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Meta-Heuristic Strategies in Scientific Judgment Western University Scholarship@Western Electronic Thesis and Dissertation Repository October 2011 Meta-heuristic Strategies in Scientific Judgment Spencer P. Hey University of Western Ontario Supervisor Charles Weijer The University of Western Ontario Graduate Program in Philosophy A thesis submitted in partial fulfillment of the equirr ements for the degree in Doctor of Philosophy © Spencer P. Hey 2011 Follow this and additional works at: https://ir.lib.uwo.ca/etd Part of the Philosophy of Science Commons Recommended Citation Hey, Spencer P., "Meta-heuristic Strategies in Scientific Judgment" (2011). Electronic Thesis and Dissertation Repository. 292. https://ir.lib.uwo.ca/etd/292 This Dissertation/Thesis is brought to you for free and open access by Scholarship@Western. It has been accepted for inclusion in Electronic Thesis and Dissertation Repository by an authorized administrator of Scholarship@Western. For more information, please contact [email protected]. META-HEURISTIC STRATEGIES IN SCIENTIFIC JUDGMENT (Thesis format: Monograph) by Spencer Hey Graduate Program in Philosophy A thesis submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy The School of Graduate and Postdoctoral Studies The University of Western Ontario London, Ontario, Canada c Spencer Phillips Hey 2011 THE UNIVERSITY OF WESTERN ONTARIO School of Graduate and Postdoctoral Studies CERTIFICATE OF EXAMINATION Examiners: Supervisor: ..................... ..................... Dr. C. Smeenk Dr. C. Weijer Supervisory Committee: ..................... Dr. G. Barker ..................... Dr. R. Batterman ..................... Dr. W. Wimsatt ..................... Dr. G. Barker ..................... Dr. M. Speechley The thesis by Spencer Phillips Hey entitled: Meta-heuristic Strategies in Scientific Judgment is accepted in partial fulfillment of the requirements for the degree of Doctor of Philosophy ............... .............................. Date Chair of the Thesis Examination Board ii Abstract In the first half of this dissertation, I develop a heuristic methodology for analyzing sci- entific solutions to the problem of underdetermination. Heuristics are rough-and-ready proce- dures used by scientists to construct models, design experiments, interpret evidence, etc. But as powerful as they are, heuristics are also error-prone. Therefore, I argue that they key to prudently using a heuristic is the articulation of meta-heuristics—guidelines to the kinds of problems for which a heuristic is well- or ill-suited. Given that heuristics will introduce certain errors into our scientific investigations, I empha- size the importance of a particular category of meta-heuristics involving the search for robust evidence. Robustness is understood to be the epistemic virtue bestowed by agreement amongst multiple modes of determination. The more modes we have at our disposal, and the more these confirm the same result, the more confident can we be that a result is not a mere artifact of some heuristic simplification. Through an analysis of case-studies in the philosophy of biology and clinical trials, I develop a principled method for modeling and evaluating heuristics and robustness claims in a qualitative problem space. The second half of the dissertation deploys the heuristic methodology to address ethical and epistemological issues in the science of clinical trials. To that end, I develop a network model for the problem space of clinical research, capable of representing the various kinds of experiments, epistemic relationships, and ethical justifications intrinsic to the domain. I then apply this model to ongoing research with the antibacterial agent, moxifloxacin, for the treatment of tuberculosis, tracking its development from initially successful and promising in vitro and animal studies to its disappointing and discordant performance across five human efficacy trials. Given this failure to find a robust result with moxifloxacin across animal and human studies, what should researchers now do? While my final analysis of this case does not definitively answer that question, I demonstrate how my methodology, unlike a statistical meta-analysis, helps to clarify the directions for further research. Keywords: Heuristics, robustness, epistemology, methodology, clinical trials, Duhem iii Contents Certificate of Examination ii Abstract iii List of Figures vi List of Tables vii 1 Duhem’s Solution 1 1.1 What is Good Sense? . 3 1.2 The Duhem Problem “Solved” by Bayesian Means . 8 1.3 Heuristics, Meta-heuristics, and Problem Space . 10 2 Robustness and Its Critics 26 2.1 The Language of Robustness . 29 2.2 Independence or Diversity . 33 2.3 Discordance and Relevance . 44 3 Meta-heuristics in the Group Selection Controversy 49 3.1 Wright’s and Maynard Smith’s Models . 51 3.2 Wade’s Experiment and Analysis . 59 3.3 Explanatory Pathways and Group Selection’s Problem Space . 65 4 Assay Sensitivity and the Ethics of Placebo-Controlled Clinical Trials 72 4.1 Background . 74 4.2 Sound Methodological Reasoning? . 80 4.3 Assay Sensitivity Revised . 87 4.4 Robustness and a Clinical Research Program . 91 5 The Aim and Structure of Clinical Research 99 5.1 The Moxifloxacin-TB Problem Space . 101 5.2 Alternative Analyses . 112 5.3 Animal Models and Human Models . 116 6 A Philosophy of Scientific Judgment 123 6.1 Solving the Problem of Undetermination . 123 6.2 The Strategy of Robustness . 126 iv 6.3 Modeling a Research Program’s Problem Space . 128 6.4 Future Work . 129 Bibliography 132 Curriculum Vitae 142 v List of Figures 1.1 Problem space for mathematical explanation . 15 1.2 Fast-and-frugal tree for medical decision-making . 17 1.3 Problem space for improving ICU assignment . 18 1.4 Problem space for improving physician behavior . 19 1.5 Problem space for ecological management . 24 2.1 Problem space for robustness and stability considerations in modeling . 33 2.2 Blank PRECIS “wheel” . 38 2.3 Example PRECIS graph for 2 trials . 39 2.4 Problem space for modeling strategies in population biology . 43 3.1 Maynard Smith’s “haystack model”, random mating . 54 3.2 Maynard Smith’s “haystack model”, r = 0:2................... 55 3.3 Maynard Smith’s “haystack model”, r = 0:8................... 56 3.4 Wade’s experimental group treatments . 60 3.5 Griesemer and Wade’s pathways of explanation . 66 3.6 Problem space for group selection studies . 68 4.1 PRECIS graph of a mixed trial . 90 4.2 Model of problem space for a clinical research program . 93 4.3 Robustness PRECIS graph for 3 Trials . 96 5.1 Model of moxifloxacin-TB problem space . 102 vi List of Tables 2.1 PRECIS dimensions . 37 2.2 Biological modeling dimensions . 42 3.1 Wright’s group selection model . 52 5.1 Summary of pre-clinical moxifloxacin-TB studies . 104 5.2 Summary of phase I and II moxifloxacin-TB studies . 105 vii Chapter 1 Duhem’s Solution Pierre Duhem observed that even the best designed experiments never test hypotheses in iso- lation. It is the experiment as a whole—a combination of theories, hypotheses, and auxiliary assumptions—that confronts the evidence. A recalcitrant experimental result is therefore never enough, on its own, to provide a conclusive refutation of any single hypothesis; evidence is never sufficient to prescribe how a theory ought to be constructed or revised. So how does a scientist go about modifying her theories? And more importantly, how should she go about it? Philosophers of science have made much of this “Duhem problem”. However, most have overlooked the fact that Duhem also proposed a solution.1 Scientific judgments are underde- termined, but it does not follow that they must be irrational or arbitrary. For Duhem, underde- termination simply meant that judgments cannot be made on the basis of logic. Instead, they must rely on what he calls “good sense”. The positivist’s distinction between the context of discovery and the context of justification, combined with the fact that Duhem does not say very much about “good sense” in his Aim and Structure of Physical Theory, provides a plausible explanation for philosophers overlooking his solution. “Good sense”, whatever it may be, is only relevant to the context of discovery, which is assumed to be irrational and therefore irrelevant to philosophical reflection. Although this dubious distinction in contexts is no longer as dominant in the philosophy of science, a rigorous analysis of the “intuitive” judgments contributing to scientific practice; judgments fulfilling the promises of Duhemian good sense, has yet to appear. The aim of this thesis is to provide exactly this rigorous analysis: What is Duhemian good sense? How does it function in 1It is worth noting that a persistent state of confusion surrounds philosophical discussion of “Duhem’s prob- lem” or “the Duhem thesis”. Much of the attention paid to this subject in the 20th century confuses Duhem’s ideas with those of W. V. O. Quine, substituting the former’s rather modest form of underdetermination for ver- sions of the latter’s more radical holism. Although there are now a number of excellent and clarifying responses to this confusion (cf. Laudan 1965, Ariew 1984, Needham 2000, Darling 2002), it bears repeating that the rad- ical “Duhem-Quine Thesis”, that hypotheses can be maintained in the face of any evidence whatsoever, is not a position Duhem held. 2 Chapter 1. Duhem’s Solution scientific practice? How can we develop the concept further to aid in scientific progress? But I should be clear: This is not a historical project. Duhem’s ideas about underdeter- mination and good sense will largely serve as a philosophical touchstone, an epistemological problem that will be familiar to many philosophers of science. The next section will be the most historical, as I will show that Duhem’s concept of good sense is not mere hand-waving, but is fundamental to his picture of scientific practice. But the majority of my work is going well beyond Duhem’s basic idea. Specifically, I will argue that we can elucidate his conception of good sense with contemporary work on heuristics.
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