Research

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Is science rational, and if so, in virtue of what? Popper (1959), Kuhn (1962), Lakatos (1968), and Feyerabend (1975) took this to be the central question of philosophy of science, and each gave very different answers.

Kitcher (1993) and Strevens (2003) have suggested that science may achieve a kind of “aggregate epistemic rationality” even when individual scientists are not epistemically rational. This raises questions about how the behavior of individual scientists translates into epistemic outcomes at the aggregate level, or in other words about how individual behavior contributes to or detracts from the success of science.

My research investigates how aspects of the social organization of science affect individual scientists’ decisions and how these in turn contribute to the aggregate epistemic rationality of science. I identify three research themes.

The first theme is the credit economy. The primary reward for good scientific work is credit or recognition (Merton 1957). Credit is crucial to having a successful career as a scientist (Hull 1988, Kitcher 1993).

Because the (conscious or unconscious) desire to receive credit for their work has such a big influence on scientists’ behavior, an important question is whether behavior guided by this desire is likely to be epistemically successful. Where previous work has focused on the good news, my research identifies both positive and negative effects of the credit economy.

Consider the social norms that scientists take themselves to be held to, such as universalism (scientific claims should be evaluated impartially) and disinterestedness (scientists should be unbiased). Does the credit economy incentivize scientists to conform to these norms? And do these norms contribute to or detract from the aggregate epistemic rationality of science?

“Communism and the Incentive to Share in Science” (Heesen forthcoming b) addresses these questions for the communist norm, which calls on scientists to share their results widely rather than keep them secret. I argue that the communist norm contributes to the epistemic rationality of science, and that a concern for credit gives scientists an incentive to conform to it.

Another question about the credit economy regards the role of the priority rule—the winner-takes-all rule under which only the first scientist to make a discovery gets credit for it. This rule can be implemented in different ways. Depending on what counts as a discovery and how much credit is given for different discoveries, different “priority rules” and hence different incentive structures are created. In future research I intend to carry out a systematic investigation of the upsides and downsides of different priority rules.

The second theme is the social stratification of science. Science is extremely hierarchical—as reflected in the distribution of grant money, research time, productivity, and citations—even more so than other areas of social life are (Cole and Cole 1973, Xie 2014).

If this hierarchy is largely based on merit, it likely contributes to the epistemic rationality of science (Cole and Cole 1973). But what criteria could be used to determine whether the hierarchical structure of science tracks merit? Some of the obvious answers do not work, because random luck can produce a hierarchical structure that is indistinguishable from one produced by merit, as I show in “Academic Superstars: Competent or Lucky?” (Heesen forthcoming a).

If science is not a meritocracy, perhaps it should be made less hierarchical. How could a more be egalitarian structure be achieved? Would such a structure lead to better science? To take an extreme example, would requiring all scientific work to be published anonymously improve the epistemic rationality of science? In “When Journal Editors Play Favorites” (Heesen forthcoming c) I argue that it is both ethically and epistemically better if scientific work is anonymous to journal editors (triple-anonymous reviewing).

The third theme is scientific publications and peer review. In my working paper “Why the Reward Structure of Science Makes Reproducibility Problems Inevitable” I argue that the pressure to publish contributes to epistemic problems, in particular difficulties in reproducing scientific results. I aim to expand this line of research and suggest possible solutions.

My investigations of the social epistemology of science use mathematical models, drawing mainly upon game theory. Game theory is a particularly suitable tool to study the social structure of science, as it studies how individual success interacts with aggregate success in a multi-agent system.

Changing a model parameter or assumption is relatively easy, whereas experimenting with the real social structure of science is virtually impossible. This allows me to ask counterfactual questions of the form: “Would changing this feature of the social structure of science help or harm the epistemic rationality of science?” In this way I tie philosophical questions (“What is the epistemic role of the social structure of science?”) closely to practical questions (“How could certain policies help or harm science?”).

The use of mathematical models comes with its own set of methodological questions. Do models teach us anything about the world, and if so, how? In extant views judgments of similarity between the model and the world play an important role (e.g., Sugden 2000, Mäki 2009, Weisberg 2013).

In future work I will develop a view that does not rely on judgments of similarity. My view reinterprets robustness as a property of sets of models rather than a property of particular models. Some of the basic ideas are set out in “Vindicating Methodological Triangulation” (Heesen et al. forthcoming).

References

  • Jonathan R. Cole and Stephen Cole. Social Stratification in Science. University of Chicago Press, Chicago, 1973. ISBN 0226113388.
  • Paul Feyerabend. Against Method. New Left Books, London, 1975.
  • Remco Heesen. Academic superstars: Competent or lucky? Synthese, forthcoming a. ISSN 1573-0964. doi:10.1007/s11229-016-1146-5. URL http://dx.doi.org/10.1007/s11229-016-1146-5.
  • Remco Heesen. Communism and the incentive to share in science. Philosophy of Science, forthcoming b. ISSN 0031-8248.
  • Remco Heesen. When journal editors play favorites. Philosophical Studies, forthcoming c. ISSN 0031-8116. doi:10.1007/s11098-017-0895-4. URL http://dx.doi.org/10.1007/s11098-017-0895-4.
  • Remco Heesen, Liam Kofi Bright, and Andrew Zucker. Vindicating methodological triangulation. Synthese, forthcoming. ISSN 1573-0964. doi:10.1007/s11229-016-1294-7. URL http://dx.doi.org/10.1007/s11229-016-1294-7.
  • David L. Hull. Science as a Process: An Evolutionary Account of the Social and Conceptual Development of Science. University of Chicago Press, Chicago, 1988. ISBN 0226360504.
  • Philip Kitcher. The Advancement of Science: Science without Legend, Objectivity without Illusions. Oxford University Press, Oxford, 1993. ISBN 0195046285.
  • Thomas S. Kuhn. The Structure of Scientific Revolutions. The University of Chicago Press, Chicago, 1962.
  • Imre Lakatos. Criticism and the methodology of scientific research programmes. Proceedings of the Aristotelian Society, 69:149–186, 1968. ISSN 00667374. URL http://www.jstor.org/stable/4544774.
  • Robert K. Merton. Priorities in scientific discovery: A chapter in the sociology of science. American Sociological Review, 22(6):635–659, 1957. ISSN 00031224. URL http://www.jstor.org/stable/2089193.
  • Uskali Mäki. Realistic realism about unrealistic models. In Harold Kincaid and Don Ross, editors, The Oxford Handbook of the Philosophy of Economics, chapter 4, pages 68–98. Oxford University Press, Oxford, 2009.
  • Karl Popper. The Logic of Scientific Discovery. Hutchinson, London, 1959.
  • Michael Strevens. The role of the priority rule in science. The Journal of Philosophy, 100(2):55–79, 2003. ISSN 0022362X. URL http://www.jstor.org/stable/3655792.
  • Robert Sugden. Credible worlds: the status of theoretical models in economics. Journal of Economic Methodology, 7(1):1–31, 2000. doi: 10.1080/135017800362220. URL http://dx.doi.org/10.1080/135017800362220.
  • Michael Weisberg. Simulation and Similarity: Using Models to Understand the World. Oxford University Press, Oxford, 2013. ISBN 9780199933662.
  • Yu Xie. “Undemocracy”: inequalities in science. Science, 344(6186):809–810, 2014. doi: 10.1126/science.1252743. URL http://www.sciencemag.org/content/344/6186/809.short.