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Conor Mayo-Wilson
University of Washington
  1. The Independence Thesis: When Individual and Social Epistemology Diverge.Conor Mayo-Wilson, Kevin J. S. Zollman & David Danks - 2011 - Philosophy of Science 78 (4):653-677.
    In the latter half of the twentieth century, philosophers of science have argued (implicitly and explicitly) that epistemically rational individuals might compose epistemically irrational groups and that, conversely, epistemically rational groups might be composed of epistemically irrational individuals. We call the conjunction of these two claims the Independence Thesis, as they together imply that methodological prescriptions for scientific communities and those for individual scientists might be logically independent of one another. We develop a formal model of scientific inquiry, define four (...)
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  2. Scoring Imprecise Credences: A Mildly Immodest Proposal.Conor Mayo-Wilson & Gregory Wheeler - 2016 - Philosophy and Phenomenological Research 92 (1):55-78.
    Jim Joyce argues for two amendments to probabilism. The first is the doctrine that credences are rational, or not, in virtue of their accuracy or “closeness to the truth” (1998). The second is a shift from a numerically precise model of belief to an imprecise model represented by a set of probability functions (2010). We argue that both amendments cannot be satisfied simultaneously. To do so, we employ a (slightly-generalized) impossibility theorem of Seidenfeld, Schervish, and Kadane (2012), who show that (...)
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  3.  40
    Scientific Collaboration and Collective Knowledge.Thomas Boyer-Kassem, Conor Mayo-Wilson & Michael Weisberg (eds.) - 2017 - New York, USA: Oxford University Press.
    Current scientific research almost always requires collaboration among several (if not several hundred) specialized researchers. When scientists co-author a journal article, who deserves credit for discoveries or blame for errors? How should scientific institutions promote fruitful collaborations among scientists? In this book, leading philosophers of science address these critical questions.
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  4. Wisdom of the Crowds Vs. Groupthink: Learning in Groups and in Isolation.Conor Mayo-Wilson, Kevin Zollman & David Danks - 2013 - International Journal of Game Theory 42 (3):695-723.
    We evaluate the asymptotic performance of boundedly-rational strategies in multi-armed bandit problems, where performance is measured in terms of the tendency (in the limit) to play optimal actions in either (i) isolation or (ii) networks of other learners. We show that, for many strategies commonly employed in economics, psychology, and machine learning, performance in isolation and performance in networks are essentially unrelated. Our results suggest that the appropriateness of various, common boundedly-rational strategies depends crucially upon the social context (if any) (...)
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  5.  21
    The Computational Philosophy: Simulation as a Core Philosophical Method.Conor Mayo-Wilson & Kevin J. S. Zollman - 2021 - Synthese 199 (1-2):3647-3673.
    Modeling and computer simulations, we claim, should be considered core philosophical methods. More precisely, we will defend two theses. First, philosophers should use simulations for many of the same reasons we currently use thought experiments. In fact, simulations are superior to thought experiments in achieving some philosophical goals. Second, devising and coding computational models instill good philosophical habits of mind. Throughout the paper, we respond to the often implicit objection that computer modeling is “not philosophical.”.
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  6. Structural Chaos.Conor Mayo-Wilson - 2015 - Philosophy of Science 82 (5):1236-1247.
    A dynamical system is called chaotic if small changes to its initial conditions can create large changes in its behavior. By analogy, we call a dynamical system structurally chaotic if small changes to the equations describing the evolution of the system produce large changes in its behavior. Although there are many definitions of “chaos,” there are few mathematically precise candidate definitions of “structural chaos.” I propose a definition, and I explain two new theorems that show that a set of models (...)
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  7.  69
    Reliability of Testimonial Norms in Scientific Communities.Conor Mayo-Wilson - 2014 - Synthese 191 (1):55-78.
    Several current debates in the epistemology of testimony are implicitly motivated by concerns about the reliability of rules for changing one’s beliefs in light of others’ claims. Call such rules testimonial norms (tns). To date, epistemologists have neither (i) characterized those features of communities that influence the reliability of tns, nor (ii) evaluated the reliability of tns as those features vary. These are the aims of this paper. I focus on scientific communities, where the transmission of highly specialized information is (...)
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  8. Epistemic Decision Theory's Reckoning.Conor Mayo-Wilson & Gregory Wheeler - manuscript
    Epistemic decision theory (EDT) employs the mathematical tools of rational choice theory to justify epistemic norms, including probabilism, conditionalization, and the Principal Principle, among others. Practitioners of EDT endorse two theses: (1) epistemic value is distinct from subjective preference, and (2) belief and epistemic value can be numerically quantified. We argue the first thesis, which we call epistemic puritanism, undermines the second.
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  9.  84
    The Limits of Piecemeal Causal Inference.Conor Mayo-Wilson - 2014 - British Journal for the Philosophy of Science 65 (2):213-249.
    In medicine and the social sciences, researchers must frequently integrate the findings of many observational studies, which measure overlapping collections of variables. For instance, learning how to prevent obesity requires combining studies that investigate obesity and diet with others that investigate obesity and exercise. Recently developed causal discovery algorithms provide techniques for integrating many studies, but little is known about what can be learned from such algorithms. This article argues that there are causal facts that one could learn by conducting (...)
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  10. Russell on Logicism and Coherence.Conor Mayo-Wilson - 2011 - Russell: The Journal of Bertrand Russell Studies 31 (1):89-106.
    According to Quine, Charles Parsons, Mark Steiner, and others, Russell's logicist project is important because, if successful, it would show that mathematical theorems possess desirable epistemic properties often attributed to logical theorems, such as a prioricity, necessity, and certainty. Unfortunately, Russell never attributed such importance to logicism, and such a thesis contradicts Russell's explicitly stated views on the relationship between logic and mathematics. This raises the question: what did Russell understand to be the philosophical importance of logicism? Building on recent (...)
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  11. The Problem of Piecemeal Induction.Conor Mayo-Wilson - 2011 - Philosophy of Science 78 (5):864-874.
    It is common to assume that the problem of induction arises only because of small sample sizes or unreliable data. In this paper, I argue that the piecemeal collection of data can also lead to underdetermination of theories by evidence, even if arbitrarily large amounts of completely reliable experimental and observational data are collected. Specifically, I focus on the construction of causal theories from the results of many studies (perhaps hundreds), including randomized controlled trials and observational studies, where the studies (...)
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    Causal Conclusions That Flip Repeatedly and Their Justification.Kevin T. Kelly & Conor Mayo-Wilson - 2010 - Proceedings of the Twenty Sixth Conference on Uncertainty in Artificial Intelligence 26:277-286.
    Over the past two decades, several consistent procedures have been designed to infer causal conclusions from observational data. We prove that if the true causal network might be an arbitrary, linear Gaussian network or a discrete Bayes network, then every unambiguous causal conclusion produced by a consistent method from non-experimental data is subject to reversal as the sample size increases any finite number of times. That result, called the causal flipping theorem, extends prior results to the effect that causal discovery (...)
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  13.  64
    Ockham Efficiency Theorem for Stochastic Empirical Methods.Kevin T. Kelly & Conor Mayo-Wilson - 2010 - Journal of Philosophical Logic 39 (6):679-712.
    Ockham’s razor is the principle that, all other things being equal, scientists ought to prefer simpler theories. In recent years, philosophers have argued that simpler theories make better predictions, possess theoretical virtues like explanatory power, and have other pragmatic virtues like computational tractability. However, such arguments fail to explain how and why a preference for simplicity can help one find true theories in scientific inquiry, unless one already assumes that the truth is simple. One new solution to that problem is (...)
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  14.  34
    Statistical Inference as Severe Testing: How to Get Beyond the Statistics.Conor Mayo-Wilson - 2021 - Philosophical Review 130 (1):185-189.
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    Collectivist Foundations for Bayesian Statistics.Conor Mayo-Wilson & Aditya Saraf - unknown
    What justifies the use of Bayesian statistics in science? The traditional answer is that Bayesian statistics is simply an instance of orthodox expected utility theory. Thus, Bayesian statistical methods, like principles of utility theory, are justified by norms of individual rationality. In particular, most Bayesians argue that a scientist's credences must satisfy the probability axioms if she adheres to norms of practical and epistemic rationality. We argue that, to justify Bayesian statistics as a tool for science, it is necessary that (...)
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    A New Graduate Reader in Formal Epistemology: Arlo-Costa, Hendricks, van Bentham, Boensvang, and Rendsvig : Readings in Formal Epistemology. Dordrecht: Springer, Xxiii + 937 Pp, $129 HB. [REVIEW]Conor Mayo-Wilson - 2017 - Metascience 26 (2):241-243.
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  17.  14
    Causal Identifiability and Piecemeal Experimentation.Conor Mayo-Wilson - 2019 - Synthese 196 (8):3029-3065.
    In medicine and the social sciences, researchers often measure only a handful of variables simultaneously. The underlying assumption behind this methodology is that combining the results of dozens of smaller studies can, in principle, yield as much information as one large study, in which dozens of variables are measured simultaneously. Mayo-Wilson :864–874, 2011, Br J Philos Sci 65:213–249, 2013. https://doi.org/10.1093/bjps/axs030) shows that assumption is false when causal theories are inferred from observational data. This paper extends Mayo-Wilson’s results to cases in (...)
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