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Scientific revolutions and the explosion of scientific evidence

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Abstract

Scientific realism, the position that successful theories are likely to be approximately true, is threatened by the pessimistic induction according to which the history of science is full of successful, but false theories. I aim to defend scientific realism against the pessimistic induction. My main thesis is that our current best theories each enjoy a very high degree of predictive success, far higher than was enjoyed by any of the refuted theories. I support this thesis by showing that the amount and quality of scientific evidence has increased enormously in the recent past, resulting in a big boost of success for the best theories.

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Notes

  1. See Boyd (1990), Hacking (1983), Worrall (1989), Kitcher (1993), Carrier (1993), Leplin (1997), Psillos (1999), French and Ladyman (2003).

  2. Similar claims and arguments are presented by Doppelt (2007), Park (2011), and Fahrbach (2009, (2011a, (2011b). My paper builds on their work. It aims to fill several gaps in the argumentation of those approaches and provide a more solid foundation for the whole dialectic.

  3. The term “inductive inference” is meant to cover all valid non-deductive inferences.

  4. On truth approximation, see, e.g., Niiniluoto (1999) and Kuipers (2000).

  5. Many further examples can be found in Lyons (2002), Stanford (2006), and Vickers (2013).

  6. In what follows, the locution “a significant proportion” always means “at least a significant proportion”. Thus, the premise of the PI includes the possibility that most or all successful historical theories are false.

  7. The premise, in turn, is supported by the sample of past refuted theories offered by the anti-realist.

  8. What I am using here is the equivalence that a statement P undermines a statement R, just in case it supports the negation of R. This equivalence is true on many or most accounts of inductive inference, for example, if undermining and supporting are understood in probabilistic terms: \(\hbox {Pr}(R{\vert }P)\) is near zero iff Pr(\(\lnot R{\vert }P\)) is near one. Sometimes I use the notions of supporting and undermining and similar notions in an absolute sense (in probabilistic terms: probabilities near one or near zero), and sometimes in an incremental sense (in probabilistic terms: increase or decrease of probability). The context will make clear what I mean.

  9. Compare Saatsi and Vickers (2011, p. 33) and Wray (2015).

  10. An analogous remark applies, if the NMA is interpreted as an instance of IBE (inference to the best explanation). IBE is a kind of inductive inference and therefore fallible. It is not refuted, if some cases of best explanations turn out to be false, as long as such cases are rare.

  11. This line of thought is presented forcefully by Wray (2013). It is surprisingly complex, so I won’t attempt to analyze all of its aspects here.

  12. Ideas akin to the projective PI are mentioned or hinted at by Stanford (2006, Chap. 1.2), Doppelt (2007), Bird (2007), Roush (2009), Wray (2013, p. 4328), and others.

  13. The projective PI, as I understand it here, has roughly the form of an inductive inference from the set of past cases of a population to the next future cases of the population (similar to the inference from observed ravens to the next unobserved raven, but with the difference that not all past cases have actually been observed, so that there is an initial inductive step from the sample of actually observed past refuted theories offered by the anti-realist to the whole set of past theories). The simple PI has roughly the form of an inductive inference from the set of past cases of a population to the whole population (from observed ravens to all ravens). Note, however, that a number of inferentially relevant features, such as the role of degrees of success in the projective PI and the role of “attrition” (Ruhmkorff 2013), are not captured by the two inductive forms.

  14. Stanford (2006, Chaps. 1, 6) and Devitt (2011) also mention a charge of ad-hocness, but they may have something like the first objection in mind.

  15. Thanks to Paul Thorn for the reminder.

  16. At this point one may diagnose a stalemate: Neither the realist nor the anti-realist are able to gain the upper hand (compare Stanford 2006; Chakravartty 2007). Stanford goes on to develop a new version of the PI, the “New Induction”, which he claims avoids the stalemate. The New Induction states that we should project the existence of unconceived alternatives from past to present. The New Induction adds a number of novel and interesting considerations to the realism debate, but some authors argue that it ends in stalemate as well (Kukla 2010; Ruhmkorff 2011; Egg 2016), whereas I think it can be countered with the material presented in this paper, as I will indicate below.

  17. A further analysis would presumably show that the two kinds of assessments are related somehow, but I discuss them separately in this paper.

  18. I assume that these claims about theory testing, as well as similar claims in other parts of the paper, are truisms. They are not accepted by everyone, of course. Many philosophers are still in the grip of well-known qualms of a Kuhnian type. These include the following: “puzzle solving” in normal science cannot confirm the basic assumptions of a paradigm; theories only grow in a “sea of anomalies”; in normal science when a prediction is proven false scientists don’t blame the basic assumptions of the paradigm, but rather each other; and so on (see for example Kuhn 1962; Hoyningen-Huene 1993, p. 179; Bird 2000, p. 37). These are interesting assertions to be sure, but I cannot discuss them here.

  19. Even for results that are not considered to be well-established, scientist are not eager to retest them. “The replication of previously published results has rarely been a high priority for scientists, who tend to regard it as grunt work. Journal editors yawn at replications. Honours and advancement in science go to those who publish new, startling results, not to those who confirm—or disconfirm—old ones” (Adler 2014).

  20. These are empirical claims. I deem them sufficiently plausible to assume them here, but there are exceptions. As we will see later, sometimes scientists do engage in projects where at least one of their aims is to test a well-established theory. Maybe such cases are more wide-spread than it first appears. If so, then all the better.

  21. Remember that realism only asserts the approximate truth of these theories. For example, not every infectious disease is caused by microbes, viruses or parasites, but the only known exception among more than 200 known types of infectious diseases are prion diseases which are extremely rare. (Thanks to Hasok Chang for this example, even though I use it for a purpose contrary to the one he intended.)

  22. The term “Periodic Table of Elements“ is meant to denote the statements that can be taken to be associated with the Periodic Table of Elements, for example the statement that every chemical substance can be decomposed into the chemical elements.

  23. The notion of observability I have in mind here is the usual one of Fraassen (1980), but the precise understanding of this notion is of little consequence here.

  24. See Stanford (2006, Chap. 2) and Fahrbach (2011a).

  25. Barrett (2003) argues that the claims that quantum mechanics and relativity are approximately true have no clear sense for us today and are “hopelessly vague”. See also Barrett (2008).

  26. Beisbart (2009) and Butterfield (2012) discuss examples of underdetermination in cosmology. If any of their examples concerns theories with very high success, then they also threaten the success-to-truth inference. I treat cosmology as part of fundamental physics.

  27. Fundamental physics is the only scientific discipline (apart from metaphysics) that aims for maximal generality, at least in principle. Besides, it is the only scientific discipline that does not rest content with approximate truth, but aims, at least in principle, to find theories that are completely precise and exception-less (compare Earman and Roberts 1999, p. 446), which is another reason to assign very low priors.

  28. de Solla Price (1963), Vickery (2000), Fahrbach (2011a).

  29. A growth with a constant doubling rate can be represented by an exponential function \(f(t)=a \hbox {e}^{bt}\), where t is time and a and b are suitable constants. A doubling rate of 20 years means that, approximately, \(f(t+70\hbox { years}) = 10 \cdot f(t)\) and \(f(t+100\hbox { years}) = 30 \cdot f(t)\).

  30. See previous footnote.

  31. On tyrannosaur fossils see Brusatte et al. (2010, p. 1481), on human fossils see Bryson and Roberts (2003, Chap. 28).

  32. https://en.wikipedia.org/wiki/GenBank. Compare Lupski (2010).

  33. See, for example, Nordhaus (2002).

  34. For some anecdotal evidence see Bixby (2002, p. 14), Holdren et al. (2010, p. 71) and Yudkowsky, (2007).

  35. For example, the data of ARGO, GenBank, the Sloan Digital Sky Survey, and many other surveys are freely accessible online for everybody.

  36. Humphreys (2004, especially Chap. 3) discusses the huge difference the increase in computing power has made in the physical sciences. He observes that “much of the success of the modern physical sciences is due to calculation” (2004, p. 55).

  37. A reminder: Judgments about the degrees of success of theories depend only on the amount, quality, and diversity of the passed tests, and are independent of the NMA intuition, whereas judgments about the confirmation of theories by evidence concern the probability or credibility of the theories given the evidence, and depend on the assumption that the NMA has probative force.

  38. See any textbook on the theory of evolution, sites like www.talkorigins.org, or the entry “Evidence of common descent” in Wikipedia.

  39. The Sloan Digital Sky Survey mentioned earlier is just one of dozens of sky surveys produced in the last decades, see Djorgovski et al. (2012).

  40. To get an idea just how great the diversity of currently available measurement devices and techniques is have a look at Wikipedia, e.g. the entries on “measuring instruments” and “non-destructive testing”.

  41. Some of these areas are quite different from others, therefore the diversity of the evidence for plate tectonics is of the deep kind.

  42. Further kinds of gentle tests in chemistry are described by Goodwin (2012, pp. 436–439, 441; 2013, pp. 808–809).

  43. https://www.cas.org/news/media-releases/100-millionth-substance. See the diagram there.

  44. Here is a project for a truly ambitious anti-realist: Conceive an alternative to the Periodic Table of Elements that is entirely different from it, but also able to provide a systematic categorization, like the categorization by molecular structure, of the 100 million chemical substances synthesized so far.

  45. Judgments about the precision of measurement are theory-laden to some extent (and likewise judgments about the size or diversity of data sets). I don’t think that this is a serious problem, but cannot argue the case here. Let me just note that the accuracy of scientific data is usually not taken to be threatened by a pessimistic induction.

  46. Devitt (1997, (2008) argues against the PI by invoking the improvement of scientific methods over the history of science, in which he means to include the improvement of instruments and measurement techniques. See also Roush (2009). However, note that replying to the PI by merely pointing out that scientific methods have been constantly improving runs into an objection analogous to the projective PI that theory change should be extrapolated along the improvement of method.

  47. A consequence of the massive improvements of instruments over the last few decades is that the replication of experiments has oftentimes become extremely easy. This means that the corresponding rechecking of theories has oftentimes become extremely easy. For example, most experiments at the cutting edge of physics before WWII can now be performed by students in their university education. (Compare the remarks at the end of the sub-section on computing power.)

  48. The diversity is of the “regular” sort, because the data are all of the same kind. They vary with respect to the parameter of location.

  49. Rainville (2005). See Krieger (2006) for the story behind this validation of \(\hbox {E} = \hbox {mc}^{2}\) in which the two teams made their respective measurements entirely independently from each other until they deemed them stable, and then simultaneously faxed the results to each other.

  50. Another example of an intentional test of a well-confirmed theory is a recent test of Newton’s law of gravitation for masses separated by \(55\,\upmu \hbox {m}\) (Speake 2007). This test also served to rule out some versions of string theory, hence testing Newton’s law of gravitation was not the only purpose of this project.

  51. See Park (2011) and Fahrbach (2011a, (2011b).

  52. Vickers (2013) offers some examples of refuted or abandoned theories from the recent past: S-matrix theory, Steady state cosmology, Velikovsky, Minkowski’s theory of the momentum of light. None of these theories were highly successful, they were not even generally accepted or constituted textbook knowledge, hence none were RATs. An especially convincing local version of PI which concerns results of medical studies (hence not RATs) is presented in Ruhmkorff (2013).

  53. Hence we need not decide whether or not these theories actually enjoyed very high degrees of success.

  54. A version of an optimistic induction using formal Bayesian methods is developed in Sprenger (2015).

  55. I am not claiming that categories MOD and HI are empty today, or that they contain fewer theories than category VHI today. More generally, I am neither claiming that science has come to an end, nor that it will end soon, nor that it will ever end. Although I think that, taken together, our current best theories form a fairly comprehensive and very stable world view, it is obvious that our knowledge still has many gaps. Many important questions have not received an answer yet, e.g., questions concerning diseases, natural disasters, hangovers, the future in general, and fundamental levels of physics, and some questions may never receive an answer, e.g., questions about consciousness or global features of the universe.

  56. The optimistic induction can also be used to rebut Stanford’s New Induction (2006), at least if one makes the unproblematic assumption that if the conclusion of the New Induction is true (many of our current best theories have “equally well-confirmed” unconceived alternatives), then many or most of our current best theories are false. Needless to say this issue requires more attention than I can give it here.

  57. This issue was pressed by a referee of this journal.

  58. These concerns were raised by the other referee of this journal. For some useful methodological remarks see Godfrey-Smith (2008, pp. 147–148).

  59. The anti-realist either has to show that current levels of success are still below the threshold of degrees of success beyond which past theory failures should not be projected, or she to show that such a threshold does not exist at all, i.e., past refutations imply that theory change will go on forever.

  60. Most refutations were merely partial refutations anyway, if there is anything to all the efforts by realists to show that important parts of successful-but-refuted theories were retained in successor theories.

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Acknowledgments

I would like to thank Michael Anacker, Luc Bovens, Hasok Chang, Michael Devitt, Brigitte Falkenburg, Bernward Gesang, Stephan Hartmann, Paul Hoyningen-Huene, Felicitas Krämer, James Nguyen, Helmut Pulte, Eric Schliesser, Gerhard Schurz, Mark Siebel, all anonymous referees and audiences in Konstanz, Manchester, Duesseldorf, Dublin, Oldenburg, Bochum, Dortmund, Toronto, and Bern. For detailed comments on this or earlier drafts I would like to thank Claus Beisbart, Sungbae Park, Sam Ruhmkorff, Ioannis Votsis, and Paul Thorn. It is highly probable that I forgot some people, for which I apologize.

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Fahrbach, L. Scientific revolutions and the explosion of scientific evidence. Synthese 194, 5039–5072 (2017). https://doi.org/10.1007/s11229-016-1193-y

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