The debates between Bayesian, frequentist, and other methodologies of statistics have tended to focus on conceptual justifications, sociological arguments, or mathematical proofs of their long run properties. Both Bayesian statistics and frequentist (“classical”) statistics have strong cases on these grounds. In this article, we instead approach the debates in the “Statistics Wars” from a largely unexplored angle: simulations of different methodologies’ performance in the short to medium run. We conducted a large number of simulations using a straightforward decision problem based (...) around tossing a coin with unknown bias and then placing bets. In this simulation, we programmed four players, inspired by Bayesian statistics, frequentist statistics, Jon Williamson’s version of Objective Bayesianism, and a player who simply extrapolates from observed frequencies to general frequencies. The last player functions as a benchmark: a statistical methodology should at least outperform a crude form of induction. We focused on the performance of these methodologies in guiding the players towards good decisions. Unlike an earlier simulation study of this type, we found no systematic difference in performance between the Bayesian and frequentist players, provided the Bayesian used a flat prior and the frequentist used a low confidence level. Unlike that study, we were able to use Big Data methods to mitigate problems of random error in the simulation results. The Williamsonian player, who is a novel element of our study, also had no systematic differences in their performance, provided that they use a low confidence level. These players performed similarly even in the very short run, when players were making different decisions. Our study indicates that all three methodologies should be taken seriously by philosophers and practitioners of statistics. However, the frequentist and Williamsonian performed poorly when their confidence levels were high, and the Bayesian was surprisingly harmed by biased priors, providing some unexpected lessons for these methodologies when facing this type of decision problem. (shrink)

This paper discusses Kevin Kelly’s recent attempt to justify Ockham’s Razor in terms of truth-finding efficiency. It is argued that Kelly’s justification fails to warrant confidence in the empirical content of theories recommended by Ockham’s Razor. This is a significant problem if, as Kelly and many others believe, considerations of simplicity play a pervasive role in scientific reasoning, underlying even our best tested theories, for the proposal will fail to warrant the use of these theories in practical prediction.

Science advances by means of argument and debate. Based on a formal model of complex argumentation, this article assesses the interplay between evidential and inferential drivers in scientific controversy, and explains, in particular, why both evidence accumulation and argumentation are veritistically valuable. By improving the conditions for applying veritistic indicators , novel evidence and arguments allow us to distinguish true from false hypotheses more reliably. Because such veritistic indicators also underpin inductive reasoning, evidence accumulation and argumentation enhance the reliability of (...) inductive inference, for example, inference to the best explanation. 1 Introduction2 Theory of Dialectical Structures3 Debate Simulations4 From Evidence and Arguments to Truth: The First Route5 From Evidence to Truth: The Second Route6 Conclusion. (shrink)

According to a widely held norm of rationality, one should not change prior credences without new evidence. An important argument for this norm appeals to accuracy considerations, which says that changing priors doesn’t maximize expected accuracy. This is because accuracy measures are strictly proper, and thus any probabilistically coherent person regards her own priors as uniquely maximizing expected accuracy compared with other priors. -/- This paper attempts to resist the accuracy argument against changing priors. We argue that even if rational (...) epistemic decisions maximize expected accuracy according to strictly proper accuracy measures, it can still be rational to change priors sometimes. The core idea of our argument is that changing priors can be rational if one wants to maximize not just one’s current, short-term accuracy but also future, long-term accuracy. Our argument, if successful, shows that considering long-term accuracy has significant ramifications for the accuracy-first project. (shrink)

The combination of logic and game theory provides a fine-grained perspective on information and interaction dynamics, a Theory of Play. In this paper we lay down the main components of such a theory, drawing on recent advances in the logical dynamics of actions, preferences, and information. We then show how this fine-grained perspective has already shed new light on the long-term dynamics of information exchange, as well as on the much-discussed question of extensive game rationality.

"What Is Cognitive Access?" PDF. Behavioral and Brain Sciences 30 (2007 [published 2008]): 505. Brief comments on a paper of Ned Block's. "Mechanical Mind," a review of Mind as Machine: A History of Cognitive Science by Margaret Boden. Online Published Version . From American Scientist (2008): 76-81.

Statistical Learning Theory (e.g., Hastie et al., 2001; Vapnik, 1998, 2000, 2006) is the basic theory behind contemporary machine learning and data-mining. We suggest that the theory provides an excellent framework for philosophical thinking about inductive inference.

Why conduct research concerning human genome or proving the existence of Higgs particle? What makes these problems significant or worthy of investigation? In recent epistemological discussions one can find at least two conceptions of the problem of epistemic significance: research question or cognitive problem can be practically significant or intrinsically epistemically significant, in a way that depends on the consideration whether reasons that support the significance of the problem are practical or epistemic. In this paper I am dealing with the (...) question of the possibility of determining the significance of the problem from the purely epistemic perspective. In that regard I argue that (under suitable interpretation of the problem of epistemic significance) there are no pure epistemic reasons that could determine the importance of the cognitive problem. The argument is based on three objections: (1) epistemic duty is unjustifiably identified with epistemic justification, (2) the dependence of epistemic reasons on conceputal schemes is not taken into consideration, and (3) the infinite justificatory potential of evidence is being disregarded. (shrink)

Like Glenn Shafer, we are nostalgic for the time when “philosophers, mathematicians, and scientists interested in probability, induction, and scientific methodology talked with each other more than they do now”, [p.10]. 1 Shafer goes on to mention other relevant contemporary communities. He himself has been at the interface of many of these communities while at the same time making major contributions to them and this very symposium represents something of that desired discussion. We begin with a couple of general points (...) about issues several commentators have raised and then discuss other more particular issues. (shrink)

Zašto se baviti istraživanjem ljudskog genoma ili dokazivanjem postojanja Higgsove čestice? Što te probleme čini značajnima ili vrijednima istraživanja? U novijim raspravama iz područja epistemologije može se pronaći bar dva shvaćanja problema epistemičke značajnosti: istraživačko pitanje ili spoznajni problem može biti praktično značajan ili intrinzično epistemički značajan, ovisno o tome da li su razlozi koji podržavaju značajnost problema praktični ili epistemički. U ovom radu bavim se pitanjem mogućnosti određivanja značajnosti problema iz čisto epistemičke perspektive. U tom pogledu argumentiram da ne (...) postoje čisto epistemički razlozi koji bi mogli odrediti važnost spoznajnog problema. Argument je baziran na trima prigovorima: epistemička dužnost neopravdano se poistovjećuje s epistemičkim opravdanjem, ne uzima se u obzir ovisnost epistemičkih razloga o konceptualnim shemama i zanemaruje se neograničenost opravdavalačkog potencijala evidencijske građe. (shrink)