ludwig Wittgenstein, Noam Chomsky, Michel Foucault or Jacques Derrida. The book was required reading for undergraduates in classes across the curriculum, from history and philosophy to sociology, economics, political science and the natural sciences. Before long, Kuhn's phrase “paradigm shift” was showing up everywhere from business manuals to cartoons in The New Yorker .

Kuhn began thinking about his project 15 years before it was published, while he was working on his doctorate in theoretical physics at Harvard. He became interested in developmental psychology, avidly reading works by Swiss psychologist Jean Piaget about the stages of cognitive development in children.

Kuhn saw similar developmental stages in entire sciences. First, he said, a field of study matures by forming a paradigm — a set of guiding concepts, theories and methods on which most members of the relevant community agree. There follows a period of “normal science”, during which researchers further articulate what the paradigm might imply for specific situations.

In the course of that work, anomalies necessarily arise — findings that differ from expectations. Kuhn had in mind episodes such as the accidental discoveries of X-rays in the late nineteenth century and nuclear fission in the early twentieth. Often, Kuhn argued, the anomalies are brushed aside or left as problems for future research. But once enough anomalies have accumulated, and all efforts to assimilate them to the paradigm have met with frustration, the field enters a state of crisis. Resolution comes only with a revolution, and the inauguration of a new paradigm that can address the anomalies. Then the whole process repeats with a new phase of normal science. Kuhn was especially struck by the cyclic nature of the process, which ran counter to then-conventional ideas about scientific progress.

At the heart of Kuhn's account stood the tricky notion of the paradigm. British philosopher Margaret Masterman famously isolated 21 distinct ways in which Kuhn used the slippery term throughout his slim volume. Even Kuhn himself came to realize that he had saddled the word with too much baggage: in later essays, he separated his intended meanings into two clusters. One sense referred to a scientific community's reigning theories and methods. The second meaning, which Kuhn argued was both more original and more important, referred to exemplars or model problems, the worked examples on which students and young scientists cut their teeth. As Kuhn appreciated from his own physics training, scientists learned by immersive apprenticeship; they had to hone what Hungarian chemist and philosopher of science Michael Polanyi had called “tacit knowledge” by working through large collections of exemplars rather than by memorizing explicit rules or theorems. More than most scholars of his era, Kuhn taught historians and philosophers to view science as practice rather than syllogism.

Most controversial was Kuhn's claim that scientists have no way to compare concepts on either side of a scientific revolution. For example, the idea of 'mass' in the Newtonian paradigm is not the same as in the Einsteinian one, Kuhn argued; each concept draws meaning from separate webs of ideas, practices and results. If scientific concepts are bound up in specific ways of viewing the world, like a person who sees only one aspect of a Gestalt psychologist's duck–rabbit figure, then how is it possible to compare one concept to another? To Kuhn, the concepts were incommensurable: no common measure could be found with which to relate them, because scientists, he argued, always interrogate nature through a given paradigm.

Perhaps the most radical thrust of Kuhn's analysis, then, was that science might not be progressing toward a truer representation of the world, but might simply be moving away from previous representations. Knowledge need not be cumulative: when paradigms change, whole sets of questions and answers get dropped as irrelevant, rather than incorporated into the new era of normal science. In the closing pages of his original edition, Kuhn adopted the metaphor of Darwinian natural selection: scientific knowledge surely changes over time, but does not necessarily march towards an ultimate goal.

Scientists have no way to compare concepts on either side of a scientific revolution.

And so, 50 years later, we are left with our own anomaly. How did an academic book on the history and philosophy of science become a cultural icon? Structure was composed as an extended essay rather than a formal monograph: it was written as an entry on the history of science for the soon-to-be-defunct International Encyclopedia of Unified Science . Kuhn never intended it to be definitive. He often described the book (even in its original preface) as a first pass at material that he intended to address in more detail later.

To me, the book has the feel of a physicist's toy model: an intentionally stripped-down and simplified schematic — an exemplar — intended to capture important phenomena. The thought-provoking thesis is argued with earnestness and clarity, not weighed down with jargon or lumbering footnotes. The more controversial claims are often advanced in a suggestive rather than declarative mode. Perhaps most important, the book is short: it can be read comfortably in a single sitting.

For the 50th-anniversary edition, the University of Chicago Press has included an introductory essay by renowned Canadian philosopher Ian Hacking. Like Kuhn, Hacking has a gift for clear exposition. His introduction provides a helpful guide to some of the thornier philosophical issues, and gives hints as to how historians and philosophers of science have parted with Kuhn.

The field of science studies has changed markedly since 1962. Few philosophers still subscribe to radical incommensurability; many historians focus on sociological or cultural features that received no play in Kuhn's work; and topics in the life sciences now dominate, whereas Kuhn focused closely on physics. Nevertheless, we may still admire Kuhn's dexterity in broaching challenging ideas with a fascinating mix of examples from psychology, history, philosophy and beyond. We need hardly agree with each of Kuhn's propositions to enjoy — and benefit from — this classic book.

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Kaiser, D. In retrospect: The Structure of Scientific Revolutions. Nature 484 , 164–165 (2012). https://doi.org/10.1038/484164a

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In This Article Expand or collapse the "in this article" section Scientific Progress

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• Progress and the Aims of Science
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Scientific Progress by Ilkka Niiniluoto LAST REVIEWED: 19 September 2023 LAST MODIFIED: 28 July 2015 DOI: 10.1093/obo/9780195396577-0055

Science is the systematic pursuit of new knowledge by using critical methods of inquiry. Scientists constitute a community of investigators jointly engaged in research to produce knowledge about nature, humanity, culture, and society. The notion of science may thus refer to a social institution, the researchers, the research process, the methods of inquiry, and scientific knowledge. Developments and changes in all of these aspects of science are studied by the history of science. Sociologists of science are especially interested in the professional status of the scientists and their academic institutions, the internal norms of the scientific community, forms of scientific communication, and the economics and funding systems of scientific research. Multidisciplinary science studies illuminate the interaction between science and society, especially the ways scientific advances have brought about social progress by improved technologies, economic prosperity, quality of life, and justice in society. Science education is concerned with the increased skill and expertise of the scientists. Methodology looks at the development of new methods and tools of research, such as the refinement of scientific instruments, techniques of experimentation, and statistical and computational methods. Philosophy of science analyzes science from a cognitive perspective as an attempt to improve and increase scientific knowledge. In particular, axiological studies discuss the aims of scientific inquiry. Logic and epistemology study the proper ways of scientific thinking, argumentation, and inference. The language of science and its relations to reality, observation, and theory; explanation and prediction; and patterns of scientific change belong to the main themes of general philosophy of science. Philosophical studies may also focus on key issues about special scientific disciplines, such as physics, biology, psychology, and economics. While the notion of scientific progress in the broad sense could cover improvements in all of these aspects of science, it is customary to restrict this title to advances of science in terms of its success in knowledge seeking. In this sense, scientific progress is a fundamental issue that has been actively debated within the philosophy of science since the 1960s. The task of philosophical analysis is to consider alternative answers to the conceptual or normative question: What is meant by improvement or progress in science? The definition of progress leads to the methodological question about indicators of progress: How can we recognize progressive developments in science? With these tools one can then study the factual question: To what extent and in which respects has science been progressive?

Science was born around 600 BCE with Greek philosophers of nature who wished to reveal the basic elements of the physical world by independent human reason without myths or religions. Ancient thinkers made significant contributions in mathematics (Euclid), astronomy (Ptolemy), physics (Archimedes), and medicine (Hippocrates, Galen). Plato defined the concept of knowledge (Gr. episteme ) as justified true belief, and Aristotle outlined the logical grounds of scientific reasoning. The Greek tradition was transmitted to medieval Latin scholars by the Arabs, who themselves made discoveries in chemistry and optics. The ruling synthesis of Aristotle and Christianity encouraged conservatism in the new universities, but new revolutionary trends broke out in astronomy with Copernicus in the 15th century and Johannes Kepler and Galileo in the early 17th century. As emphasized in Bury 1932 , in the modern age, science was strongly promoted by the philosophers of the Enlightenment. A broader and historically more extensive picture, with interactions between scientific and social progress, is painted in Nisbet 1980 . But when precisely was it realized that science is a collective enterprise that can accumulate new established results? The self-understanding of the Renaissance period is ambiguous, because this notion refers to the rebirth of ancient wisdom, not to novelties and discoveries. Crombie 1975 and Molland 1978 argue that the roots of the idea of progress can be found in the Middle Ages, while Zilsel 1945 locates them in the 16th century. Cohen 1976 shows that the concept of “scientific revolution” was not used by great scientists of the early modern period but rather was borrowed from the sphere of French politics in the 18th century. Sarton 1936 concludes that science is the paradigm of progress because progress has no definite and unquestionable meaning in other fields than science. Bernal 1969 sees the cultural value of science in its ability to give rational means for social planning.

Bernal, J. D. Science in History . 4 vols. Harmondsworth, UK: Penguin, 1969.

The British historian of science, with pragmatist and Marxist influences, opposes the reactionary ideal of pure science, the pursuit of truth for its own sake, which has done much to hinder the development of science. The progressive growth of science comes from its interconnection with industry.

Bury, J. B. The Idea of Progress: An Inquiry into Its Origin and Growth . New York: Macmillan, 1932.

Classic study of the idea of progress. Bury argues that, despite some anticipations by medieval and Renaissance thinkers, the conception of progress in human history was established only in the 17th and 18th centuries by the optimist thinkers of the Enlightenment. The progress of science had a strong impact in this historical process.

Cohen, I. Bernard. “The Eighteenth-Century Origins of the Concept of Scientific Revolution.” Journal of the History of Ideas 37.2 (1976): 257–288.

DOI: 10.2307/2708824

We are accustomed to describing the work of Galileo and Isaac Newton as “the scientific revolution.” A leading expert on Newton’s physics shows that the notion of “revolution” was applied to science for the first time during the heyday of the political revolution in France in the late 18th century. Available online by subscription.

Crombie, A. C. “Some Attitudes to Scientific Progress: Ancient, Medieval, and Early Modern.” History of Science 13 (1975): 213–230.

DOI: 10.1177/007327537501300303

A leading expert on medieval science shows how the idea of scientific progress gradually started to emerge already in the ancient and medieval world.

Molland, A. G. “Medieval Ideas of Scientific Progress.” Journal of the History of Ideas 39.4 (1978): 561–578.

DOI: 10.2307/2709442

“We are dwarfs on the shoulders of giants.” This famous expression of the idea that science is a cumulative effort of successive generations is often attributed to Isaac Newton. Molland shows that this image was originally used by Bernard of Chartres in the 12th century. Available online by subscription.

Nisbet, Robert. History of the Idea of Progress . London: Heinemann, 1980.

The early history of the idea of progress includes Judeo-Christian eschatology with its linear conception of time and the new economic activities in the 13th century with trade and voyages around the world.

Sarton, George. The Study of the History of Science . Cambridge, MA: Harvard University Press, 1936.

The first professor of history of science at Harvard University understood that the development of science may be at any time disrupted by conceptual and theoretical revolutions. Yet Sarton claimed that only the history of science can “illustrate the progress of mankind” because “the acquisition and systematization of positive knowledge are the only human activities which are truly cumulative and progressive” (p. 5).

Zilsel, Edgar. “The Genesis of the Concept of Scientific Progress.” Journal of the History of Ideas 6.3 (1945): 325–349.

DOI: 10.2307/2707296

A Viennese philosopher interested in the origins of science claims that the idea of scientific progress was created in the 16th century by artisans working outside the academic institutions. For example, Leonardo da Vinci was at the same time a painter, architect, engineer, and anatomist. The conceptual distinction among science, art, and technology was established only in the late 18th century by Immanuel Kant’s critical treatises. Available online by subscription. Reprinted in The Social Origins of Modern Science , edited by Diederick Raven, Wolfgang Krohn, and Robert S. Cohen, 96–122 (Dordrecht, The Netherlands: Kluwer, 2000).

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Understanding scientific progress: the noetic account

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• Volume 199 , pages 11249–11278, ( 2021 )

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What is scientific progress? This paper advances an interpretation of this question, and an account that serves to answer it (thus interpreted). Roughly, the question is here understood to concern what type of cognitive change with respect to a topic X constitutes a scientific improvement (to a greater or lesser extent) with respect to X . The answer explored in the paper is that the requisite type of cognitive change occurs when scientific results are made publicly available so as to make it possible for anyone to increase their understanding of X . This account is briefly compared to two rival accounts of scientific progress, based respectively on increasing truthlikeness and accumulating knowledge, and is argued to be preferable to both.

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Scientific Progress, Understanding and Unification

The Noetic Account of Scientific Progress and the Factivity of Understanding

What is theoretical progress of science?

Avoid common mistakes on your manuscript.

1 Introduction

The progress of science is astounding. Just two centuries ago, people suffering from infectious diseases would have been told that their illnesses were caused by ‘miasma’, i.e. impure air arising from decomposing organic matter. Progress was made in the late 19th century, when the miasma theory was replaced by the theory that infectious diseases are caused by unobservably small entities passing between organisms, i.e. ‘germs’. The progress that has since been made builds on this theory, e.g. in the discovery that some infectious diseases (such as tuberculosis and the plague) are caused by bacteria, while others (such as seasonal influenza and COVID-19) are caused by viruses. So at least on the topic of infectious diseases, scientists have made significant progress over the years. But why? In virtue of what do these developments count as progressive? What is scientific progress?

It is natural to worry that this question is too ‘philosophical’, in the pejorative sense of the term, to admit of a definitive answer. For example, Chang describes it as “one of the most significant issues in the philosophy of science today”, but then immediately notes its “immense difficulty” (Chang 2007 , p. 1). Part of that difficulty is surely that the question itself can seem unclear , misguided , and even pointless : (i) What would it even be to advance a philosophical account of scientific progress? (ii) Doesn’t science progress in a variety of quite different ways, depending on the scientific field, its methodology, or even the particular research project in question? (iii) And even supposing that some general account of scientific progress could be provided, what would be the point of such an exercise?

In this paper, my first aim is to show that these worries can be convincingly allayed. In response to (i), I will argue that the question, ‘What is scientific progress?’, has at least one interpretation on which the question itself is perfectly clear and intelligible. In response to (ii), I will argue that, on this interpretation, there is no particular reason to think that a general account of scientific progress cannot be provided. Finally, in response to (iii), I will argue that this interpretation of the question makes evident why answering the question thus interpreted is important—viz., not just because of its intrinsic intellectual importance, but also due to the practical implications of different answers.

My second main aim for this paper is to elaborate and argue for a particular answer to the question thus interpreted. This answer is based on the idea that progress regarding some phenomenon consists in increasing our potential to understand that phenomenon, a proposal closely akin to what I have previously called the noetic account of scientific progress (Dellsén 2016 ). The current paper develops this proposal by coupling it with a general definition of ‘understanding’, and by specifying whose potential increase in understanding is at issue. The resulting account is then compared to two rival accounts, which respectively define progress in terms of increasing truthlikeness and accumulating knowledge, and defended against three potential objections.

2 The question of scientific progress

As promised in the introduction, I start by clarifying the question at issue, ‘What is scientific progress?’, so as to make clear why it’s intelligible, tractable, and important. I will proceed by first making a number of preliminary points to precisify the relevant concept of scientific progress, before then returning to the question itself, how to go about answering it, and why that matters.

A first thing to note is that scientific progress , in contrast to scientific change , is a partly normative or evaluative concept, i.e. a ‘thick’ concept. To say that science made progress between \(t_1\) and \(t_2\) is to say that there was some improvement in or of science between \(t_1\) and \(t_2\) (cf. Niiniluoto 2019 , §2.2). This is not to say that the overall state of the world is better at \(t_2\) than \(t_1\) , of course, since other things might have changed for the worse between \(t_1\) and \(t_2\) . Nevertheless, something must improve in order for scientific progress to occur. It follows immediately that choosing between accounts of scientific progress has normative implications. For example, all else being equal, if one account implies that successfully completing a research project would achieve scientific progress while another account implies that no progress would be made even on a successful completion of that project, then the first account, but not the second, implies that scientists have some reason to pursue that project.

This in turn has important implications for the methodology most appropriate in debates about scientific progress. Specifically, this arguably means that accounts of scientific progress should be tested against our considered normative judgments, e.g. regarding whether to pursue some research project at the expense of another. By contrast, these accounts should not be tested against our linguistic intuitions about whether we would initially and unreflectively be inclined to refer to a given episode as ‘scientific progress’. After all, if it turned out that the term ‘scientific progress’, as it is in fact used in natural language, systematically classifies less pursuitworthy research projects as more ‘progressive’ (and vice versa), then we should surely modify, or re-engineer, the concept of scientific progress so as to fit with our considered judgments about what sorts of pursuits are in fact most valuable. Footnote 1

Second, what is the ‘something’ that needs to improve between \(t_1\) and \(t_2\) in order for scientific progress to occur during this period? It’s tempting to answer that it is science as a whole, or perhaps some particular scientific discipline. But for reasons noted by Niiniluoto ( 2019 , §2.1), accounts of scientific progress are not meant to cover all ways in which a scientific discipline could improve. For example, a discipline could improve by virtue of receiving more funding, by increasing its independence from pernicious outside influences, or by increasing gender equality among scientists. Although such changes would clearly be improvements in a general sense, the debate about scientific progress concerns a narrower class of changes that Niiniluoto labels ‘cognitive’. Dellsén ( 2018 , p. 2) characterizes these as having “to do with improvement in our theories, hypotheses, or other representations of the world, rather than other improvements of or within science”.

Third, some recent discussions of scientific progress have introduced a useful distinction between what constitutes scientific progress and what merely promotes it (see, e.g., Bird 2008 , p. 280; Dellsén 2016 , p. 73). A cognitive change constitutes progress when the change is an improvement in some respect regardless of what other changes are thereby brought about, or made more likely to be brought about, at some later time. By contrast, a cognitive change promotes progress when the change is an improvement only in so far as later changes are brought about, or made more likely to be brought about (i.e. probabilified), by that cognitive change. Both constituting and promoting progress thus count as improvements, but the latter counts as an improvement only in virtue of leading to or probabilifying an occurrence of the former at some later time. Footnote 2 For example, consider the formulation of a new concept that is subsequently used to state a theory which, let’s suppose, is an improvement on the previous theories in some domain. The formulation of this concept would arguably not itself constitute progress, but it would definitely promote progress in so far as it helps scientists to state, and thus eventually accept, a progressive theory. Footnote 3

It should be clear that, depending on the the phenomenon in question, a number of quite different things might promote progress to a greater or lesser extent. For example, randomized controlled trials promote progress on the effectiveness of medical treatments, while computer simulation models promote progress on biological and economic systems (and not normally vice versa). As this makes clear, there is no reason to think there is a unified general story to tell about what promotes progress across all scientific disciplines. Indeed, what presently promotes progress within some discipline might cease to do so, or do so to a lesser extent, in the future, given technological or methodological changes. So there is a strong case to be made for a kind of ‘pluralism’ about what promotes progress. Note, however, that it does not follow that we should be pluralists about what constitutes progress, since these different ways of promoting progress might well be instrumental for achieving the same type of cognitive improvement.

Fourth, scientific progress is gradable—a matter of degree—in the sense that a given episode can be more or less progressive, perhaps in addition to being progressive outright (in a binary sense). Footnote 4 Although some theorists fail to address what determines degrees of progress, instead providing accounts only of outright progress, Footnote 5 this is arguably an unmotivated restriction of the topic at hand. If at all possible, an account of scientific progress should explain not just why a given episode is progressive, but also why it is more or less progressive than another episode—at least when the two episodes concern the same topic. For example, an account of scientific progress worth its salt should explain why adopting Tycho Brahe’s geo-heliocentric model of the solar system would not have constituted as much progress as adopting Kepler’s version of Copernicus’s heliocentric model, even though adopting either model would arguably have been an improvement on the earlier Ptolemaic model.

A fifth and final point is that we can distinguish between a topic-specific concept of scientific progress ( progress-on - X ), and a more general, across-topic concept of scientific progress ( overall progress ). Consider an episode that exhibits cognitive improvement with respect to one topic \(X_1\) , and yet simultaneously exhibits the opposite, i.e. cognitive decline, with respect to another topic \(X_2\) . How should we describe such an episode in terms of scientific progress? Well, if we are using a topic-specific concept of scientific progress, progress-on- X , such an episode can simply be described as simultaneously exhibiting progress on \(X_1\) , and the opposite of progress, i.e. regress, on \(X_2\) . On the other hand, if we are using the general, across-topic concept of overall progress , then the question of whether there is progress in that sense during the episode presumably turns on whether the there was enough progress made on \(X_1\) to outweigh the regress on \(X_2\) . This suggests that overall progress can be defined as the aggregation or sum of degrees of progress (and regress) on the various different topics \(X_1\) , \(X_2\) , etc., on which cognitive change takes place during an episode. Footnote 6 Since progress-on- X therefore seems to be the more fundamental notion of the two, we will primarily be concerned with it in what follows.

To summarize, then: scientific progress is a type of improvement over time, so characterizing a change as progressive has immediate normative implications; this improvement concerns cognitive changes specifically, rather than other types of improvements in or of science; the question as to what constitutes such progress can, and should, be distinguished from what promotes it; scientific progress is gradable , in the sense that an episode can be said to be more or less progressive in addition to being outright progressive; and finally, a concept of progress-on - X can be distinguished from, and yet used to define, a concept of overall progress . With all this in mind, we can say that the seemingly simple question ‘What is scientific progress?’ can be precisified as follows:

What type of cognitive change with respect to a given topic X constitutes a (greater or lesser degree of) scientific improvement with respect to X ?

In the introduction, I mentioned three types of worries about our original question, ‘What is scientific progress?’. The first was that the question was unclear. That, I submit, need no longer worry us, since the original question can now be replaced by the painstakingly precise (Q). The second worry was that science clearly progresses in different ways, depending on the scientific field, methodology, or research project. That worry is assuaged by pointing out that different disciplines may promote progress in different ways even if the same type of cognitive change constitutes progress across those disciplines. Furthermore, by relativizing progress to a particular topic X , we have opened up the possibility that what constitutes progress with regard to one topic \(X_1\) could differ from what constitutes progress with regard to another topic \(X_2\) . Finally, the worry that an account of scientific progress would be pointless is clearly misconceived, since as we have seen the question of progress is ultimately a normative question that has direct implications for what scientists ought to spend their time and resources on. It is thus an issue that is of obvious relevance not only to philosophers of science, but also to science administrators and working scientists.

3 The noetic account, revised and elaborated

In this section, I develop an account of scientific progress, i.e. an answer to (Q), that centers around the concept of understanding . To a first approximation, the account holds that scientific progress with respect to X consists in a change in the publicly available information about X that helps us increase our understanding of X , where ‘increased understanding’ is defined as gaining a more accurate or comprehensive model of X ’s dependence relations, such as its causal relations. In so far as such dependence relations ground explanation and prediction, e.g. through causal explanation, this account implies a strong link between scientific progress, on the one hand, and explanation and prediction, on the other. In this respect, the current account resembles the original noetic account (Dellsén 2016 ). Indeed, although there are differences between the two accounts—some of which will be brought out below—the former is sufficiently close to the latter to be viewed as a modification and elaboration of the original account. In what follows, I spell out this new version of the noetic account.

Let me first make a methodological point. In what follows, I offer a definition of the the relevant notion of understanding, before defining progress in terms of that notion. Although I believe that this definition of understanding is at least as good as any alternative definition on offer, Footnote 7 I will not provide any arguments to that effect in what follows. Indeed, those who prefer a different definition of the concept of understanding may take the definition that I offer here as purely stipulative. After all, our concern is ultimately not with the nature of understanding as such, but rather with an account of scientific progress—i.e., with answering (Q). For those purposes it is merely a matter of terminology whether the account is stated in terms of the notion of understanding or in terms of the concepts used below to attempt to define that notion. Thus, if you prefer, you may take ‘understanding’ to be a mere label for the cognitive state, described below, in terms of which the current noetic account defines scientific progress.

3.1 Dependency models

The definition of understanding to which I appeal in what follows makes use of the notion of a dependency model . This is a model of the dependence relations that aspects of a given phenomenon stand in, or fail to stand in, to other aspects of the phenomenon, or of other phenomena. Such a model thus contains information about relations between (aspects of) phenomena—both ‘positive’ information about how they are related, and ‘negative’ information about how they fail to be related. The relations in question are dependence relations , the paradigmatic instance of which is causation , but which may include other dependence relations such as grounding . The relata of these relations are variables, rather than specific or actual values of such variables; they may be either continuous (e.g., an object’s mass m ) or discreet (e.g., a population size N ). Thus dependence relations encode information not just about the actual state of some phenomenon, but also how the phenomenon would have been different if other things had been different in some specific way.

So a dependency model of a phenomenon, in so far as it is accurate and comprehensive, encodes information about dependencies. Most dependency models that are even just somewhat accurate and comprehensive will be enormously complex, but let me illustrate with a simple, toy example. According to Hooke’s law, the force exerted by a spring on an object fastened to it, displaced at a distance x from a relaxed position, is given by \(F_s=-kx\) , where k is a positive constant specifying the ‘stiffness’ of the spring (the minus sign indicates that that the force \(F_s\) is opposite to that of the displacement x ). So if the object is pulled a distance x and then released, then assuming as an idealization that no other forces act on the object, the force \(F_s\) will accelerate the object towards its relaxed position in accordance with Newton’s second law, \(F=ma\) . Hence the object’s acceleration when released will be \(a=-\frac{kx}{m}\) . This tells us a great deal about what the object’s acceleration depends on, e.g. its mass; and, indeed, about what it does not depend on, e.g. its volume. This is a paradigmatic example of a (very simple) dependency model, in this case of a composite phenomenon consisting of an elastic spring and an object attached to it.

In the above example, all the dependence relations involved are causal—at least arguably so. As I have intimated, however, this is not always the case. Suppose we supplement this model with information about how the spring’s stiffness k is determined. Now, k can clearly be calculated from Hooke’s law by plugging in actual values for the force \(F_s\) and distance x . But what k depends on has to do with various facts about the spring itself, e.g. its length at relaxed position, the number of coils, the diameters of those coils, and the material from which the spring is constructed. The relation between k and these facts about the spring is arguably not causation; rather, it is something closer to grounding. Footnote 8 So a more comprehensive dependency model of the spring and its attached object includes these arguably-non-causal dependence relations as well. An even more comprehensive dependency model would contain even more (causal or non-causal) information of this sort.

A more accurate dependency model, by contrast, would correct some of the inaccuracies contained in the above model. For example, it is of course not true of any real system of this sort that the only force that acts on the object is due to the spring. Hence we cannot really identify the F in \(F=ma\) with the \(F_s\) in \(F_s=-kx\) , as would strictly speaking be required to derive \(a=-\frac{kx}{m}\) . For example, if the spring and object are located on an horizontal surface, then a (non-zero) friction force, \(F_f = -\mu mg\) , will act against \(F_s\) , so that \(F=F_s-F_f\) . From this it follows immediately that \(a = \frac{F}{m} = -\frac{kx}{m}+\mu g\) . We thus have a more accurate dependency model of the spring and the attached object, e.g. in that we see that (and how) the acceleration of the object depends on the friction between the object and the surface. This model tells us, among other things, that the effect of friction on acceleration does not depend on the object’s mass.

3.2 Scientific understanding

So much for dependency models. What has this got to do with understanding? Well, on the view of scientific understanding I favor (Dellsén 2020 ), the latter can be defined in terms of the former: an agent S understands X if and only if S grasps a sufficiently accurate and comprehensive dependency model of X ; and S ’s degree of understanding of X is proportional to the accuracy and comprehensiveness of their dependency model of X . I note immediately that the target of this type of understanding, X , is some part of our world; not a mere representation thereof, such as a theory, concept, or explanation. In the literature on understanding, this type of understanding is generally referred to as ‘objectual understanding’, and often contrasted with ‘understanding why’ or ‘explanatory understanding’ (see, e.g., Kvanvig 2003 ; Khalifa 2013 ; Kelp 2015 ). I note also that there are many terms that intentionally do not occur in this definition, including notably ‘justified’ and ‘know’. As we shall see, understanding can come apart from what philosophers typically mean by those terms.

What about truth ? Well, it follows immediately from the definition above that an increased understanding of X can be identified with having a more accurate, or more comprehensive, dependency model of X . Thus incorporating true information into one’s dependency model of X , in so far as it reveals something about the relevant dependence relations, will necessarily increase one’s understanding of X . In this sense, understanding is ‘factive’. And yet the current definition allows for departures from the truth to increase understanding, most straightforwardly since incorporating an intentional approximation, which deliberately contains a slight falsehood, can significantly increase the comprehensiveness of a model at the expense of a small loss of accuracy. To return to our earlier example, setting \(F=F_s\) —although strictly false, since \(F_f\) is non-zero—initially contributed to understanding the object’s acceleration. However, as the subsequently modified version of the example also illustrates, we would in that case gain even more understanding by de-approximating and instead setting \(F=F_s-F_f\) .

Many theorists associate understanding very closely with explanation (e.g., Strevens 2013 ; de Regt 2017 ; Khalifa 2017 ). On the above definition, this is correct only in so far as understanding consists in modelling the dependence relations that form the ontological basis for explanation. Thus it is true that, when it’s possible to explain X or aspects of X , a completely accurate and comprehensive understanding of X will provide all the information needed for explanation. However, understanding can also consist in the realization that a phenomenon or some aspect thereof cannot be explained at all, or that it cannot be explained by some particular other phenomenon or aspect thereof. For example, we noted before that the decrease of acceleration due to friction exerted on an object moving on a horizontal surface does not depend on its mass; hence it cannot be explained by its mass. Nevertheless, incorporating this very fact—that the decreased acceleration due to friction does not depend on the object’s mass—into our dependency model increases our understanding of the object’s acceleration. So understanding, by the above definition, is in this way a more general concept than explanation, and should not simply be identified with the cognitive benefits of explanation.

Related to this is the fact that understanding brings with it various other cognitive benefits. Chief among these are manipulation and prediction. Consider the spring again. Suppose you want to modify the surface on which the object is placed so as to make sure that it does not move at all when released at a distance x from the spring’s relaxed position. You might do this by replacing a smooth surface with one that is covered in sandpaper, for example. If you grasp the final dependency model described above, in which \(a=-\frac{kx}{m}+\mu g\) , this can be achieved by setting \(a=0\) and then isolating the friction coefficient \(\mu = \frac{kx}{mg}\) , which measures the extent to which the object and the surface create friction with one another. This tells you what grit size you need for the sandpaper, for example, so as to get the object to stay put at a given distance x from a relaxed position. Similarly, for the purposes of prediction, you need to know what will happen given the current state of the spring—or what would happen given some counterfactual state of the spring. Your understanding, via your dependency model, tells you precisely that, e.g. by revealing what the acceleration of the object will be when released at distance x , or would be if released at some alternative distance \(x'\) .

3.3 Scientific progress

Now, how do we get from this definition of understanding to an account of scientific progress, i.e. to an answer to (Q)? We might say that scientific progress, i.e. the type of cognitive change with respect to a given phenomenon X that constitutes scientific improvement relative to X , is increased understanding of X . However, this is incomplete as it stands, since it fails to specify whose understanding increases in scientifically progressive episodes. Indeed, there is a more general question in the vicinity here that applies to any account of scientific progress, viz. whose cognitive attitudes must in some way improve in order for scientific progress to take place? In so far as this question has been discussed at all, the agents in question have been assumed to be the relevant scientists themselves, either individually or collectively as a group. Footnote 9 Applied to an understanding-based account, this implies that scientific progress relative to X occurs precisely when scientists themselves (individually or collectively) increase their understanding of X .

On further reflection, however, this exclusive focus on the cognitive attitudes of scientists themselves seems unmotivated. If all that really improved through scientifically progressive episodes were the scientists’ own attitudes, e.g. in increasing their understanding, then why should non-scientists care about scientific progress at all? In particular, how could the extensive funding of ‘pure’ scientific research, with no clear practical benefits for non-scientists, be justified if scientific progress merely consisted in some scientists improving their cognitive attitudes? In light of this problem, I suggest we move to a conception of scientific progress according to which it is not the cognitive attitudes of those who make scientific progress that determine whether an episode is progressive; rather, progress is determined by the publicly available information, such as that contained in peer-reviewed journal articles, on the basis of which any relevant member of society (including scientists but not excluding non-scientists) can form or sustain the relevant type of cognitive attitudes. In the case of the noetic account, then, I suggest that what matters to progress on X is whether changes in the publicly available scientific information makes it possible for relevant members of society to increase their understanding of X . Footnote 10

We are now—finally!—in a position to formulate a revised noetic account of scientific progress:

The noetic account (restated): The type of cognitive change with respect to a given phenomenon X that constitutes (a greater or lesser degree of) scientific improvement relative to X is a change due to scientific research in the publicly available information that enables relevant members of society to increase their understanding of X .

This somewhat Procrustean formulation of the account is meant to explicitly mirror the question to which it is an answer, (Q). More colloquially, the noetic account thus reformulated holds that scientific progress consists in making available scientific information that helps us as a society to better understand relevant phenomena. Given the identification of understanding with dependency modelling, scientific progress enables us to model dependencies in these and related phenomena—which, in turn, helps us explain, manipulate and predict them on a regular basis.

At this point it is worth reiterating that there may be many different ways of promoting scientific progress even if there is a single type of cognitive change that constitutes progress (see Sect. 2 ). On the noetic account, progress is promoted by any development that leads to or probabilifies changes in available scientific information which enable relevant members of society to increase their understanding. Thus most of the everyday activities of working scientists—including, for example, experimentation and observation, theoretical exploration, and developing novel methods—will promote scientific progress on the noetic account, because and in so far as these are important steps towards enabling us to increase our understanding of some phenomena. To say that these activities promote progress is emphatically not to say that they are less important than the activities that constitute progress. After all, a given episode (e.g. an especially decisive experiment) might promote a great deal more progress than another episode (e.g. a minor modification to a causal model) constitutes, in which case there is a straightforward sense in which the former contributes more to scientific progress than the latter. Footnote 11

4 Rival accounts of scientific progress

In this section, I consider two of the main rivals to the noetic account of scientific progress, viz. the truthlikeness account initially proposed by Popper ( 1963 , 1979 ) and subsequently developed by Niiniluoto ( 1980 , 1984 , 2014 , 2017 ), and the epistemic account , as formulated and defended by Bird ( 2007 , 2008 , 2016 , 2019 ). Footnote 12 For each account, I will compare it to the noetic account—highlighting the points on which the accounts are in agreement, and explaining where they diverge—and then briefly argue that the noetic account improves on its rival.

4.1 The truthlikeness account

Briefly, the truthlikeness account holds that scientific progress occurs when accepted scientific theories get closer to the truth, i.e. become more truthlike. In the special case of one theory \(T_1\) being replaced with another theory \(T_2\) (with no other changes or additions to accepted theories), scientific progress occurs if and only if \(T_1\) is more truthlike than \(T_2\) . The key notion of truthlikeness (or verisimilitude ) is meant to measure the extent to which a theory captures the complete truth about the world in a given conceptual framework. Thus, one way in which \(T_2\) may be more truthlike than \(T_1\) is if \(T_2\) makes true (or approximately true) claims on which \(T_1\) is silent, since \(T_2\) would thus capture a larger part of the complete truth about the world than \(T_1\) . Another way for \(T_2\) to be more truthlike than \(T_1\) is if \(T_2\) corrects some false claims made by \(T_1\) . In both cases, replacing \(T_1\) with \(T_2\) would constitute progress on the truthlikeness account.

In Niiniluoto’s version of the truthlikeness account, which is the most developed account of this sort in the literature, the truthlikeness of a scientific theory T is defined relative to a language L . Roughly, T ’s truthlikeness is then a measure of the similarity between a maximally specific claim C * in L , which fully captures everything that is true, and a disjunction of other such maximally specific claims \((C_1 \vee ... \vee C_n)\) in L , which captures the content of T by effectively listing all the maximally specific possible states of affairs allowed by T (Niiniluoto 1987 ; see also Oddie 1986 ). This definition of truthlikeness brings out a rather notorious problem for truthlikeness accounts, viz. that extant definitions are ‘language-dependent’ in the sense that the truthlikeness of T may be higher or lower in another language \(L'\) as compared to L . In so far as it is implausible that there is any single objectively correct language relative to which truthlikeness can be defined, this leads to progress being language-relative. It is a matter of contention whether this is a serious problem for the truthlikeness account (see, e.g., Miller 2006 ; Bird 2016 ; Oddie 2016 ; Niiniluoto 2017 ); since this is well-trodden terrain, I will not comment further on this issue here.

In comparing the truthlikeness account to the noetic account, the first thing to say is that the two accounts are similar in two important respects. First, the intuitive notion of truthlikeness (of theories) corresponds quite closely to the noetic account’s two notions of accuracy and comprehensiveness (of dependency models). Thus, were it not for certain connotations of the term ‘truthlikeness’, such as the language-relativity therein and its focus on theories rather than dependency models, it would not be too misleading to state the noetic account in terms of increasing truthlikeness of dependency models. Footnote 13 Second, the truthlikeness account resembles the noetic account in imposing no distinctively epistemic requirements on scientific progress, such as the requirement that progressive theories or models be epistemically justified. Of course, as Niiniluoto ( 2017 , pp. 3299–3300) notes, accepted scientific theories generally enjoy at least some degree of empirical confirmation, but it does not follow on either account that scientific progress cannot occur in the absence of the type of justification required for knowledge (more on this in Sect. 4.2 ).

Regarding the differences between the noetic and truthlikeness accounts, note that where the truthlikeness account focuses on (increasingly truthlike) theories , the noetic account focuses on (increasingly accurate and comprehensive) dependency models . The main difference between these is that dependency models target specific phenomena in the world, whereas theories are more general and abstract claims with no particular target. Footnote 14 Of course, the two are not unrelated. As some of my examples above intimate, scientists use (or apply) theories to gain understanding of phenomena, i.e. to construct dependency models thereof. Earlier we saw how Hooke’s law, \(F_s=-kx\) , together with Newton’s second law of motion, \(F=ma\) , can be used to construct a dependency model of a (hypothetical) system consisting of an elastic spring and an attached object. This model reveals that, and how, the object’s acceleration a depends on its mass m , the displacement distance x , and the spring’s stiffness k . Since true or truthlike theories undergird understanding in this way, they are profoundly important for scientific progress from the noetic account’s point of view.

With that said, increasing understanding and increasing truthlikeness can come apart; when they do, progress follows the former rather than the latter. Consider first cases in which already existing theories are used to construct new dependency models that are either more accurate or more comprehensive than previous models. In such cases, theory stands still while understanding marches on. Our simple example of the spring provides a case in point. In constructing the dependency model of the system, with which we see (among other things) what and how the object’s acceleration depends on, we did not increase the truthlikeness of our theories. Admittedly, there is a sense in which a new ‘theory’ was added when we derived \(a=-\frac{kx}{m}\) from Hooke’s law and Newton’s second law of motion. However, precisely because this ‘theory’ follows logically from previously accepted theories, and thus adds no logical content to them, it cannot possibly increase the truthlikeness of accepted theories. Thus the truthlikeness theorist is forced to say, implausibly, that there is no progress in cases of this sort.

Another way in which increasing understanding can come apart from increasing truthlikeness concerns the use of idealizations to gain understanding. For our purposes, idealizations can be understood as falsehoods that are deliberately included in some representation. Now, in some cases, accepting theories with idealizations increases the truthlikeness of accepted theories in a straightforward way, since the idealized theory may capture part of the complete truth about the world in a way that previous theories failed to do—even when the new theory contains an idealization and is thus false (Niiniluoto 2017 , p. 3298). So to see how the noetic and truthlikeness accounts diverge in this respect, we must look to cases in which idealizations play a role in scientific progress even when more truthlike versions of the relevant theories are, or could be, accepted. Specifically, the cases I have in mind are those where a true or truthlike theory is accepted, and yet a corresponding idealized (and thus less truthlike) theory is either adopted or kept on the books Footnote 15 because the latter facilitates understanding in a way that former fails to do. Footnote 16

To use a familiar example, consider that the standard derivation of Boyle’s law ( \(P \propto \frac{1}{V}\) ) assumes that the molecules in a gas never collide with each other. Since this assumption is blatantly false of any real gas, the set of theories used in the derivation of Boyle’s law is clearly less truthlike than an alternative set of theories in which this assumption has been replaced with the (true) assumption that, while the molecules do collide, these collisions balance each other out. Indeed, Boyle’s law can be derived from this set of strictly true theories as well, so the truthlikeness account cannot even claim that the idealization here is a ‘necessary evil’ in our path towards true or truthlike theories. So why is the blatantly false assumption that molecules don’t collide kept on the books at all, as part of the publicly available information that scientists, engineers, and others, can draw upon? Why not throw it out like any other falsehood that has been replaced by a true or more truthlike alternative?

Roughly following Strevens’s ( 2008 , 2017 ) account of idealization, I suggest that the answer is that the idealization facilitates understanding in a way that the non-idealized assumption does not. The inclusion of such an obvious falsehood—that the molecules don’t collide at all—is a way of highlighting the absence of a dependence relation—in this case, between Boyle’s law holding of a particular gas, on the one hand, and whether and the extent to which its molecules collide with each other, on the other hand. Put differently, the idealization conveys in an especially dramatic way that for Boyle’s law to hold of a given gas, it is irrelevant whether collisions occur between the molecules in the gas. This is the type of ‘negative’ information about a phenomenon’s dependence relations that may be involved in understanding the phenomenon (see Sect. 3 ). Thus, on the noetic account, the derivation of Boyle’s law from idealized assumptions constitutes progress, even when a non-idealizing derivation is also available.

The standard derivation of Boyle’s law is but one example among many in which a set of theories containing an idealization provides more understanding than its de-idealized counterpart. It does this in virtue of revealing something about what the target phenomenon doesn’t depend on. Footnote 17 Here’s another example. Derivations of trajectories of planets around stars frequently assume that both the planets and the stars are point masses , i.e. extensionless particles with positive masses. Of course, we know that this is not just false, but impossible. This would be a problem if the assumption of point masses was meant to convey ‘positive’ information about what the planets’ trajectories do depend on; however, as a way of conveying ‘negative’ information about what these trajectories do not depend on, the assumption of something impossible serves as an especially vivid way to flag that the trajectories do not depend on the volumes of planets or stars. Thus, while including this idealization—this blatant falsehood—clearly doesn’t increase the truthlikeness of our theories, it does increase our understanding of the planets’ trajectories.

To sum up the discussion so far, then, the noetic account comes apart from the truthlikeness account in at least two ways. On the one hand, the noetic account counts as progressive episodes in which already-accepted theories are applied to increase our understanding of specific phenomena. On the other hand, the noetic account also counts as progressive episodes in which idealizations are introduced to convey what a target phenomenon does not depend on—even when non-idealized alternatives are available. In both cases, the noetic account expands the range of progressive episodes from what is counted as such by the truthlikeness account.

Are there also episodes that the truthlikeness account counts as progressive but the noetic account doesn’t? Such cases would have to involve increases in the truthlikeness of accepted theories that fail to increase our understanding of relevant phenomena. However, from the noetic account’s point of view, the point of proposing new theories in science is to increase our understanding in one way or another. Consequently, there should be very few if any cases in actual scientific practice of increasingly truthlike theories that fail to increase understanding in one way or another. Even theories that are far removed from empirical reality, such as string theory, contain a lot of information about dependencies (e.g. that a particle’s mass depends on the vibrational state of the corresponding string), and thus potentially provide us with great deal of understanding.

But although cases of increasing truthlikeness without increasing understanding will be rare in scientific practice, we can easily conceive of hypothetical cases. Consider entirely spurious correlations : statistical correlations between two or more phenomena that aren’t due to any dependence (e.g. causal) relation between those phenomena, or between these phenomena and some other phenomenon. For example, it is presumably entirely spurious that the average margarine consumption in the U.S. was highly correlated ( \(r=0.9926\) ) with divorce rates in the state of Maine in the years 2000–2009 (Vigen 2015 , pp. 18–20). The ‘theory’ that these two quantities are correlated is truthlike—indeed, fully true. So if this correlation were to be accepted, it would presumably constitute progress on the truthlikeness account. However, this ‘theory’ arguably couldn’t increase anyone’s understanding of either U.S. margarine consumption or Maine divorce rates, since it fails to tell us anything about what these quantities depend on, e.g. what causes or grounds them. Thus the acceptance of this claim would not constitute progress on the noetic account, regardless of how truthlike it is.

One might worry that the noetic account goes too far in discounting spurious correlations as non-progressive. Does this imply that searching for correlations is never a worthwhile scientific practice? Not at all. Although correlation is not causation—or any kind of dependence relation, for that matter—the former is normally a (fallible) guide to the latter. Thus, correlations often promote progress on the noetic account, e.g. through prompting more serious studies of the correlated variables where researchers control for possible confounders. However, this only holds when the correlations in question are not entirely spurious in the above sense, i.e. when the correlation is due to a dependence relationship between those phenomena or between them and a third phenomenon. So, on the noetic account, entirely spurious correlations do not even promote progress in the way that non-spurious correlations normally do, which explains why they seem so frivolous from a scientific point of view.

4.2 The epistemic account

Bird’s ( 2007 , 2016 ) epistemic account holds that scientific progress occurs precisely when scientists accumulate knowledge . The key term ‘knowledge’ is notoriously difficult to define, and Bird agrees with Williamson ( 2000 ) that it is unanalyzable and sui generis . Regardless, Bird follows epistemological orthodoxy in taking knowledge to require truth, belief, and epistemic justification. That is, one cannot know something unless it is true, one believes it, and one is justified in believing it. Two of these three requirements, viz. truth and belief, have analogues in the noetic and truthlikeness accounts in so far as both require progressive representations to be more accurate/truthlike, and that these representations are, or could be, in some sense accepted, adopted, or grasped by some agents. By contrast, no version of the requirement that progressive theories be epistemically justified is present in either the noetic account or the truthlikeness account. Footnote 18 Thus, although other components of the epistemic account might also be problematic, we shall focus on the justification requirement in what follows.

Before we begin, however, let me clarify that to reject a justification requirement on scientific progress is not tantamount to claiming that the practice of seeking confirmation for scientific claims plays no role in the progress of science. Far from it. The point of scientific confirmation is to separate, as far as possible, fact from fiction. For the noetic account, the relevant facts are those that can be used to construct models of dependence relations, which in turn constitute understanding. Without scientific confirmation, these models would generally be woefully inaccurate, and thus fail to constitute understanding. Moreover, even if by some fluke an unconfirmed model were to be sufficiently accurate to increase our understanding, in the absence of scientific confirmation we would not be able to tell it apart from alternative, inaccurate models. Consequently, an unconfirmed but accurate model would rarely, if ever, in fact be used by any of us to increase our understanding. For these reasons, scientific confirmation certainly plays a key role in the progress of science on the noetic account.

What really separates the epistemic from the noetic account (and from the truthlikeness account) Footnote 19 is whether epistemic justification, i.e. the type of justification that is required for knowledge, partly constitutes scientific progress. According to the epistemic account, a scientific theory or model that fails to be epistemically justified cannot constitutively contribute to scientific progress, because such a theory or model would fail to be known. Indeed, Bird argues for the epistemic account by appealing to actual and hypothetical cases in which scientists form unjustified, but nevertheless true, beliefs about scientific phenomena. In these cases, Bird claims that the epistemic account “accords with the verdict of intuition”, while not requiring justification for progress “conflicts with what we are intuitively inclined to say” (Bird 2007 , p. 66). In particular, Bird says that that it would not have been progressive for scientists to accept Alfred Wegener’s theory of continental drift when Wegener first proposed it in 1912, because the theory was not sufficiently justified at the time to count as knowledge. Although Bird targets the truthlikeness account specifically, his argument would apply also to the noetic account if Wegener’s theory would have been made publicly available in a way that made it possible for relevant members of society to increase their understanding of relevant phenomena, e.g. the lithosphere of the Earth.

Many commentators disagree with Bird’s intuitions about such cases (Rowbottom 2008 ; Cevolani and Tambolo 2013 ; Niiniluoto 2014 ; Dellsén 2016 ). Footnote 20 More importantly, it is unclear why (alleged) facts about what “we are intuitively inclined to say” should count for much at all in discussions of scientific progress. After all, as noted above, the question of scientific progress is unmistakably normative: it is not about the extension of a concept that we happen to possess, but about what types of cognitive changes in science ought to be pursued and incentivized (see Sect. 2 ). For the purposes of answering the latter type of question, we should arguably consult our reflective judgments rather than our untutored intuitions. Of course, the outcome of such reflections might be that we we may end up agreeing with what our previous selves were already inclined to say, i.e. with our original intuitions—but that, too, would be a reflective judgment.

With all this in mind, I turn now to presenting an objection to the claim that justification is necessary for scientific progress—and thus, by implication, to the epistemic account. Footnote 21 This objection appeals to a type of higher-order evidence , i.e. evidence about the epistemic character of some other, typically first-order, evidence (Christensen 2010 ; Kelly 2010 ). What is interesting about higher-order evidence is that, in many cases, it undermines or defeats the epistemic justification otherwise provided by first-order evidence. Footnote 22 In science, the first-order evidence is simply what we would usually call ‘scientific evidence’, the type of evidence that is systematically collected in science and published in scientific journals (e.g. observational data and experimental results). Thus higher-order evidence in science could potentially undermine or defeat the epistemic justification provided by ordinary, first-order scientific evidence. If so, this type of higher-order evidence in science would, in a roundabout way, prevent progress from occurring according to the epistemic account—even in cases where our theories/models are true/accurate.

Consider a form of higher-order evidence that should be particularly familiar to philosophers of science, viz. historical higher-order evidence. According to a general version of the pessimistic meta-induction (e.g., Poincaré 1952 ; Hesse 1976 ; Laudan 1981a ), most past theories (including many of the most successful ones) have turned out to be false by our current lights; hence, by enumerative induction, we have reason to believe that most of our current theories (including many of the most successful ones) will suffer the same fate. Note that this is an an argument that the supposed historical failures of scientific theories undermine or defeat the epistemic justification for current theories that would otherwise be provided by the ordinary, first-order scientific evidence in their favor. Thus the historical record is, according to the pessimistic meta-induction, a type of higher-order evidence against current theories being epistemically justified. In so far as the pessimistic meta-induction is successful, no such theories would be epistemically justified, regardless of how highly confirmed they are by ordinary first-order scientific evidence, because the historical higher-order evidence would prevent it from providing justification for current theories.

Admittedly, there are reasons to think that this general version of the pessimistic meta-induction greatly overstates the extent to which the historical record undermines the justification for current theories provided by the first-order evidence in their favor. Many of the central posits of past theories are preserved in current theories (e.g., Kitcher 1993 ; Psillos 1999 ; Chakravartty 2007 ), and current theories are arguably better confirmed by first-order scientific evidence than their past counterparts (Roush 2010 ; Fahrbach 2011 , 2017 ). So it is doubtful, at best, that the historical record supports the wholesale conclusion that current scientific theories are epistemically unjustified across the board. With that said, it seems undeniable that, at least in some cases, more local versions of the pessimistic meta-induction does indeed undermine the epistemic justification for scientific theories at various points in history (Ruhmkorff 2013 ; Asay 2019 )—including some episodes that are arguably paradigmatic of scientific progress.

Thus consider cases where, in a particular scientific domain or discipline D , scientists have in the past successively adopted theories \(T_1,...,T_{n-1}\) , none of which are even approximately true by our current lights. Consider a point in time where the most recent theory in D , \(T_n\) , has only recently been adopted. Then, even if \(T_n\) is at least as well confirmed by the first-order scientific evidence as any of its predecessors, the higher-order evidence against \(T_n\) could be sufficiently strong (e.g., because n is sufficiently high) to defeat the justification that would otherwise be conferred on \(T_n\) . Hence \(T_n\) cannot, at least not at this point, be known. But is it plausible that this historical fact about the previously adopted theories by itself prevents the adoption of \(T_n\) from contributing to scientific progress? Indeed, supposing that \(T_n\) is otherwise of the standard required for progress, e.g. in enabling us to increase our understanding of relevant phenomena, then isn’t the adoption of \(T_n\) all the more progressive given that previous theories in the same domain D were so far off track?

A historical case, familiar from debates about the pessimistic meta-induction (Stanford 2006 , pp. 51–140), may be used to illustrate the point. In the latter half of the 19th century, various theories were proposed by the most eminent biologists of the day to explain the mechanism by which biological traits are inherited from one generation to the next. Chief among these were Charles Darwin’s pangenesis theory , proposed in 1868; Francis Galton’s stirp theory , proposed in 1879; and August Weismann’s germ-plasm theory , proposed in 1892. Shortly thereafter, in 1902–1904, Walter Sutton and Theodor Boveri independently developed versions of the currently accepted chromosome theory , according to which chromosomes located in all dividing cells carry genetic information from parent to offspring. Assuming that the chromosome theory is indeed correct, the three earlier theories were all fundamentally mistaken, in that each posited some non-existent carrier of genetic material—‘gemmules’ for Darwin, ‘stirps’ for Galton, and ‘germ-plasm’ for Weismann. Now consider a point in time shortly after Sutton and Boveri’s theory was proposed, e.g. 1905. Did their theory contribute to scientific progress at that time?

According to the epistemic account, the answer must be ‘no’. The historical record of failed theorizing about heredity—i.e. the pangenesis, stirp, and germ-plasm theories of Darwin, Galton, and Weismann, respectively—indicated that this most recent theory would suffer the same miserable fate. Even if the first-order scientific evidence in favor of the Sutton-Boveri chromosome theory was already strong at the time, the fact that theorizing in this domain had turned up so many theories that were, by their lights at the time, mistaken, prevents this evidence from epistemically justifying the chromosome theory in the way it otherwise would have. It follows that scientists accepting or believing the theory would not qualify as knowledge, in which case the episode fails to constitute scientific progress on the epistemic account. Thus, whereas we might have thought that Sutton and Boveri’s chromosome theory was all the more progressive in virtue of replacing fundamentally mistaken theories, the epistemic account evidently delivers the opposite verdict that the episode did not constitute progress at all.

The noetic account offers a very different analysis of these types of cases. The chromosome theory accurately depicts the underlying causal mechanism of biological inheritance, which in turn allows us to increase our understanding of, among other things, actual inherited traits (such as the color of your eyes). Thus, as soon as the chromosome theory was made publicly available, primarily via Sutton’s publication of the theory in the recently established Biological Bulletin (Sutton 1902 , 1903 ), there was progress on the noetic account. The noetic account does not also require the theory to be epistemically justified in the sense required for knowledge. Consequently, the historical higher-order evidence which serves to undermine or defeat the justification for the chromosome theory does not in any way prevent it from contributing to progress on the noetic account. Hence the noetic account, in contrast to the epistemic account, straightforwardly counts this and similar episodes as constituting scientific progress.

To reiterate an earlier point, this does not imply that scientific evidence or confirmation is of no relevance to scientific progress on the noetic account. If the chromosome theory had not been supported by (first-order) scientific evidence, such as Boveri’s experiments with sea urchins and Sutton’s work on grasshoppers, the theory would probably not have been published at all, and it would certainly not have achieved the status that it later did. In this way, ordinary first-order scientific evidence is crucial to scientific progress on noetic account. What is not crucial—indeed, irrelevant—is whether there is some historical higher-order evidence available which would prevent this first-order scientific evidence from providing the type of epistemic justification for scientists’ beliefs in the chromosome theory that would make them constitute knowledge.

5 Challenges to the noetic account

In this section, I consider several challenges to the noetic account that aim to show that it is too narrow to accommodate the full range of cases that plausibly fall under scientific progress. The general worry here is that by identifying scientific progress with enabling increased understanding, rather than with some more general developments such as increased truthlikeness of accepted theories, we have excluded a variety of developments that ought to count as progressive. I will consider three specific versions of this worry, viz. that the noetic account is too narrow in virtue of (i) making metaphysical assumptions regarding dependence relations, (ii) excluding scientifically important classification schemes, and (iii) excluding discoveries of previously unknown phenomena. My contention will be that, ultimately, none of these charges hit home because the noetic account is not as narrow as one might have thought.

Let me first acknowledge, however, that there are two alternative strategies for responding to these challenges. The first is to concede that the noetic account is too narrow as it stands, and subsequently modify the account so as to incorporate other developments than those that enable increased understanding. There are many ways to do this. Most straightforwardly, one might combine elements of the noetic account with elements of alternative accounts, such as the truthlikeness account—and say, for example, that progress consists in enabling increased understanding or increased truthlikeness . Footnote 23 The obvious downside to this hybridization strategy is that it sacrifices the simplicity of the (non-hybird) noetic account. Ultimately, one may of course end up thinking that this is a sacrifice worth making. But we won’t know until we have thoroughly considered whether the noetic account is able to respond convincingly to the challenges (i)–(iii) without hybridization. So let’s see how far we can get with the (non-hybird) noetic account before we concede ground to its opponents and adopt a hybrid account instead.

A second alternative strategy is a revisionist one. Faced with the charge that the noetic account is too narrow, e.g. in virtue of counting classification schemes as non-progressive, one could argue that its narrowness is a virtue rather than a vice. The narrowness of an account of scientific progress is what gives it its critical bite—its potential for serving as the basis of philosophically informed decisions about which research projects to pursue (at all, or at the expense of others). Note, for example, that an account of scientific progress that accommodates all scientific developments as progressive can’t ever deliver the verdict that some projects are not worth pursuing at all, which is one of the purposes to which such an account would be put. Footnote 24 So, in some instances, the correct response to charges of narrowness might be to embrace it as a desirable feature of accounts of scientific progress. Although this type of revisionist strategy is indeed appropriate for some purported cases of scientific progress (see Sects. 5.2 and 5.3 ) I do not think it works as a general strategy since many of the apparently-excluded developments are very much worth pursuing.

5.1 Excess metaphysical baggage?

The first challenge that I will consider is that the noetic account appears to assume, in a way that the truthlikeness and epistemic accounts do not, that there are certain metaphysical relations in the world, e.g. causation and grounding, which our dependency models come to accurately represent to some degree in cases of scientific progress. But what if the world is metaphysically sparse, devoid of necessary connections between distinct existences, as per Hume’s dictum (Wilson 2010 )? What if the things we call ‘causation’ and ‘grounding’ are mere shadows of reality, e.g. regularities that we happen to notice in our experiences? If so, it might seem as if the noetic account would make scientific progress not just rare, but impossible. After all, there would be no dependence relations out there in the world for us to represent in such a way as to make scientific progress possible on the noetic account.

The short response to this challenge is that, appearances perhaps to the contrary, the noetic account is compatible with metaphysical outlooks that entirely reject necessary connections in nature. All that’s required for understanding is that there be some facts of the matter about how one thing depends on (e.g. is caused by) another. It does not matter whether these facts of the matter are ultimately facts about the fundamental fabric of reality, or whether they are instead reducible to or explained by other features of reality, such as regularities in our experiences, our human psychology, or our social practices. Thus, for example, one can easily pair the noetic account with a regularity theory of causation, such as Mackie’s ( 1974 ), on which causal relations are nothing over and above certain regularities in the events constituting the purported causes and effects. Footnote 25 Since such theories do not deny that—indeed explain how—some events cause others, they clearly don’t make it impossible to accurately represent causal relations.

As far as grounding is concerned, the situation is essentially similar although slightly more delicate. A complication comes from the fact that some authors use the term ‘grounding’ in a way that prejudges metaphysical questions, e.g. about the independent existence and fundamentality of the grounding relation itself, or about the grounding entity being more fundamental than what it grounds (e.g., Schaffer 2009 ; Raven 2016 ). For the purposes of this paper, I don’t mean for the notion of ‘grounding’ to carry any such metaphysical baggage. Rather, my use of the term is merely meant to refer to a type of non-synchronic relation that is analogous to causation, and that typically holds between a reduced object, state, or property, on the one hand, and its reductive base, on the other hand. Without some such notion, it seems to me that it would be hard to make sense of the way in which we understand the properties of water by reducing it to \(H_2O\) , for example. An accurate and relatively comprehensive dependency model ought to reflect the ways in which the various observable properties of water, e.g. its being liquid at room temperature, depend on its underlying chemical composition (and not vice versa).

But while we thus arguably need something like the notion of ‘ground’ to account for some types of understanding in science, we don’t need to make any metaphysically loaded assumptions about what it refers to (Dasgupta 2017 ). In particular, we need not posit the existence of any fundamental, primitive, or unified relation in the world to which the notion refers. Instead we can agree with ‘grounding skeptics’, who argue that grounding is to be identified with or reduced to other metaphysical dependence relations, such as type or token identity, supervenience, or determination (Wilson 2014 ; Koslicki 2015 ; Hofweber 2016 ), which may or may not themselves be reducible to something less metaphysically bloated. Alternatively, grounding may well turn out to be a form of non-diachronic causation (Wilson 2018 ), in which case reductive theories of the latter could arguably be applied to the former as well. Furthermore, a possibility left open by the noetic account is that the dependence relations normally called ‘grounding’ are largely due to mind-dependent psychological facts about what human beings happen to classify as explanatory rather than any sort of fundamental facts about reality (Norton and Miller 2019 ). In any case, it should be clear that the notion of ‘grounding’ to which I have cautiously appealed above carries no special metaphysical baggage beyond what is already needed to account for commonplace scientific reductions such as that between water and \(H_2O\) .

5.2 Non-progressive classification schemes?

Another challenge for the noetic account concerns classification schemes used in science, such as the periodic table of elements and the Linnaean system of biological classification. The challenge here is that, in contrast to ordinary physical theories, for example, it is less clear what information about dependence relations is conveyed by such classification schemes. Indeed, one might argue that in so far as such schemes tell us anything, they merely describe various properties of the classified entities in a particularly economical manner without ever taking a stand on the causes or grounds of these entities or their properties. So does the noetic account imply that developing classification schemes contributes nothing towards scientific progress?

I think not. To see why, let us start by noting that no plausible account of scientific progress should count all classification schemes as contributing to scientific progress. The purpose of any classification is to convey information in an efficient manner (Mill 1874 ; Mayr 1974 ). Indeed, all classification schemes convey some information or other—minimally, they convey that the elements in a given category satisfy the conditions for membership of that category. So the question is, what type of information must a given classification scheme convey in order for its adoption to count as progressive? Here different accounts of scientific progress clearly part ways, in so far as they count different types of information as progressive. Let us focus on the noetic account, against which the current challenge is directed. This account implies that progress-constituting classification schemes convey information about dependence relations, e.g. causal relations, that might hold between the classified entities or between those entities and other entities not classified in that scheme. In addition, the noetic account also envisions progress-promoting classification schemes, which would roughly be those that cause or raise the probability of enabling increased understanding at some later time.

In my view, it’s plausible that these are precisely the types of classification schemes that are found to be of value in scientific practice. To substantiate this claim, consider first the information contained in the periodic table of elements (see Scerri 2007 ). The classification of certain elements into groups serves to highlight the ways in which these elements’ atomic structure is responsible for their distinctive macro-level properties. For example, the periodic table nicely conveys the information that the six naturally occurring elements classified as ‘noble gases’ have similar chemical properties (e.g., being odorless, colorless, and generally unreactive) due to to having a similar atomic structure (viz., a full outer shell of valence electrons). Indeed, it is in virtue of latching onto dependence relations of this type that the periodic table enjoys such remarkable predictive success that Mendeleev was able to use it to predict the discovery of previously unknown elements with pre-specified chemical properties (Scerri and Worrall 2001 ). Far from counting the periodic table as non-progressive, then, the noetic account explains the value of the periodic table as conveying exactly the type of information that serves to increase understanding.

Let us also consider the Linnaean system of biological taxonomy, which classifies biological species hierarchically into higher taxa at different ranks (primarily genus , family , order , class , phylum , and kingdom ). Any discussion of this system is complicated by the fact that there is not agreement among biologists about which species should be grouped together at each rank (see Hull 1988 , pp. 158–276). The most widely accepted view, cladism (e.g., Hennig 1966 ), holds that biological classification should be based on recency of common descent and thus reflect the evolutionary relationships between different species. So if two species evolved from a common ancestor, from which a third species did not evolve, then cladism implies that the two aforementioned species should at some rank be classified together in a way that excludes the third. For example, birds and crocodiles share a common ancestor that is not an ancestor of lizards, so cladism implies that birds and crocodiles should at some rank be grouped together in way that excludes lizards (Sober 2000 , pp. 165–166). Since a cladistic classification scheme is thus explicitly designed to reflect causal relationships between (current and past) species, it conveys understanding in a straightforward manner. Thus the development of a cladistic taxonomy clearly counts as progressive on the noetic account.

What if cladism is rejected, despite its popularity? Even if we think cladism is correct, we may want our account of scientific progress to be consistent not just with our preferred view of biological classification, but also with other views that are taken seriously by working biologists. Here I cannot consider all alternatives to cladism, but let me nevertheless briefly consider the alternative that stands in starkest contrast with cladism, viz. phenetics (e.g., Sneath and Sokal 1973 ). In a phenetic taxonomy, species are grouped together in higher taxa based on ‘overall similarity’, regardless of how they are evolutionarily related. For example, since lizards and crocodiles are arguably more similar to each other than either of them is to birds, pheneticists typically hold that lizards and crocodiles should be grouped together in a way that excludes birds. The underlying idea behind phenetics is that ‘overall similarity’, e.g. in observable traits, is a more objective or theory-neutral basis for biological classification than evolutionary ancestry. This might seem to go against the noetic account, in so far as phenetic taxonomies fail to directly convey any information about causal relationships between species. Footnote 26

However, things will not seem so straightforward once we consider the main motivation for developing phenetic taxonomies. Prominent pheneticists, such as Sneath and Sokal ( 1973 ), were motivated not by a desire to avoid causal relationships between species in biological theorizing. On the contrary, they maintained that a phenetic taxonomy would be better suited than a cladistic one as a theoretically neutral basis for making inferences about evolutionary relationships. On the pheneticists’ view, developing a cladistic taxonomy risks begging the very question that a biological classification scheme ought to help us answer, viz. how different species are evolutionarily related. So the phenetic point of view is that biological classification should contain the data from which evolutionary relationships are inferred, as opposed to containing the conclusions of such inferences (see Hull 1988 , pp. 117–120). Put differently, the main point of a phenetic taxonomy is to promote the discovery of evolutionary relationships, which are causal relations. So while developing a phenetic taxonomy would admittedly not constitute much scientific progress on the noetic account, it would certainly—indeed, is specifically designed to—promote progress. Footnote 27

To sum up, then, the noetic account provides a framework for making sense of the debate from both sides of the cladism-phenetics divide. Cladists hold that biological classification ought to reflect the underlying causal relationships between species, so that a taxonomy directly conveys information that increases our understanding of biological species. Successfully developing cladistic classification schemes therefore constitutes scientific progress on the noetic account. By contrast, pheneticists hold that biological classification ought to reflect the current ‘overall similarities’ between species, regardless of ancestry. But the point of such classification is to help evaluate, in a supposedly theory-neutral way, hypotheses about the causal relationships between species. Successfully developing phenetic classification schemes therefore promotes scientific progress on the noetic account. Either way, the noetic account can effortlessly explain the scientific value of biological taxonomies.

With all of that said, there will of course be some—indeed, infinitely many—classification schemes that the noetic account counts as more-or-less worthless as far as scientific progress is concerned, i.e. as neither constituting nor promoting any noteworthy degree of progress. If the noetic account is correct, these will inevitably be a bit silly. For example, consider a classification of all objects in the universe into those that are less than 10 m from the tip of my nose in any direction, and those that are outside of this sphere. Presumably, this classification conveys little or no information about dependence relations, and promotes little or no discoveries of them either. Hence it counts as relatively useless for the purposes of scientific progress on the noetic account (and rightly so). Generally, then, whether a given classification scheme counts as constituting or promoting progress, or as doing neither, depends on the classification scheme in question, and the use to which it is put. So, on the noetic account, the relationship between progress and classification will have to be evaluated on a case-by-case basis. I hope it’s clear, however, that the noetic account does plausibly count as progressive two of the most prominent classification schemes in current science, viz. the periodic table and Linnaean taxonomy.

5.3 Non-progressive existential discoveries?

I turn now to a final challenge to the noetic account. Roughly, the challenge is to accommodate discoveries of new phenomena, such as previously unknown biological species, new physical effects, and archeological findings. The worry is that such discoveries might not enable anyone to increase their understanding since they don’t necessarily contain information about dependence relations. A closely related worry is that the noetic account might not count theoretical postulations of (real) entities as progressive, again because the mere posit that an entity exists doesn’t necessarily contain information about dependence relations. What unifies these worries is the concern that the noetic account does not account for progress through what we may call existential discoveries , viz. empirical or theoretical uncoverings of previously unknown entities. Footnote 28

The first thing to note about this challenge is that it is clearly not the case that all existential discoveries are scientifically progressive—or, if they are, some are much less progressive than others. Bird ( 2007 ) imagines researchers who count, measure, and classify billions of grains of sand on a particular beach. As Bird admits, this “adds little to scientific progress” (Bird 2007 , p. 84). So, a fortiori , had the researchers ‘discovered’ only a particular grain of sand, this adds even less—if indeed anything at all—to scientific progress. To take an even more extreme example, consider Charles Dawson’s discovery in 1921 of the skull fragments that became known as the ‘Piltdown man’. The composition of these fragments, with canine teeth but a human-like skull, suggested that they came from an early humanoid that might serve as the ‘missing link’ in the evolution of humans from other primates. However, this discovery was not progressive (indeed, perhaps significantly regressive or progress-demoting) since the skull fragments turned out to be have been fraudulently put together in an effort to deceive archeologists—probably by Dawson himself (Groote et al. 2016 ). An account of scientific progress that treats ‘discoveries’ like this as on a par with the discoveries of, for example, quarks and platypuses, would clearly be inadequate. So the challenge for the noetic account, or indeed for any account of scientific progress, is not to show how every existential discovery adds (significantly, or at all) to scientific progress; rather, it is to show how some select group of existential discoveries do so and that others don’t (or not as much).

So what would make an existential discovery progressive according to the noetic account? Well, first of all, the discovery of a new entity often directly conveys information about dependence relations. For example, the postulation and subsequent detection of the up and down quarks directly increased our understanding of neutrons and protons, because the latter are constituted by, and thus depend on, the former. This is a case in which the discovered entities (up and down quarks) stand in a dependence relation to already known entities (neutrons and protons) that we are hoping to understand better. There are also cases in which the discovery of an entity indirectly reveals something about dependence relations between other entities. For example, the discovery of the platypus, the first egg-laying mammal to be discovered by Europeans, revealed (to Europeans) that the distinctively mammalian properties of having mammary glands and fur/hair, for example, are not caused by the same speciation event as those that cause most mammals to give birth to live offspring. Put differently, the discovery of the platypus conveys information about the evolutionary lineage of mammals, which of course is a type of information about dependence relations between mammalian species and their ancestral species.

In these examples, existential discoveries convey information about dependence relations, and thus constitute scientific progress on the noetic account. In other cases, such discoveries only or primarily contribute to progress by promoting its occurrence at a later time. The most obvious, and perhaps most common, way in which they might do so is through being evidence for claims about dependence relations which in turn increase our understanding. For example, consider Brownian motion, the random fluctuation of particles suspended in liquids or gases, which was discovered already in 1827 by the botanist Robert Brown. Since Brown merely observed the phenomenon, and did not explain it in any way, his discovery conveyed no understanding at the time, and thus didn’t constitute progress on the noetic account. However, Brown’s discovery promoted progress in so far as it caused Albert Einstein ( 1956 ) to provide an elegant explanation of Brownian motion based on the kinetic theory of heat and the atomic theory of matter. Thus the discovery of Brownian motion promoted progress, not just on Brownian motion itself but also on the nature of heat and matter, in so far as Brownian motion served as evidence for the kinetic and atomic theories of these respective phenomena.

Finally, even when existential discoveries do not constitute progress by conveying information about dependence relations, and even when they don’t promote progress through being evidence for claims about dependence relations, there is still a third way in which existential discoveries may facilitate progress on the noetic account. Obviously, one cannot understand something that hasn’t been discovered. So when we discover an entity or phenomenon X , we are always enabling progress with regard to X on the noetic account (where ‘enabling’ is a special case of promotion). Footnote 29 Consider, for example, the common but poorly-understood disease variously known as myalgic encephalomyelitis (ME) or chronic fatigue syndrome (CFS). Although the underlying causes of ME/CFS are still quite unclear, its status as a distinct disease has been widely acknowledged in recent years, e.g. by the Center for Disease Control and Prevention (CDC) in the United States (Fukuda 1994 ). This recent discovery—or, if you prefer, postulation—of ME/CFS is a prerequisite for an understanding of the disease, e.g. through research into its possible neurological and epidemiological causes. Footnote 30

I thus conclude that existential discoveries may count as progressive in three distinct ways. Many such discoveries, e.g. of the up and down quarks, constitute progress, since they reveal information about what other more familiar phenomena, e.g. neutrons and protons, depend on. Other existential discoveries primarily serve to promote progress through constituting evidence for claims about dependence relations, e.g. in the way that Brownian motion led to our current understanding of heat and matter. Finally, all existential discoveries enable progress on the discovered phenomenon itself; thus, in so far as we care to make progress on that phenomenon, such discoveries automatically promote progress.

6 Conclusion

What is scientific progress? In this paper, I have sought to address this question in two ways. On the one hand, I have precisified the question itself by introducing various distinctions, such as that between constituting and promoting progress, and between progress-on- X and overall progress. Thus precisified, I have suggested that the most fundamental question of scientific progress concerns what type of cognitive change with respect to a topic X constitutes a scientific improvement (to a greater or lesser extent) with respect to X . On the other hand, I have advanced and defended a revised version of the noetic account of scientific progress. A cognitive change constitutes a scientific improvement on X just in case it makes scientific results publicly available so as to enable relevant members of society, including scientists themselves, to increase their understanding of X . I have sought to show how this account can explain various features of scientific practice that are puzzling or inexplicable on alternative accounts, such as why idealized theories are not always abandoned when more accurate alternative become available, why discovering entirely spurious correlations plays a minimal role in scientific practice, and why higher-order evidence (e.g. from pessimistic meta-inductions) is not an obstacle to scientific progress. Finally, I have defended the noetic account against several challenges that accuse the noetic account of being too narrow to accommodate the full range of cases of scientific progress.

This type of philosophical methodology dates back to Carnap ( 1962 ), but has recently been revived under various labels such as ‘conceptual engineering’ (Cappelen 2018 ), ‘conceptual ethics’ (Burgess and Plunkett 2013a , b ), and ‘ameliorative analysis’ (Haslanger 2013 ).

Hence, one can test for whether (one judges that) a given scientific improvement constitutes or promotes progress with a thought experiment in which one imagines that the episode in question either has no effects whatsoever or that its effects clearly do not constitute progress. If the episode is still an improvement, it constitutes progress; if not, it promotes progress.

See Rowbottom ( 2015 , p. 104) for another type of example of something that could promote progress without constituting it in certain circumstances, viz. flatly false beliefs that lead to future progress.

Consequently, an episode may also promote more and less progress, corresponding to how much progress it leads to or probabilifies. The latter can be measured as the probability-weighted average of degrees of progress in all epistemically possible scenarios, mirroring the definition of expected utility in standard decision theory.

For example, Bird ( 2007 , p. 84) explicitly declines to give an account of degrees (or ‘rates’) of progress on the grounds that “it is a much more difficult question” than what determines outright progress.

In my view, the most plausible approach to aggregating progress on different topics in this way into a notion of overall progress would take the weighted sum of progress on each topic, where the weights assigned to progress on each topic are determined by the scientific significance, in Kitcher’s ( 2001 , 2011 ) sense, of the topics in question. Thus if making progress on the evolution of human beings is of greater scientific significance than making progress on the evolution of sea urchins, for example, then the former would contribute more to overall progress than the latter.

For arguments that it is superior, see Dellsén ( 2020 ). Rival accounts are provided by, among others, Strevens ( 2013 ), Wilkenfeld ( 2013 ), Grimm ( 2014 ), Bengson ( 2015 ), Elgin ( 2017 ), de Regt ( 2017 ), and Khalifa ( 2017 ), although many of these accounts have important similarities with mine.

That said, what I am calling causation and grounding may well be species of the same genus (Wilson 2018 ).

Some earlier discussions of scientific progress (e.g., Bird 2007 ; Dellsén 2016 ) appear to assume that progress is determined by changes in the attitudes of individual scientists. More recently, Bird ( 2019 ) (see also Ross 2020 and Harris 2021 ) has argued that progress is determined by the collective attitudes of scientific communities, where the latter are not neatly reducible to individual attitudes. Relatedly, Niiniluoto ( 2017 , p. 2399) refers to it as a “hidden assumption” that “the primary application of the notion of scientific progress concerns successive theories which have been accepted by the scientific community”.

A great deal more could be said about what constitutes ‘publicly available scientific information’, who counts as ‘relevant members’ of society, and how the relevant information ‘makes it possible’ for them to increase their understanding. However, since nothing in what follows depends on how the noetic account is spelled out in these respects, I’ll leave that for another occasion (see Dellsén ms).

Indeed, we might even want to say that the former is, in some general sense, “more progressive” than the latter. I have no quarrel with this way of speaking as long as we then keep firmly in mind that accounts of scientific progress (including the noetic account) are not currently meant to directly explicate this general sense of the term.

Given the limited space of a journal article, my discussions of these accounts will inevitably be quite brief. I also lack the space to discuss the functional account (Kuhn 1970 ; Laudan 1977 , 1981b ; Shan 2019 ), non-standard versions of the truthlikeness and epistemic accounts (e.g., Aronson et al. 1994 ; Barnes 1991 ; Northcott 2013 ; Park 2017 ), hybrid accounts (e.g., Bangu 2015 ; Goebel 2019 ), and various other accounts that have been proposed (e.g., Douglas 2014 ; Rowbottom 2019 ; Saatsi 2019 ).

This might seem to suggest that the noetic account comes close to characterizing scientific progress as increasing legisimilitude , i.e. closeness to (true) laws of nature (see, e.g., Cohen 1980 ; Niiniluoto 1983 ). However, I would resist the characterization of dependence relations as laws of nature, since (i) many dependence relations are much too specific and fragile to count as laws (see Woodward 2003 , pp. 239–314), and (ii) some of the relevant dependence relations may be mathematical or logical rather than nomological (see Baron et al. 2017 ).

On many accounts of scientific modelling, this is a general difference between models and theories. See, e.g., Cartwright ( 1983 ), Giere ( 1988 ), Bailer-Jones ( 2013 ), and Weisberg ( 2013 ).

In the latter case, keeping the idealized theory would not strictly speaking constitute progress so much as the alternative course of action, i.e. discarding the theory, would constitute regress. For the sake of simplicity, I have presented accounts of scientific progress as focusing on progressive episodes, but such accounts must also account for the opposite of progress, i.e. regress, and the lack of either progress or regress, i.e. what we might call ‘flatlining’.

In Weisberg’s ( 2007 , p. 642) terminology, these fall under ‘minimalist idealizations’.

This is not to say that idealizations are the only way to convey information about what a target phenomenon doesn’t depend on. It may just be a particularly efficient way of doing so, especially in cases where one is also seeking to convey information about what the target phenomenon does depend on (and how exactly it depends on those factors).

Note that the notion of understanding with which the noetic account operates (see Sect. 3.1 ) does not require epistemic justification. This is in line with arguments that understanding differs from knowledge in this respect (Hills 2016 ; Dellsén 2017 ).

I will focus on contrasting the epistemic and noetic accounts in what follows, but my criticism of the epistemic account should also be congenial to those who favor the truthlikeness account.

Mizrahi and Buckwalter ( 2014 ) investigated laypeople’s intuitions about the relationship between progress and justification. As Rowbottom ( 2015 , p. 103) points out, the study appears to go against Bird’s contention the intuitive concept of scientific progress requires justification.

See also Dellsén (forthcoming), in which this objection is discussed alongside two other objections to the epistemic account’s justification requirement on scientific progress.

The type of higher-order evidence most widely discussed in epistemology is (recognized) peer disagreement , i.e. situations in which one becomes aware that someone who is equally competent and equally well informed about some issue has formed a contrary belief to one’s own (see, e.g., Christensen 2007 ).

Although this would require one to find a way to exclude the increases in truthlikeness that I have argued are non-progressive, such as spurious correlations (see Sect. 4.1 ).

As emphasized in Sect. 2 , accounts of scientific progress are meant to help us evaluate rather than merely describe scientific developments.

Alternatively, one may adopt an agency theory of causation along the lines of Menzies and Price ( 1993 ), according to which causation is ultimately a ‘secondary quality’, due in part to (non-causal) features of the world and in part to (non-causal) features of ourselves.

For the sake of the argument below, I will assume that phenetic taxonomies do not directly convey any information about causal relationships. In fact, however, one could argue that most if not all such taxonomies do contain causal information, e.g. in that the species that are grouped together at some rank will have similar causal properties. Although this would be consistent with my argument below, I will not pursue this line of defense since I think the main purpose of a phenetic taxonomy is to help us get at the very same type of causal information that cladistic taxonomies aim to describe.

Recall that to count a development as promoting rather than constituting progress is not necessarily to downgrade its overall importance for scientific progress, since a merely progress-promoting development might lead to more progress than a progress-constituting development constitutes.

It is worth noting that rival accounts of scientific progress, such as the truthlikeness and epistemic accounts, do not seem to have any trouble counting existential discoveries as progressive. After all, the addition of a sufficiently truthlike, and/or known, existential statement would presumably increase the overall truthlikeness of accepted theories, and/or add to the stock of accumulated knowledge.

More precisely, ‘enabling’ may be thought of as a subspecies of promotion that makes progress more probable by raising its probability up from 0; whereas promoting generally can raise the probability of progress up from any probability less than 1. (An alternative approach is to sharply distinguish enabling conditions from causes (see, e.g., Lombard 1990 ), in which case enabling progress might be thought of as distinct from promoting progress.)

Does this mean that all existential discoveries enable scientific progress on the noetic account (or indeed on any account)? Not quite. It is true that, for any phenomenon X , the discovery of X enables progress on X . However, it does not follow that the discovery of X is overall progressive, since we might place no significance whatsoever on making progress-on- X . (See the discussion of Kitcher’s notion of significance in footnote 6.) This is why, I submit, we wouldn’t count the discovery of a random grain of sand on a beach as adding much to progress, not even in the sense of merely enabling progress. For although such a discovery does make it possible to make progress on that particular grain of sand, we simply don’t place any significance on making progress on such a trivial phenomenon.

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Funding was provided by Icelandic Centre for Research (Grant No. 195617-051). Many thanks to Chris Dorst, James Norton, Elmar Unnsteinsson, and two anonymous reviewers for very helpful comments on earlier drafts of this paper.

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Dellsén, F. Understanding scientific progress: the noetic account. Synthese 199 , 11249–11278 (2021). https://doi.org/10.1007/s11229-021-03289-z

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Scientific Progress

Science is often distinguished from other domains of human culture by its progressive nature: in contrast to art, religion, philosophy, morality, and politics, there exist clear standards or normative criteria for identifying improvements and advances in science. For example, the historian of science George Sarton argued that “the acquisition and systematization of positive knowledge are the only human activities which are truly cumulative and progressive,” and “progress has no definite and unquestionable meaning in other fields than the field of science” (Sarton 1936). However, the traditional cumulative view of scientific knowledge was effectively challenged by many philosophers of science in the 1960s and the 1970s, and thereby the notion of progress was also questioned in the field of science. Debates on the normative concept of progress are at the same time concerned with axiological questions about the aims and goals of science. The task of philosophical analysis is to consider alternative answers to the question: What is meant by progress in science? This conceptual question can then be complemented by the methodological question: How can we recognize progressive developments in science? Relative to a definition of progress and an account of its best indicators, one may then study the factual question: To what extent, and in which respects, is science progressive?

1. The Study of Scientific Change

• 2.The Concept of Progress

3. Theories of Scientific Progress

4. is science progressive, bibliography, other internet resources, related entries.

The idea that science is a collective enterprise of researchers in successive generations is characteristic of the Modern Age (Nisbet 1980). Classical empiricists (Francis Bacon) and rationalists (René Descartes) of the seventeenth century urged that the use of proper methods of inquiry guarantees the discovery and justification of new truths. This cumulative view of scientific progress was an important ingredient in the optimism of the eighteenth century Enlightenment, and it was incorporated in the 1830s in Auguste Comte's program of positivism: by accumulating empirically certified truths science also promotes progress in society. Other influential trends in the nineteenth century were the Romantic vision of organic growth in culture, Hegel's dynamic account of historical change, and the theory of evolution. They all inspired epistemological views (e.g., among Marxists and pragmatists) which regarded human knowledge as a process. Philosopher-scientists with an interest in the history of science (William Whewell, Charles Peirce, Ernst Mach, Pierre Duhem) gave interesting analyses of some aspects of scientific change.

In the early twentieth century, analytic philosophers of science started to apply modern logic to the study of science. Their main focus was the structure of scientific theories and patterns of inference (Suppe 1977). This “synchronic” investigation of the “finished products” of scientific activities was questioned by philosophers who wished to pay serious attention to the “diachronic” study of scientific change. Among these contributions one can mention N.R. Hanson's Patterns of Discovery (1958), Karl Popper's The Logic of Scientific Discovery (1959) and Conjectures and Refutations (1963), Thomas Kuhn's The Structure of Scientific Revolutions (1962), Paul Feyerabend's incommensurability thesis (Feyerabend 1962), Imre Lakatos' methodology of scientific research programmes (Lakatos and Musgrave 1970), and Larry Laudan's Progress and Its Problems (1977). Darwinist models of evolutionary epistemology were advocated by Popper's Objective Knowledge: An Evolutionary Approach (1972) and Stephen Toulmin's Human Understanding (1972). These works challenged the received view about the development of scientific knowledge and rationality. Popper's falsificationism, Kuhn's account of scientific revolutions, and Feyerabend's thesis of meaning variance shared the view that science does not grow simply by accumulating new established truths upon old ones. Except perhaps during periods of Kuhnian normal science, theory change is not cumulative or continuous: the earlier results of science will be rejected, replaced, and reinterpreted by new theories and conceptual frameworks. Popper and Kuhn differed, however, in their definitions of progress: the former appealed to the idea that successive theories may approach towards the truth, while the latter characterized progress in terms of the problem-solving capacity of theories.

Since the mid-1970s, a great number of philosophical works have been published on the topics of change, development, and progress in science (Harré 1975; Stegmüller 1976; Howson 1976; Rescher 1978; Radnitzky and Andersson 1978, 1979; Niiniluoto and Tuomela 1979; Dilworth 1981; Smith 1981; Hacking 1981; Schäfer 1983; Niiniluoto 1984; Laudan 1984a; Rescher 1984; Pitt 1985; Radnitzky and Bartley 1987; Callebaut and Pinxten 1987; Balzer et al. 1987; Hull 1988; Gavroglu et al. 1989; Kitcher 1993; Pera 1994). These studies have also led to many important novelties being added to the toolbox of philosophers of science. One of them is the systematic study of inter-theory relations, such as reduction (Balzer et al. 1984; Pearce 1987; Balzer 2000; Jonkisz 2000), correspondence (Krajewski 1977; Nowak 1980; Pearce and Rantala 1984; Niiniluoto 1999; Nowakowa and Nowak 2000; Rantala 2002), and belief revision (Gärdenfors, 1988; Aliseda, 2006). Another was the recognition that, besides individual statements and theories, there is also a need to consider temporally developing units of scientific activity and achievement: Kuhn's paradigm-directed normal science, Lakatos' research programme, Laudan's research tradition, Wolfgang Stegmüller's (1976) dynamic theory evolution, Philip Kitcher's (1993) consensus practice. A new tool that is employed in many defenses of realist views of scientific progress (Niiniluoto 1980, 1984, 1999; Aronson, Harré, and Way 1994; Kuipers 2000) is the notion of truthlikeness or verisimilitude (Popper 1963, 1970; Niiniluoto 1987).

New interest about the development of science promoted close co-operation between historians and philosophers of science. For example, case studies of historical examples (e.g., the replacement of Newton's classical mechanics by quantum theory and theory of relativity) have inspired many philosophical treatments of scientific revolutions. Further interesting material for philosophical discussions about scientific progress is provided by quantitative approaches in the study of the growth of scientific publications (de Solla Price 1963; Rescher 1978) and science indicators (Elkana et al . 1978). Sociologists of science have studied the dynamic interaction between the scientific community and other social institutions. One of their favorite topics has been the emergence of new scientific specialties (Mulkay 1975; Niiniluoto 1995b). Sociologists are also concerned with the pragmatic problem of progress: what is the best way of organizing research activities in order to promote scientific advance. In this way, models of scientific change turn out to be relevant to issues of science policy (Böhme 1977; Schäfer 1983; Niiniluoto 1984).

2. The Concept of Progress

2.1 aspects of scientific progress.

Science is a multi-layered complex system involving a community of scientists engaged in research using scientific methods in order to produce new knowledge. Thus, the notion of science may refer to a social institution, the researchers, the research process, the method of inquiry, and scientific knowledge. The concept of progress can be defined relative to each of these aspects of science. Hence, different types of progress can be distinguished relative to science: economical (the increased funding of scientific research), professional (the rising status of the scientists and their academic institutions in the society), educational (the increased skill and expertise of the scientists), methodical (the invention of new methods of research, the refinement of scientific instruments), and cognitive (increase or advancement of scientific knowledge). These types of progress have to be conceptually distinguished from advances in other human activities, even though it may turn out that scientific progress has at least some factual connections with technological progress (increased effectiveness of tools and techniques) and social progress (economic prosperity, quality of life, justice in society).

All of these aspects of scientific progress may involve different considerations, so that there is no single concept that would cover all of them. For our purposes, it is appropriate here to concentrate only on cognitive progress, i.e., to give an account of advances of science in terms of its success in knowledge-seeking.

2.2 Progress vs. Development

“Progress” is an axiological or a normative concept, which should be distinguished from such neutral descriptive terms as “change” and “development” (Niiniluoto 1995a). In general, to say that a step from stage A to stage B constitutes progress means that B is an improvement over A in some respect, i.e., B is better than A relative to some standards or criteria. In science, it is a normative demand that all contributions to research should yield some cognitive profit, and their success in this respect can be assessed before publication by referees (peer review) and after publication by colleagues. Hence, the theory of scientific progress is not merely a descriptive account of the patterns of developments that science has in fact followed. Rather, it should give a specification of the values or aims that can be used as the constitutive criteria for “good science.”

The “naturalist” program in science studies suggests that normative questions in the philosophy of science can be reduced to historical and sociological investigations of the actual practice of science. In this spirit, Laudan has defended the project of testing philosophical models of scientific change by the history of science: such models, which are “often couched in normative language,” can be recast “into declarative statements about how science does behave” (Laudan et al. 1986; Donovan et al. 1988). It may be the case that most scientific work, at least the best science of each age, is also good science. But it is also evident that scientists often have different opinions about the criteria of good science, and rival researchers and schools make different choices in their preference of theories and research programs. Therefore, it can be argued against the naturalists that progress should not be defined by the actual developments of science: the definition of progress should give us a normative standard for appraising the choices that the scientific communities have made, could have made, are just now making, and will make in the future. The task of finding and defending such standards is a genuinely philosophical one which can be enlightened by history and sociology but which cannot be reduced to empirical studies of science.

2.3 Progress, Quality, Impact

For many goal-directed activities it is important to distinguish between quality and progress . Quality is primarily an activity-oriented concept, concerning the skill and competence in the performance of some task. Progress is a result-oriented concept, concerning the success of a product relative to some goal. All acceptable work in science has to fulfill certain standards of quality. But it seems that there are no necessary connections between quality and progress in science. Sometimes very well-qualified research projects fail to produce important new results, while less competent but more lucky works lead to success. Nevertheless, the skillful use of the methods of science will make progress highly probable. Hence, the best practical strategy in promoting scientific progress is to support high-quality research.

Following the pioneering work of Derek de Solla Price (1963) in “scientometrics,” quantitative science indicators have been proposed as measures of scientific activity (Elkana et al . 1978). For example, output measures like publication counts are measures of scholarly achievement, but it is problematic whether such a crude measure is sufficient to indicate quality (cf. Chotkowski La Follette 1982). The number of articles in refereed journals is an indicator of the quality of their author, but it is clear that this indicator cannot yet define what progress means, since publications may contribute different amounts to the advance of scientific knowledge. “Rousseau's Law” proposed by Nicholas Rescher (1978) marks off a certain part of the total number of publications as “important” or “first-rate,” but this is merely an alleged statistical regularity.

Another example of a science indicator, citation index , is an indicator for the “impact” of a publication and for the “visibility” of its author within the scientific community. Martin and Irvine (1983) suggest that the concept of scientific progress should be linked to the notion of impact , i.e., the actual influence of research to the surrounding scientific activities at a given time. It is no doubt correct that one cannot advance scientific knowledge without influencing the epistemic state of the scientific community. But the impact of a publication as such only shows that it has successfully “moved” the scientific community in some direction. If science is goal-directed, then we must acknowledge that movement in the wrong direction does not constitute progress (Niiniluoto 1984).

The failure of science indicators to function as definitions of scientific progress is due to the fact that they do not take into account the semantic content of scientific publications. To determine whether a work W gives a contribution to scientific progress, we have to specify what W says (alternatively: what problems W solves) and then relate this content of W to the knowledge situation of the scientific community at the time of the publication of W . For the same reason, research assessment exercises may use science indicators as tools, but ultimately they have to rely on the judgment of peers who have substantial knowledge in the field.

2.4 Progress and Goals

Progress is a goal-relative concept. But even when we consider science as a knowledge-seeking cognitive enterprise, there is no reason to assume that the goal of science is one-dimensional. In contrast, as Isaac Levi's classic Gambling With Truth (1967) argued, the cognitive aim of scientific inquiry has to be defined as a weighted combination of several different, and even conflicting, epistemic utilities . As we shall see in Section 3, alternative theories of scientific progress can be understood as specifications of such epistemic utilities. For example, they might include truth and information (Levi 1967; see also Popper 1959, 1963) or explanatory and predictive power (Hempel 1965). Kuhn's (1977) list of the values of science includes accuracy, consistency, scope, simplicity, and fruitfulness.

A goal may be accessible in the sense that it can be reached in a finite number of steps in a finite time. A goal is utopian if it cannot be reached or even approached. Thus, utopian goals cannot be rationally pursued, since no progress can be made in an attempt to reach them. Walking to the moon is a utopian task in this sense. However, not all inaccessible goals are utopian: an unreachable goal, such as being morally perfect, can function as a regulative principle in Kant's sense, if it guides our behavior so that we are able to make progress towards it.

The classical sceptic argument against science, repeated by Laudan (1984a), is that knowing the truth is a utopian task. Kant's answer to this argument was to regard truth as a regulative principle for science. Charles S. Peirce, the founder of American pragmatism, argued that the access to the truth as the ideal limit of scientific inquiry is “destined” or guaranteed in an “indefinite” community of investigators (cf. Niiniluoto 1980, 1984). Almeder's (1983) interpretation of Peirce's view of scientific progress is that there is only a finite number of scientific problems and they will all be solved in a finite time. However, there does not seem to be any reason to think that truth is generally accessible in this strong sense. Therefore, the crucial question is whether it is possible to make rational appraisals that we have made progress in the direction of the truth (see Section 3.4).

A goal is effectively recognizable if there are routine or mechanical tests for showing that the goal has been reached or approached. If the defining criteria of progress are not recognizable in this strong sense, we have to distinguish true or real progress from our perceptions or estimations of progress . In other words, claims of the form ‘The step from stage A to stage B is progressive’ have to be distinguished from our appraisals of the form ‘The step from stage A to stage B seems progressive on the available evidence’. The latter appraisals, as our own judgments, are recognizable, but the former claims may be correct without our knowing it. Characteristics and measures that help us to make such appraisals are then indicators of progress .

Laudan requires that a rational goal for science should be accessible and effectively recognizable (Laudan 1977, 1984a). This requirement, which he uses to rule out truth as a goal of science, is very strong. The demands of rationality cannot dictate that a goal has to be given up, if there are reasonable indicators of progress towards it.

A goal may be backward-looking or forward-looking : it may refer to the starting point or to the destination point of an activity. If my aim is to travel as far from home as possible, my success is measured by my distance from Helsinki. If I wish to become ever better and better piano player, my improvement can be assessed relative to my earlier stages, not to any ideal Perfect Pianist. But if I want to travel to San Francisco, my progress is a function of my distance from the destination. Only in the special case, where there is only one way from A to B , the backward-looking and the forward-looking criteria (i.e., distance from A and distance to B ) determine each other.

Kuhn and Stegmüller were advocating backward-looking criteria of progress. In arguing against the view that “the proper measure of scientific achievement is the extent to which it brings us closer to” the ultimate goal of “one full, objective true account of nature,” Kuhn suggested that we should “learn to substitute evolution-from-what-we-know for evolution-toward-what-we-wish-to-know” (Kuhn 1970, p. 171). In the same spirit, Stegmüller (1976) argued that we should reject all variants of “a teleological metaphysics” defining progress in terms of “coming closer and closer to the truth.”

A compromise between forward-looking and backward-looking criteria can be proposed in the following way. If science is viewed as a knowledge-seeking activity, it is natural to define real progress in forward-looking terms: the cognitive aim of science is to know something that is still unknown, and our real progress depends on our distance from this destination. But, as this goal is unknown to us, our estimates or perceptions of progress have to be based on backward-looking evidential considerations. This kind of view of the aims of science does not presuppose the existence of one unique ultimate goal. To use Levi's words, our goals may be “myopic” rather than “messianic” (Levi 1985): the particular target that we wish to hit in the course of our inquiry has to be redefined “locally,” relative to each cognitive problem situation. Furthermore, in addition to the multiplicity of the possible targets, there may be several roads that lead to the same destination. The forward-looking character of the goals of inquiry does not exclude what Stegmüller calls “progress branching.” This is analogous to the simple fact that we may approach San Francisco from New York along two different ways—via Chicago or St Louis.

2.5 Progress and Rationality

Some philosophers use the concepts of progress and rationality as synonyms: progressive steps in science are precisely those that are based upon the scientists' rational choices. One possible objection is that scientific discoveries are progressive when they introduce novel ideas, even though they cannot be fully explained in rational terms (Popper 1959; cf. Hanson 1958; Kleiner 1993). However, another problem is more relevant here: By whose lights should such steps be evaluated? This question is urgent especially if we acknowledge that standards of good science have changed in history (Laudan 1984a).

As we shall see, the main rival philosophical theories of progress propose absolute criteria, such as problem-solving capacity or increasing truthlikeness, that are applicable to all developments of science throughout its history. On the other hand, rationality is a methodological concept which is historically relative : in assessing the rationality of the choices made by the past scientists, we have to study the aims, standards, methods, alternative theories and available evidence accepted within the scientific community at that time (cf. Doppelt, 1983, Laudan, 1987; Niiniluoto 1999). If the scientific community SC at a given point of time t accepted the standards V , then the preference of SC for theory T over T ′ on evidence e was rational just in case the epistemic utility of T relative to V was higher than that of T ′. But in a new situation, where the standards were different from V , a different preference might have been rational.

3.1 Realism and Instrumentalism

A major controversy among philosophers of science is between instrumentalist and realist views of scientific theories (Leplin 1984; Psillos 1999; Niiniluoto 1999). The instrumentalists follow Duhem in thinking that theories are merely conceptual tools for classifying, systematizing and predicting observational statements, so that the genuine content of science is not to be found on the level of theories (Duhem 1954). Scientific realists , by contrast, regard theories as attempts to describe reality even beyond the realm of observable things and regularities, so that theories can be regarded as statements having a truth value. Excluding naive realists, most scientists are fallibilists in Peirce's sense: scientific theories are hypothetical and always corrigible in principle. They may happen to be true, but we cannot know this for certain in any particular case. But even when theories are false, they can be cognitively valuable if they are closer to the truth than their rivals (Popper 1963). Theories should be testable by observational evidence, and success in empirical tests gives inductive confirmation (Hintikka 1968; Niiniluoto and Tuomela 1973; Kuipers 2000) or non-inductive corroboration to the theory (Popper 1959).

It might seem natural to expect that the main rival accounts of scientific progress would be based upon the positions of instrumentalism and realism. But this is only partly true. To be sure, naive realists as a rule hold the accumulation-of-truths view of progress, and many philosophers combine the realist view of theories with the axiological thesis that truth is an important goal of scientific inquiry. A non-cumulative version of the realist view of progress can be formulated by using the notion of truthlikeness. But there are also philosophers who accept the possibility of a realist treatment of theories, but still deny that truth is a relevant value of science which could have a function in the characterization of scientific progress. Bas van Fraassen's (1980) constructive empiricism takes the desideratum of science to be empirical adequacy : what a theory says about the observable should be true. The acceptance of a theory involves only the claim that it is empirically adequate, not its truth on the theoretical level. Van Fraassen has not developed an account of scientific progress in terms of his constructive empiricism, but presumably such an account would be close to empiricist notions of reduction and Laudan's account of problem-solving ability (see Section 3.2).

An instrumentalist who denies that theories have truth values usually defines scientific progress by referring to other virtues theories may have, such as their increasing empirical success. In 1908 Duhem expressed this idea by a simile: scientific progress is like a mounting tide, where waves rise and withdraw, but under this to-and-fro motion there is a slow and constant progress. However, he gave a realist twist to his view by assuming that theories classify experimental laws, and progress means that the proposed classifications approach a “natural classification” (Duhem 1954).

Evolutionary epistemology is open to instrumentalist (Toulmin) and realist (Popper) interpretations. A biological approach to human knowledge naturally gives emphasis to the pragmatist view that theories function as instruments of survival. Darwinist evolution in biology is not goal-directed with a fixed forward-looking goal; rather, species adapt themselves to an ever changing environment. In applying this account to the problem of knowledge-seeking, the fitness of a theory can be taken to mean that the theory is accepted by members of the scientific community. But a realist can reinterpret the evolutionary model by taking fitness to mean the truth or truthlikeness of a theory.

3.2 Empirical Success and Problem-Solving

For a constructive empiricist, it would be natural to think that among empirically adequate theories one theory T 2 is better than another theory T 1 if T 2 entails more true observational statements than T 1 . Such a comparison makes sense at least if the observation statements entailed by T 1 are a proper subset of those entailed by T 2 . Kemeny and Oppenheim (1956) gave a similar condition in their definition of reduction: T 1 is reducible to T 2 if and only if T 2 is at least as well systematized as T 1 and T 2 is observationally stronger than T 1 , i.e., all observational statements explained by T 1 are also consequences of T 2 . Variants of such an empirical reduction relation has been given by the structuralist school in terms of set-theoretical structures (Stegmüller 1976; Scheibe 1986; Balzer et al. 1987; Moulines 2000). A similar idea, but applied to cases where the first theory T 1 has been falsified by some observational evidence, was used by Lakatos in his definition of empirically progressive research programmes: the new superseding theory T 2 should have corroborated excess content relative to T 1 and T 2 should contain all the unrefuted content of T 1 (Lakatos and Musgrave 1970). The definition of Kuipers (2000) allows that even the new theory T 2 is empirically refuted: T 2 should have (in the sense of set-theoretical inclusion) more empirical successes, but fewer empirical counter-examples than T 1 .

Against these cumulative definitions it has been argued that definitions of empirical progress have to take into account an important complication. A new theory often corrects the empirical consequences of the previous one, i.e., T 2 entails observational statements e 2 which are in some sense close to the corresponding consequences e 1 of T 1 . Various models of approximate explanation and approximate reduction have been introduced to handle these situations. An important special case is the limiting correspondence relation: theory T 2 approaches theory T 1 (or the observational consequences of T 2 approach those of T 1 ) when some parameter in its laws approaches a limit value (e.g., theory of relativity approaches classical mechanics when the velocity of light c grows without limit). Here T 2 is said to be a concretization of the idealized theory T 1 (Nowak 1980; Nowakowa and Nowak 2000). However, these models do not automatically guarantee that the step from an old theory to a new one is progressive. For example, classical mechanics can be related by the correspondence condition to an infinite number of alternative and mutually incompatible theories, and some additional criteria are needed to pick out the best among them.

Kuhn's (1962) strategy was to avoid the notion of truth and to understand science as a problem-solving activity. Paradigm-based normal science is cumulative in terms of the problems solved, and even paradigm-changes or revolutions are progressive in the sense that “a relatively large part” of the problem-solving capacity of the old theory is preserved in the new paradigm. But, as Kuhn argued, it may happen that some problems solved by the old theory are no longer relevant or meaningful for the new theory. These cases are called “Kuhn-losses.” A more systematic account of these ideas is given by Laudan (1977): the problem-solving effectiveness of a theory is defined by the number and importance of solved empirical problems minus the number and importance of the anomalies and conceptual problems that the theory generates. Here the concept of anomaly refers to a problem that a theory fails to solve, but is solved by some of its rivals. For Laudan the solution of a problem by a theory T means that the “statement of the problem” is deduced from T . A good theory is thus empirically adequate, strong in its empirical content, and—Laudan adds—avoids conceptual problems.

One difficulty for the problem-solving account is to find a proper framework for identifying and counting problems (Rescher 1984; Kleiner 1993). When Newton's mechanics is applied to determine the orbit of the planet Mars, this can be counted as one problem. But, given an initial position of Mars, the same theory entails a solution to an infinite number of questions concerning the position of Mars at time t . Perhaps the most important philosophical issue is whether one may consistently hold that the notion of problem-solving may be entirely divorced from truth and falsity: the realist may admit that science is a problem-solving activity, if this means the attempt to find true solutions to predictive and explanatory questions (Niiniluoto 1984).

A different view of problem-solving is involved in those theories which discuss problems of decision and action . A radical pragmatist view treats science as a systematic method of solving such decision problems relative to various kinds of practical utilities. According to the view called behavioralism by the statistician LJ. Savage, science does not produce knowledge, but rather recommendations for actions: to accept a hypothesis is always a decision to act as if that hypothesis were true. Progress in science can then be measured by the achievement of the practical utilities of the decision maker. An alternative methodological version of pragmatism is defended by Rescher (1977) who accepts the realist view of theories with some qualifications, but argues that the progress of science has to be understood as “the increasing success of applications in problem-solving and control.” In this view, the notion of scientific progress is in effect reduced to science-based technological progress.

3.3 Explanatory Power, Unification, and Simplicity

Already the ancient philosophers regarded explanation as an important function of science. The status of explanatory theories was interpreted either in an instrumentalist or realist way: Plato's school started the tradition of “saving the appearances” in astronomy, while Aristotle took theories to be necessary truths. Both parties can take explanatory power to be a criterion of a good theory, as shown by van Fraassen's (1980) constructive empiricism and Wilfrid Sellars' scientific realism (Pitt 1981; Tuomela 1984). When it is added that a good theory should also yield true empirical predictions, the notions of explanatory and predictive power can be combined within the notion of systematic power (Hempel 1965). If the demand of systematic power simply means that a theory has many true deductive consequences in the observational language, this concept is essentially equivalent to the notion of empirical success and empirical problem-solving ability discussed in Section 3.2, but normally explanation is taken to include additional conditions besides mere deduction. Inductive systematization should also be taken into account (Hempel 1965; Niiniluoto and Tuomela 1973).

One important idea regarding systematization is that a good theory should unify empirical data and laws from different domains (Kitcher 1993). For Whewell, the paradigm case of such “consilience” was the successful unification of Kepler's laws and Galileo's laws by means of Newton's theory.

If theories are underdetermined by observational data, then one is often advised to choose the simplest theory compatible with the evidence (Foster and Martin 1966). Simplicity may be an aesthetic criterion of theory choice, but it may also have a cognitive function in helping us in our attempt to understand the world in an “economical” way. Ernst Mach's notion of the economy of thought is related to the demand of manageability , which is important especially in the engineering sciences and other applied sciences: for example, a mathematical equation can be made “simpler” by suitable approximations, so that it can be solved by a computer. Simplicity has also been related to the notion of systematic or unifying power. This is clear in Eino Kaila's concept of relative simplicity , defined as the ratio between the explanatory power and the structural complexity of a theory (cf. Niiniluoto 1980, 1999). According to this conception, progress can be achieved by finding structurally simpler explanations of the same data, or by increasing the scope of explanations without making them more complex. Laudan's formula of solved empirical problems minus generated conceptual problems is a variation of the same idea.

3.4 Truth and Information

Realist theories of scientific progress take truth to be an important goal of inquiry. This view is built into the classical definition of knowledge as justified true belief: if science is a knowledge-seeking activity, then it is also a truth-seeking activity. However, truth cannot be the only relevant epistemic utility of inquiry. This is shown in a clear way by the cognitive decision theory (Levi 1967; Niiniluoto 1987).

Let us denote by B = { h 1 , …, h n } a set of mutually exclusive and jointly exhaustive hypotheses. Here the hypotheses in B may be the most informative descriptions of alternative states of affairs or possible worlds within a conceptual framework L . For example, they may be complete theories expressible in a finite first-order language. If L is interpreted on a domain U , so that each sentence of L has a truth value (true or false), it follows that there is one and only one true hypothesis (say h *) in B . Our cognitive problem is to identify the target h * in B . The elements h i of B are the (potential) complete answers to the problem. The set D ( B ) of partial answers consists of all non-empty disjunctions of complete answers. The trivial partial answer in D ( B ), corresponding to ‘I don't know’, is represented by a tautology, i.e., the disjunction of all complete answers.

For any g in D ( B ), we let u ( g , h j ) be the epistemic utility of accepting g if h j is true. We also assume that a rational probability measure P is associated with language L , so that each h j can be assigned with its epistemic probability P ( h j / e ) given evidence e . Then the best hypothesis in D ( B ) is the one g which maximizes the expected epistemic utility

(1) U ( g / e ) = n ∑ i =1 P ( h j / e ) u ( g , h j )

For comparative purposes, we may say that one hypothesis is better than another if it has a higher expexted utility than the other by formula (1).

If truth is the only relevant epistemic utility, all true answers are equally good and all false answers are equally bad. Then we may take u ( g , h j ) simply to be the truth value of g relative to h j :

u ( g , h j ) = 1 if h j is in g   = 0 otherwise.

Hence, u ( g , h *) is the real truth value tv ( g ) of g relative to the domain U . It follows from (1) that the expected utility U ( g / e ) equals the posterior probability P ( g / e ) of g on e . In this sense, we may say that posterior probability equals expected truth value. The rule of maximizing expected utility leads now to an extremely conservative policy: the best hypotheses g on e are those that satisfy P ( g / e ) = 1, i.e., are completely certain on e (e.g. e itself and tautologies). On this account, if we are not certain of the truth, then it is always progressive to change an uncertain answer to a logically weaker one.

The argument against using high probability as a criterion of theory choice was made already by Popper in 1934 (see Popper 1959). He proposed that good theories should be bold or improbable. This idea has been made precise in the theory of semantic information.

Levi (1967) measures the information content I ( g ) of a partial answer g in D ( B ) by the number of complete answers it excludes. With a suitable normalization, I ( g ) = 1 if and only if g is one of the complete answers h j in B , and I ( g ) = 0 for a tautology. If we now choose u ( g , h j ) = I ( g ), then U ( g / e ) = I ( g ), so that all the complete answers in B have the same maximal expected utility 1. This measure favors strong hypotheses, but it is unable to discriminate between the strongest ones. For example, the step from a false complete answer to the true one does not count as progress. Therefore, information cannot be the only relevant epistemic utility.

Another measure of information content is cont ( g ) = 1 − P ( g ) (Hintikka 1968). If we choose u ( g , h j ) = cont ( g ), then the expected utility U ( g / e ) = 1 − P ( g ) is maximized by a contradiction, as the probability of a contradictory sentence is zero. Any false theory can be improved by adding new falsities to it. Again we see that information content alone does not give a good definition of scientific progress. The same remark can be made about explanatory and systematic power.

Levi's (1967) proposal for epistemic utility is the weighted combination of the truth value tv ( g ) of g and the information content I ( g ) of g :

(2) a I ( g ) + (1 − a ) tv ( g ),

where 0 < a < 1/2 is an “index of boldness,” indicating how much the scientist is willing to risk error, or to “gamble with truth,” in his attempt to be relieved from agnosticism. The expected epistemic utility of g is then

(3) a I ( g ) + (1 − a ) P ( g / e ).

A comparative notion of progress ‘ g 1 is better than g 2 ’ could be defined by requiring that both I ( g 1 ) > I ( g 2 ) and P ( g 1 / e ) > P ( g 2 / e ), but most hypotheses would be incomparable by this requirement. By using the weight a , formula (3) expresses a balance between two mutually conflicting goals of inquiry. It has the virtue that all partial answers g in D ( B ) are comparable with each other: g is better than g ′ if and only if the value of (3) is larger for g than for g ′.

If epistemic utility is defined by information content cont(g) in a truth-dependent way, so that

U ( g , e ) = cont ( g ) if g is true   = − cont (¬ g ) if g is false,

(i,e., in accepting hypothesis g , we gain the content of g if g is true, but we lose the content of the true hypothesis ¬ g if g is false), then the expected utility U ( g / e ) is equal to

(4) P ( g / e ) − P ( g )

This measure combines the criteria of boldness (small prior probability P ( g )) and high posterior probability P ( g / e ). Similar results can be obtained if cont ( g ) is replaced by Hempel's (1965) measure of systematic power syst ( g , e ) = P (¬ g /¬ e ).

For Levi, the best hypothesis in D ( B ) is the complete true answer. But his utility assignment also makes assumptions that may seem problematic: all false hypotheses (even those that make a very small error) are worse than all truths (even the uninformative tautology); all false complete answers have the same utility (see, however, the modified definition in Levi, 1980); among false hypotheses utility covaries with logical strength. These features are motivated by Levi's project of using epistemic utility as a basis of acceptance rules. But if such utilities are used for ordering rival theories, then the theory of truthlikeness suggests other kinds of principles.

3.5 Truthlikeness

Popper's notion of truthlikeness is also a combination of truth and information (Popper 1963, 1972). For him, verisimilitude represents the idea of “approaching comprehensive truth.” Popper's explication used the cumulative idea that the more truthlike theory should have (in the sense of set-theoretical inclusion) more true consequences and less false consequences, but it turned that this comparison is not applicable to pairs of false theories. An alternative method of defining verisimilitude, initiated in 1974 by Pavel Tichy and Risto Hilpinen, relies essentially on the concept of similarity (Oddie 1986; Niiniluoto 1987).

In the similarity approach, as developed in Niiniluoto (1987), closeness to the truth is explicated “locally” by means of the distances of partial answers g in D ( B ) to the target h * in a cognitive problem B . For this purpose, we need a function d which expresses the distance d ( h i , h j ) = d ij between two arbitrary elements of B . By normalization, we may choose 0 ≤ d ij ≤ 1. The choice of d depends on the cognitive problem B , and makes use of the metric structure of B (e.g., if B is a subspace of the real numbers ℜ) or the syntactic similarity between the statements in B . Then, for a partial answer g , we let D min ( h i , g ) be the minimum distance of the disjuncts in g from h i , and D sum ( h i , g ) the normalized sum of the distances of the disjuncts of g from h i . Then D min ( h i , g ) tells how close to h i hypothesis g is, so that the degree of approximate truth of g (relative to the target h *) is 1 − D min ( h *, g ). On the other hand, D sum ( h i , g ) includes a penalty for all the mistakes that g allows relative to h i . The mini-sum measure

(5) D ms ( h i , g ) = a D min ( h i , g ) + bD sum ( h i , g ),

where a > 0 and b > 0, combines these two aspects. Then the degree of truthlikeness of g is

(6) Tr ( g , h *) = 1 − D ms ( h *, g ).

Thus, parameter a indicates our cognitive interest in hitting close to the truth, and parameter b indicates our interest in excluding falsities that are distant from the truth. In many applications, choosing a to be equal to 2 b gives intuitively reasonable results.

If the distance function d on B is trivial, i.e., d ij = 1 if and only if i = j , and otherwise 0, then Tr ( g , h *) reduces to the variant (2) of Levi's definition of epistemic utility.

Obviously Tr ( g , h *) takes its maximum value 1 if and only if g is equivalent to h *. If g is a tautology, i.e., the disjunction of all elements h i of B , then Tr ( g , h *) = 1 − b . If Tr ( g , h *) < 1 − b , g is misleading in the strong sense that its cognitive value is smaller than that of complete ignorance.

When h * is unknown, the degree of truthlikeness (6) cannot be calculated. But the expected degree of verisimilitude of a partial answer g given evidence e is given by

(7) ver ( g / e ) = n ∑ i =1 P ( h i / e ) Tr ( g , h i )

If evidence e entails some h j in B , or makes h j completely certain (i.e., P ( h j / e ) = 1), then ver ( g / e ) reduces to Tr ( g , h j ). If all the complete answers h i in B are equally probable on e , then ver ( h i / e ) is also constant for all h i .

The truthlikeness function Tr allows us to define an absolute concept of real progress :

(RP) Step from g to g ′ is progressive if and only if Tr ( g , h *) < Tr ( g ′, h *),

and the expected truthlikeness function ver gives the relative concept of estimated progress :

(EP) Step from g to g ′ seems progressive on evidence e if and only if ver ( g / e ) < ver ( g ′/ e ).

(Cf. Niiniluoto 1980.) According to definition RP, it is meaningful to say that one theory g ′ satisfies better the cognitive goal of answering problem B than another theory g . This is an absolute standard of scientific progress in the sense of Section 2.5. Definition EP shows how claims of progress can be fallibly evaluated on the basis of evidence: if ver ( g / e ) < ver ( g ′/ e ), it is rational to claim on evidence e that the step from g to g ′ in fact is progressive. This claim may of course be mistaken, since estimation of progress is relative to two factors: the available evidence e and the probability measure P employed in the definition of ver . Both evidence e and the epistemic probabilities P ( h i / e ) may mislead us. In this sense, the problem of estimating verisimilitude is as difficult as the problem of induction.

In Section 3.5., we made a distinction between real and estimated progress in terms of the truthlikeness measures. A similar distinction can be made in connection with measures of empirical success. For example, one may distinguish two notions of the problem-solving ability of a theory: the number of problems solved so far , and the number of solvable problems. Real progress could be defined by the latter, while the former gives us an estimate of progress.

The scientific realist may continue this line of thought by arguing that all measures of empirical success in fact are at best indicators of real cognitive progress, measured in terms of truth or truthlikeness. For example, if T explains e , then it can be shown that e also confirms T , or increases the probability of T . A similar reasoning can be employed to give the so-called “ultimate argument” for scientific realism: theoretical realism is the only assumption that does not make the empirical success of science a miracle (Putnam, 1978; Psillos 1999; Niiniluoto 1999; cf. criticism in Laudan 1984b). This means that the best explanation of the empirical progress of science is the hypothesis that science is also progressive on the level of theories.

The thesis that science is progressive is an overall claim about scientific activities. It does not imply that each particular step in science has in fact been progressive: individual scientists make mistakes, and even the scientific community is fallible in its collective judgments. For this reason, we should not propose such a definition that the thesis about the progressive nature of science becomes a tautology or an analytic truth. This undesirable consequence follows if we define truth as the limit of scientific inquiry (this is sometimes called the consensus theory of truth), as then it is a mere tautology that the limit of scientific research is the truth (Laudan 1984a). But this “trivialization of the self-corrective thesis” cannot be attributed to Peirce who realized that truth and the limit of inquiry coincide at best with probability one (Niiniluoto 1980). The notion of truthlikeness allows us to make sense of the claim that science converges towards the truth. But the characterization of progress as increasing truthlikeness, given in Section 3.5, does not presuppose “teleological metaphysics” (Stegmüller 1976), “convergent realism” (Laudan 1984), or “scientific eschatology” (Moulines 2000), as it does not rely on any assumption about the future behavior of science.

The claim about scientific progress can still be questioned by the theses that observations and ontologies are relative to theories. If this is true, the comparison of rival theories appears to be impossible on cognitive or rational grounds. Kuhn (1962) compared paradigm-changes to Gestalt switches (Dilworth 1981). Feyerabend (1984) concluded from his methodological anarchism that the development of science and art resemble each other.

Hanson, Popper, Kuhn, and Feyerabend agreed that all observation is theory-laden , so that there is no theory-neutral observational language. Accounts of reduction and progress, which take for granted the preservation of some observational statements within theory-change, thus run into troubles. Even though Laudan's account of progress allows Kuhn-losses, it can be argued that the comparison of the problem-solving capacity of two rival theories presupposes some kind of correlation or translation between the statements of these theories (Pearce 1987). Various replies have been proposed to this issue. One is the movement from language to structures (Stegmüller 1976; Moulines 2000), but it turns out that a reduction on the level structures already guarantees commensurability, since it induces a translation between conceptual frameworks (Pearce 1987). Another has been the point that an evidence statement e may happen to be neutral with respect to rival theories T 1 and T 2 , even though it is laden with some other theories. The realist may also point that the theory-ladenness of observations concerns at most the estimation of progress (EP), but the definition of real progress (RP) as increasing truthlikeness does not mention the notion of observation at all.

Even though Popper accepted the theory-ladenness of observations, he rejected the more general thesis about incommensurability as “the myth of the framework” (Lakatos and Musgrave 1970). Popper insisted that the growth of knowledge is always revolutionary in the sense that the new theory contradicts the old one by correcting it, but there is still continuity in theory-change, as the new theory should explain why the old theory was successful to some extent. Feyerabend tried to claim that successive theories are both inconsistent and incommensurable with each other, but this combination makes little sense. Kuhn argued against the possibility of finding complete translations between the languages of rival theories, but in his later work he admitted the possibility that a scientist may learn different theoretical languages (Hoyningen-Huene 1993). Kuhn kept insisting that there is “no theory-independent way to reconstruct phrases like ‘really there’,” i.e., each theory has its own ontology. Convergence to the truth seems to be impossible, if ontologies change with theories. The same idea has been formulated by Putnam (1978) and Laudan (1984a) in the so-called “pessimistic meta-induction”: as many past theories in science have turned out to be non-referring, there is all reason to expect that even the future theories fail to refer—and thus also fail to be approximately true or truthlike.

The difficulties for realism seem to be reinforced by the observation that measures of truthlikeness are relative to languages. The choice of conceptual frameworks cannot be decided by means of the notion of truthlikeness, but needs additional criteria. In defense of the truthlikeness approach, one may point to the fact that the comparison of two theories is relevant only in those cases where they are considered (perhaps via a suitable translation) as rival answers to the same cognitive problem. It is interesting to compare Newton's and Einstein's theories for their truthlikeness, but not Newton's and Darwin's theories.

Another line is to appeal to theories of reference in order to show that rival theories can after all be regarded as speaking about the same entities (Psillos 1999). For example, Thompson, Bohr, and later physicists are talking about the same electrons, even though their theories of the electron differ from each other. This is not possible on the standard descriptive theory of reference: a theory T can only refer to entities about which it gives a true description. Kuhn's and Feyerabend's meaning holism, with devastating consequences for realism, presupposes this account of reference. A similar argument is used by Moulines (2000), who denies that progress could be understood as “knowing more about the same,” but his own structuralist reconstruction of progress with “partial incommensurability” assumes that rival theories share some intended applications. Causal theories of reference allow that reference is preserved even within changes of theories (Kitcher 1993). The same result is obtained if the descriptive account is modified by introducing a Principle of Charity (Putnam 1975; Smith 1981; Niiniluoto 1999): a theory refers to those entities about which it gives the most truthlike description. This makes it possible that even false theories are referring. Moreover, there can be reference invariance between two successive theories, even though both of them are false; progress means then that the latter theory gives a more truthlike description about their common domain than the old theory.

Does this mean that, by choosing to be charitable, we can simply decide that some theory sequences are progressive? The answer is negative, since charitable reference fixing is not arbitrary: the relevant degrees of truthlikeness depend on the relations between theories and reality.

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