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Heuristic Appraisal at the Frontier of Research

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Heuristic Reasoning

Part of the book series: Studies in Applied Philosophy, Epistemology and Rational Ethics ((SAPERE,volume 16))

Abstract

How can we speed up both basic and translational scientific research without major new financial investment? One way is to speed up the process by which good proposals are funded. Another is to do a better job of identifying research that is potentially transformative. There are internal institutional barriers as well as sluggish and conservative policies in place in many government funding agencies, universities, and private firms, policies that are risk-averse and characterized by short-term accounting. While perhaps calling for transformational research, their selection procedures promote normal basic research and translational research instead. This chapter proposes that progress can be made by giving increased weight to heuristic appraisal—appraisal of the future promise of proposed research—and correspondingly less weight to confirmational appraisal—the logical and probabilistic relations between theories and data sets already on the table. Emphasis on the latter, as studied by traditional confirmation theory, is a legacy of logical positivism. Adapting a form of scenario planning from the business community is one positive suggestion.

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Notes

  1. 1.

    This is so not only for analysts who would define truth epistemically as what we get in the limit of successful inquiry but also for those strong realists who reject the epistemological reduction of truth.

  2. 2.

    See [43, 45, 48, 49]. In [45] I spoke too much of expected fertility and gave insufficient emphasis to the mere possibility of breakthroughs that can drive research in crisis situations. It should also be clear that ‘future promise’ is intended to cover a range of possibilities, from strong to weak. Generally, ‘future promise’ cannot be modeled by a probability calculus. Carlo Cellucci and Emiliano Ippoliti are on a better track to speak of “plausibility,” especially for transformative research in the middle and low ranges [6, 7, 25, 26]. But in crisis situations where something truly radical seems called for, even plausibility (as often understood) can be too restrictive.

  3. 3.

    I have written on some of these matters before, e.g., [45], where I called confirmational appraisal epistemic appraisal (EA). That term conceded too much to the traditional view that epistemology begins in context of justification, whereas the very point of my work is to emphasize the epistemological relevance of research decisions and actions prior to the testing phase.

  4. 4.

    Traditionalists are not committed to a completely static conception of confirmation. When I speak of theories, models, etc., already “on the table” I mean only ideas well developed enough to put to some sort of empirical test, not necessarily the proverbial “final product” ready for inclusion in textbooks.

  5. 5.

    See Shapere [66]. Levins [34] famously (and controversially) argued that in ecology one must sacrifice either realism, precision, or generality.

  6. 6.

    Margolis stresses habits of mind and pattern recognition as the keys to cognition. See his [37, 38].

  7. 7.

    Ernan McMullin first used this term, to my knowledge, in [39]. I must have “borrowed” it from him. I should add that he was a more thoroughgoing scientific realist than I am. He is not responsible for my excesses.

  8. 8.

    See Dennett’s “Tower of Generate and Test” in [13], pp. 373 ff.

  9. 9.

    In [22] Godfrey-Smith challenges Jablonka and Lamb’s claims in [27] that evolutionary biology is now undergoing a Kuhnian revolution, on the ground that biology is not so tightly organized as physics. Jablonka and Lamb contend that evolutionary and developmental biology and the study of evolution of culture are now experiencing a multitracked revolution, a significant challenge to evolutionary orthodoxy in the name of evo-devo (evolutionary and developmental biology) and epigenetics. Epigenetics itself is clearly a transformative development, whether or not we should classify it as a Kuhnian revolution.

  10. 10.

    See [31] for details. Examples are easily multiplied in all spheres of innovation. Who could have foreseen the import of 19th-century work on specific heats or spectral lines, or the invention of the transistor by Shockley, Bardeen, and Brattain in the late 1940s? Both have had immense impacts on both basic science and technological innovation.

  11. 11.

    Some qualification is needed here and at other points. To some degree, strong nonlnearity undermines Kuhn's normal-revolutionary distinction.  Since even apparently normal work can trigger a transformative change if the state of the field is just right (the nonlinearity point made earlier), work at this stage can still be strongly directed. The crunch comes in the positive work that aims to re-orient the field.

  12. 12.

    This is an odd interpretation, since NSF was founded to support basic research, not to engage in the applied activities of any number of corporations, government agencies, and non-governmental organizations. Thanks to Kelly Moore for this point.

  13. 13.

    Bas van Fraassen [69, p. 125] both addresses the problem of new theories and sharply rejects traditional confirmation theory: “An almost century-long, practically fruitless effort to codify evidential relations (so-called confirmation theory, a bit of bombast if anything is) should have convinced us that ‘in accord with experience’ is not a simple, uncritically usable notion.”

  14. 14.

    There are several difficulties with their sort of position, in my opinion, besides the usual ones specific to Bayesian approaches [6, 7, 21, 25, 26, 68]. Given that they are not naïve empiricists, it is surprising that empirical curve fitting dominates their examples of breakthrough research and theory dynamics. Their HA component is almost entirely past-directed to what is already “on the table,” thus reducing HA to CA. And they take orthodox science to be stable and robust, not fragile.

  15. 15.

    Subjective Bayesians will respond that all of the HA considerations can be included in the prior probabilities, but this move leaves HA in the traditional domain of individual psychology, without providing further analysis.

  16. 16.

    Cohen [9, p. 286] provides the key quotation: It is “only at remote intervals that we can reasonably expect any sudden and brilliant innovation which shall produce a marked and permanent impress on the character of any branch of knowledge.” The appearance of a “Bacon or a Newton, an Oersted or a Wheatstone, a Davy or a Daguerre, is an occasional phenomenon whose existence and career seem to be specially appointed by Providence, for the purpose of effecting some great important change in the conditions or pursuits of man.” [His main point was] that the “year which has passed has not, indeed, been marked by any of those striking discoveries which at once revolutionize, so to speak, the department of science on which they bear.” Thanks to Devin Bray for the Nietzsche reference.

  17. 17.

    See [7, 20, 25]. On diversity-tolerant coherence see [62, 63].

  18. 18.

    The history of economics and that of philosophy of science are somewhat parallel in leaving innovation out of “theory,” relegating it to “outsider” status as an exogenous factor that occasionally disturbs the equilibrium of the system. See [2, 73] as well as [50].

  19. 19.

    I believe that [14] commits this mistake. The frontiers of our predecessors are usually no longer our frontiers. We have new frontiers, unimagined by our predecessors. The new dispensation may dramatically reorganize the search spaces. For example, today’s epigenetics research largely overturns the old nature-nurture distinction. Search spaces increase exponentially in size as we realize that thousands of genes and epigenetic factors seem to be involved in some phenotypic traits. Ditto for neural networks and their embodiment.

  20. 20.

    Here I am indebted to Harvey Wagner. For a variation on this idea, see his [72] for a description of the Teknekron business plan.

  21. 21.

    Of course, successful technological innovation must also function well enough to enjoy a viable market share, although, thanks to timing and marketing advantages, the objectively superior technology does not always win. Is science different in this respect?

  22. 22.

    Thanks to Don Howard for reminding me of this.

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Acknowledgements

Thanks to Emiliano Ippoliti for organizing the workshop and also to Maria Teresa Cipollone for stimulating questions. Unfortunately, given the length of the present paper, I have had to postpone my published answer to her. I have benefitted from recent correspondence with Britt Holbrook and from sessions on policy at the February 2013 American Association for the Advancement of Science (AAAS) meetings in Boston. My largest debt is to the MIRRORS project at the University of Catania, Italy, which first got me, hesitantly, into the policy business. Thanks there especially to Franco Coniglione, Salvo Vasta, and Enrico Viola (see [10, 70]). Enrico and I continue to collaborate on these issues. And thanks to Roberto Poli for calling my attention to scenario planning some years ago. For helpful discussion of realism issues, i am indebted to my students, especially to Devin Bray.

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    Thomas Nickles

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    Emiliano Ippoliti

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Nickles, T. (2015). Heuristic Appraisal at the Frontier of Research. In: Ippoliti, E. (eds) Heuristic Reasoning. Studies in Applied Philosophy, Epistemology and Rational Ethics, vol 16. Springer, Cham. https://doi.org/10.1007/978-3-319-09159-4_4

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