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Fast and Frugal Heuristics at Research Frontiers

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Models and Inferences in Science

Part of the book series: Studies in Applied Philosophy, Epistemology and Rational Ethics ((SAPERE,volume 25))

Abstract

How should we model scientific decision-making at the frontiers of research? This chapter explores the applicability of Gerd Gigerenzer’s “fast and frugal” heuristics to frontier contexts, i.e., to so-called context of discovery. Such heuristics require only one or a very few steps to a decision and only a little information. While the approach is somewhat promising, given the limited resources in frontier contexts, trying to extend it to fairly “wild” frontiers raises challenging questions. This chapter attempts to frame the issues (rather than to provide resolutions to them), and thereby to cast light on frontier contexts, which have been misunderstood by philosophers, the general public, and funding agencies alike.

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Notes

  1. 1.

    There are other lines of both descriptive and normative work of this sort. Meehl (1954) was critical of the expert-intuitions approach typical of traditional clinical psychology and argued, with empirical support, that relatively simple decision rules often provide better results. Meehl’s rules are not always fast and frugal, and Gigerenzer does not reject expert intuition as a sometimes-reliable basis for decision-making. See also Dawes (1979). Bishop and Trout (2005) argue for an ameliorative epistemology to replace standard analytic epistemology. They identify with the Meehl tradition and are rather critical of Gigerenzer’s approach (Chaps. 8 and 9).

  2. 2.

    See the quotations from Edward Feigenbaum and Allen Newell in my (2015).

  3. 3.

    There are exceptions, e.g., causal Bayesian networks and (other) algorithmic searches of large databases.

  4. 4.

    For a defense of these claims, see Nickles (2006, 2015, draft-2015).

  5. 5.

    Strong proponents of Bayesian methods sometimes leave the impression that Bayesian methods are the updated form of a universal, content-neutral scientific method; but many frontier contexts would seem to pose severe difficulties for Bayesian methods as for other approaches.

  6. 6.

    Moreover, as the CAS website informs us, CAS deals only with “disclosed” chemistry, not the undisclosed, secret research for military and proprietary purposes.

  7. 7.

    Wikipedia article “Chemical Abstracts,” accessed 2 June 2015.

  8. 8.

    AI researcher Douglas Lenat soon extended these ideas to all of science. “Discovery is ubiquitous,” he said (Lenat 1978). Problem solving as search pervades scientific work, including the testing and justification phases (regarded as ongoing practices or processes rather than finished products), and is not limited to an early stage of “context of discovery.” Thus understanding discovery is necessary to understand science.

  9. 9.

    Wimsatt (1980) stressed that even reliable heuristics work well only in limited domains of application.

  10. 10.

    The distinction is between those AI systems that incorporate rules gleaned from human experts and knowledge-based systems more generally.

  11. 11.

    Gigerenzer’s treatment of heuristics thus differs from the “heuristics and biases” program of Tversky and Kahneman, which retains the classical conception of rationality (Kahneman et al. 1982). See, e.g., Gigerenzer et al. (1999, 2011).

  12. 12.

    On Darwin and Turing see Dennett (2009). For his Tower of Generate and Test see his (1995, 1996).

  13. 13.

    According to Simon (1992, 155): “Intuition is nothing more and nothing less than recognition.” Klein (1999) develops a “Recognition-Primed Decision” model in which intuition plays an important role.

  14. 14.

    It is no wonder that those positivists who drew the invidious context-of-discovery/context-of-justification distinction on traditional logical grounds found context of discovery to be non-logical and hence non-epistemic.

  15. 15.

    The peer-review process used by most journals and funding agencies has recently come under fire. See, e.g., Braben (2004) and Gillies (2008).

  16. 16.

    Structure, Chap. V, but see also his 1960s attempts at computer modeling in Kuhn (1970a, b).

  17. 17.

    Darwin himself saw a connection to the Meno problem as is evident from his notebook entries. See Desmond and Moore (1991, p. 263).

  18. 18.

    See the especially the Goldman, Gorman, and Wang comments on Todd and Gigerenzer (2000).

  19. 19.

    See Meehl (1954), Bishop and Trout (2005) and Trout (2009, Chap. 5, “Stat versus Gut”).

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Acknowledgement

Thanks to Emiliano Ippoliti for organizing this stimulating conference, and thanks to him and to Fabio Sterpetti for their infinite patience and for work on the volume. Thanks also to Markus Kemmelmeier for a helpful comment.

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  1. University of Nevada, Reno, NV, USA

    Thomas Nickles

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  1. Thomas Nickles
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Editors and Affiliations

  1. Dipartimento di Filosofia, Sapienza University of Rome, Rome, Italy

    Emiliano Ippoliti

  2. Dipartimento di Filosofia, Sapienza University of Rome, Rome, Italy

    Fabio Sterpetti

  3. Department of Philosophy, University of Nevada, Reno, Nevada, USA

    Thomas Nickles

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Nickles, T. (2016). Fast and Frugal Heuristics at Research Frontiers. In: Ippoliti, E., Sterpetti, F., Nickles, T. (eds) Models and Inferences in Science. Studies in Applied Philosophy, Epistemology and Rational Ethics, vol 25. Springer, Cham. https://doi.org/10.1007/978-3-319-28163-6_3

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