After some general remarks about the interrelation between philosophical and statistical thinking, the discussion centres largely on significance tests. These are defined as the calculation of p-values rather than as formal procedures for ‘acceptance‘ and ‘rejection‘. A number of types of null hypothesis are described and a principle for evidential interpretation set out governing the implications of p- values in the specific circumstances of each application, as contrasted with a long-run interpretation. A number of more complicated situ- ations are (...) discussed in which modification of the simple p-value may be essential. (shrink)

This book analyzes the origins of statistical thinking as well as its related philosophical questions, such as causality, determinism or chance. Bayesian and frequentist approaches are subjected to a historical, cognitive and epistemological analysis, making it possible to not only compare the two competing theories, but to also find a potential solution. The work pursues a naturalistic approach, proceeding from the existence of numerosity in natural environments to the existence of contemporary formulas and methodologies to heuristic pragmatism, a concept (...) introduced in the book's final section. This monograph will be of interest to philosophers and historians of science and students in related fields. Despite the mathematical nature of the topic, no statistical background is required, making the book a valuable read for anyone interested in the history of statistics and human cognition. (shrink)

Gigerenzer’s ‘external validity argument’ plays a pivotal role in his critique of the heuristics and biases research program (HB). The basic idea is that (a) the experimental contexts deployed by HB are not representative of the real environment and that (b) the differences between the setting and the real environment are causally relevant, because they result in different performances by the subjects. However, by considering Gigerenzer’s work on frequencies in probability judgments, this essay attempts to show that there are fatal (...) flaws in the argument. Specifically, each of the claims is controversial: whereas (b) is not adequately empirically justified, (a) is inconsistent with the ‘debiasing’ program of Gigerenzer’s ABC group. Therefore, whatever reason we might have for believing that the experimental findings of HB lack experimental validity, this should not be based on Gigerenzer’s version of the argument. (shrink)

Frequentism and Bayesianism represent very different approaches to hypothesis testing, and this presents a skeptical challenge for Bayesians. Given that most empirical research uses frequentist methods, why (if at all) should we rely on it? While it is well known that there are conditions under which Bayesian and frequentist methods agree, without some reason to think these conditions are typically met, the Bayesian hasn’t shown why we are usually safe in relying on results reported by significance testers. In this article, (...) I provide arguments that such conditions will usually be met; the Bayesian can maintain her theoretical disagreement with the frequentist while holding that her error is mostly harmless in practice. (shrink)

Cancer research is experiencing ‘paradigm instability’, since there are two rival theories of carcinogenesis which confront themselves, namely the somatic mutation theory and the tissue organization field theory. Despite this theoretical uncertainty, a huge quantity of data is available thanks to the improvement of genome sequencing techniques. Some authors think that the development of new statistical tools will be able to overcome the lack of a shared theoretical perspective on cancer by amalgamating as many data as possible. We think instead (...) that a deeper understanding of cancer can be achieved by means of more theoretical work, rather than by merely accumulating more data. To support our thesis, we introduce the analytic view of theory development, which rests on the concept of plausibility, and make clear in what sense plausibility and probability are distinct concepts. Then, the concept of plausibility is used to point out the ineliminable role played by the epistemic subject in the development of statistical tools and in the process of theory assessment. We then move to address a central issue in cancer research, namely the relevance of computational tools developed by bioinformaticists to detect driver mutations in the debate between the two main rival theories of carcinogenesis. Finally, we briefly extend our considerations on the role that plausibility plays in evidence amalgamation from cancer research to the more general issue of the divergences between frequentists and Bayesians in the philosophy of medicine and statistics. We argue that taking into account plausibility-based considerations can lead to clarify some epistemological shortcomings that afflict both these perspectives. (shrink)

This paper aims to investigate the so-called paradox of deterministic probabilities: in a deterministic world, all probabilities should be subjective; however, they also seem to play important explanatory and predictive roles which suggest they are objective. The problem is then to understand what these deterministic probabilities are. Recent proposed solutions of this paradox are the Mentaculus vision, the range account of probability, and a version of frequentism based on typicality. All these approaches aim at defining deterministic objective probabilities as to (...) make them compatible with determinism. In this paper I argue that one can think of the equivalent of subjective and objective deterministic probabilities in terms of typicality. Also, I show that only what I identify with objective probabilities play the necessary explanatory and predictive roles, while the subjective components essentially guide beliefs. In this way, the paradox is solved. (shrink)

In what follows, I suggest that it makes good sense to think of the truth of the probabilistic generalizations made in the life sciences as metaphysically grounded in stochastic mechanisms in the world. To further understand these stochastic mechanisms, I take the general characterization of mechanism offered by MDC :1–25, 2000) and explore how it fits with several of the going philosophical accounts of chance: subjectivism, frequentism, Lewisian best-systems, and propensity. I argue that neither subjectivism, frequentism, nor a best-system-style interpretation (...) of chance will give us what we need from an account of stochastic mechanism, but some version of propensity theory can. I then draw a few important lessons from recent propensity interpretations of fitness in order to present a novel propensity interpretation of stochastic mechanism according to which stochastic mechanisms are thought to have probabilistic propensities to produce certain outcomes over others. This understanding of stochastic mechanism, once fully fleshed-out, provides the benefits of allowing the stochasticity of a particular mechanism to be an objective property in the world, a property investigable by science, a way of quantifying the stochasticity of a particular mechanism, and a way to avoid a problematic commitment to the causal efficacy of propensities. (shrink)

Statistical tests of the primality of some numbers look similar to statistical tests of many nonmathematical, clearly empirical propositions. Yet interpretations of probability prima facie appear to preclude the possibility of statistical tests of mathematical propositions. For example, it is hard to understand how the statement that n is prime could have a frequentist probability other than 0 or 1. On the other hand, subjectivist approaches appear to be saddled with ‘coherence’ constraints on rational probabilities that require rational agents to (...) assign extremal probabilities to logical and mathematical propositions. In the light of these problems, many philosophers have come to think that there must be some way to generalize a Bayesian statistical account. In this article I propose that a classical frequentist approach should be reconsidered. I conclude that we can give a conditional justification of statistical testing of at least some mathematical hypotheses: if statistical tests provide us with reasons to believe or bet on empirical hypotheses in the standard situations, then they also provide us with reasons to believe or bet on mathematical hypotheses in the structurally similar mathematical cases. (shrink)

Evolutionary theory offers the possibility of building an epistemology that requires neither a theory of truth nor a definition of knowledge, thus bypassing some of the more notable difficulties with standard approaches to epistemology. Following a critique of one of the most popular approaches to thinking about cultural evolution I argue for a frequentist approach to evolutionary epistemology, and that cultural transmission should be understood as coordinated phenotypic variability within groups of closely related organisms. I construct a formal system (...) which allows the analysis of the evolution of networks of multiple interacting populations. Of particular epistemological interest is the relationship between genetic evolution and the evolution of cultural entities. The information-theoretic notion of "mutual information" is employed to quantify the transfer of information from the environment as it determines genetic fitnesses into the distribution of acquired traits Two simple models are constructed and analyzed. A two-level model of the navigation of a colony of bacteria demonstrates how selection on inherited traits affects the acquisition of information on the level of transient behavioral traits. A three-level model of bumblebee foraging shows how information about the environment is caught up in distributions of acquired foraging preferences--a bee-analog of human cognitive states. The Appendix contains an analytic proof of information gain under selection. (shrink)

Una cuestión controvertida en la teoría bayesiana de la confirmación es el estatus de las probabilidades iniciales. Aunque la tendencia dominante entre los bayesianos es considerar que la única constricción legítima sobre los valores de dichas probabilidades es la consistencia formal con los teoremas de la teoría matemática de la probabilidad, otros autores -partidarios de lo que se ha dado en llamar "bayesianismo objetivo"- defienden la conveniencia de restricciones adicionales. Mi propuesta, en el marco del bayesianismo objetivo, recoge una sugerencia (...) de Wesley Salmon. No obstante, mientras que para Salmon las probabilidades iniciales se justifican en tanto incorporan la frecuencia de éxito de las hipótesis del pasado, el factor decisivo en mi opinión es el éxito probabilístico de las hipótesis del pasado. Así, la asignación de una probabilidad inicial a una hipótesis h será correcta si refleja el éxito probabilístico de hipótesis del pasado semejantes a h. En esta línea, propongo una regla para fijar las probabilidades iniciales de las hipótesis, y acabo discutiendo algunas objeciones generales en contra de mi planteamiento. One of the most controversial issues for Bayesian theory of confirmation concerns the status of prior probabilities. Orthodox Bayesianism claims that logical consistency with the theorems of mathematical theory of probability is the only constraint on prior probabilities, while some authors claim for further requirements. I agree with the latter standpoint. My proposal is inspired by Wesley Salmon's frequentist justification of prior probabilities. According to him, prior probabilities are the best assessments of past scientific hypotheses' success. I think that historical information is relevant here, but I propose to focus on a particular sort of success -i.e., "probabilistic success". I claim that a particular prior for h is correct insofar as it reflects the probabilistic success of past scientific hypotheses similar to h. I also suggest a rule to set the priors for contemporary hypotheses. The closing paragraphs are devoted to some general objections against this proposal. (shrink)

The book deals with the changing nature and with the history of the concept of scientific determinism from the classical mechanics until the time immediately preceding quantum mechanics: such a historical-philosophical reconstruction is aimed at (1) signalizing and overcoming the deficiencies of the received opinion on the topic and (2) understanding better a concept which has influenced science from the beginning. -/- Before dealing with historical matters I develop in the first Chapter a kind of new, three-dimensional “measurement system” for (...) analyzing any concept of scientific determinism: many different concepts have been developed in the course of history, and we need a classification system which, on the one hand, is inclusive and broad enough to deal with different concepts; on the other hand, which makes possible to differentiate with some precision between them. My “measurement system” has three dimension or parameter: the “strength” (of the determining link between different states of the physical system), the “depth” (depending on how strong the ontological commitment of the particular concept of scientific determinism is), and the breadth (referring to the domain or the object, to which a deterministic evolution is ascribed). -/- In the second chapter I discuss briefly some main shortcomings of the received opinion about scientific determinism. Then I try to identify the “core” of scientific determinism at its origins while at the same time embedding it in the broader historical-cultural context especially of the Renaissance. On the one hand I show how the core of scientific determinism was mathematical, and this distinguished it from other related concepts, as mechanism, the principle of sufficient reason, and the aristotelic conception of science. On the other hand, precisely the support which these related concepts provided to scientific determinism, together with its mathematical nature, endowed it with an incredible resistance against empirical classification. -/- In the following three chapters I analyze the historic-philosophical development of scientific determinism along the three parameter of my measurement system. -/- In the third Chapter I reconstruct the weakening of scientific determinism with respect to its depth: the starting point is the question about the extent in which the deterministic mathematical descriptions do refer. In the development of mechanics into analytical mechanics the more and more formal character of its mathematical descriptions became evident and it became less obvious that these formal, deterministic structures also have an immediate material truth. Parallel developments within epistemology, starting with Kantian philosophy (which I deal with quite in detail), let the deterministic structures (principle, laws, equations, causal relations) appear less absolute and real and more as a product of reason, as conventions, or as models. A brief consideration of the interpretation of mechanics and its principles by Jacobi, Hertz, Mach and Poincaré shows the implications of these developments. -/- The fourth chapter reconstructs the weakening of scientific determinism with respect to the strength of the determining link: here my work deepens the track which has been opened by Ian Hacking, who interpreted the so-called “probabilistic revolution” in the 19th Century as the main reason for the erosion of determinism. The increasing pervasiveness of statistical methods in the social and natural sciences in the course of the 19th Century gave rise to a conception of scientific laws which could dispense with strict determinism. A new, empirical interpretation of probability (Frequentism) and the statistical explanation of thermodynamic phenomena in physics even suggested chance phenomena to be a necessary condition for the emergence of natural laws. -/- The last part of my work (Ch. 5, 6 and 7) considers the period (2nd half of the 19th century until ca. 1920) in which the concept of scientific determinism became explicit and was discussed as a world-picture or a world-view – that is, in its maximal breadth. I argue that there were two main reasons for the emergence of an explicit and ideological opposition “determinism vs. indeterminism” at that time: the first was the successful application of the deterministic paradigm to sociology, history, physiology and psychology in the course of the 19th Century, which provided scientific determinism with ethical implications (in particular with respect to the problem of free will, which scientific determinism seemed to deny). The second, related reason is that in the 19th Century natural scientists became public men, science was increasingly popularized and scientific issues were increasingly related to life-issues, to worldview-questions, and even to politics. In Chapter 6 I reconstruct the debates on the issue “determinism vs. indeterminism” in such a public, ideological and sometimes even political context. Among the discussants were Fechner, Du Bois-Reymond, Helmholtz, Bernard, Ostwald, Haeckel, Boussinesq, Maxwell, Boutroux, Poincaré, Renouvier, James und Peirce. Also in Vienna the debate on the issue of determinism was fervid and took ideological and political connotations: Michael Stöltzner and Deborah Coen have pointed to a particular tradition of “Vienna Indeterminism” (Stöltzner), or Viennese liberal probabilism (Coen), which was characterized by a strongly empirical conception of science and by the full acceptance and appreciation of statistical thinking in science. In the last Chapter of my work I focus on the early philosophy of Edgar Zilsel, a philosopher who stood near to the Vienna Circle and who has been much neglected in the literature by now, and I suggest considering it as part of the “Vienna Indeterminism”. First, I show how he gave an indeterministic foundation both to statistical and causal knowledge as well as to irreversibility in physics. Second, I inquire into his philosophy of probability and show how it developed in parallel to the ones of Hans Reichenbach and Richard von Mises. Finally, Zilsel happens to be a further, relevant case-study for pointing to the ideological and political side of the issue of determinism. -/- At the end of this reconstruction it should have become clear how no physical theory, no empirical evidence and no experimental confirmation can support or contradict scientific determinism in its non-trivial formulations. The validity of any such formulation depends strongly not only (from the systematical point of view) from the definition of other concepts (like time, physical state, causality etc.), but also from the particular conception of science which more or less implicitly constitutes its background, and which is subjected to historical change. -/- (ISBN: 9783495491034). (shrink)

Philosophers now seem to agree that frequentism is an untenable strategy to explain the meaning of probabilities. Nevertheless, I want to revive frequentism, and I will do so by grounding probabilities on typicality in the same way as the thermodynamic arrow of time can be grounded on typicality within statistical mechanics. This account, which I will call typicality frequentism, will evade the major criticisms raised against previous forms of frequentism. In this theory, probabilities arise within a physical theory from statistical (...) behavior of almost all initial conditions. The main advantage of typicality frequentism is that it shows which kinds of probabilities (that also have empirical relevance) can be derived from physics. Although one cannot recover all probability talk in this account, this is rather a virtue than a vice, because it shows which types of probabilities can in fact arise from physics and which types need to be explained in different ways, thereby opening the path for a pluralistic account of probabilities. (shrink)

The orthodox view in statistics has it that frequentism and Bayesianism are diametrically opposed—two totally incompatible takes on the problem of statistical inference. This paper argues to the contrary that the two approaches are complementary and need to mesh if probabilistic reasoning is to be carried out correctly.

The view that chances are relative frequencies of occurrence within actual, finite reference classes has long been written off. I argue that it ought to be reconsidered. Focusing on non-deterministic chance, I defend a version of finite frequentism in which reference classmates are required to have qualitatively identical pasts. While my analysis can evade or resist several standard objections, it has a counterintuitive consequence: non-trivial chances entail the existence of past light cones that are perfect intrinsic duplicates. In mitigation, I (...) argue that our scientific knowledge is consistent with the hypothesis that there are many such duplicates in the actual world. Moreover, my analysis has some striking advantages: it is simple, it is metaphysically undemanding, and it makes possible a satisfying explanation of the chance–credence connection. 1 Introduction2 Target3 Sketch and Skirmish4 Articulation5 Standard Worries5.1 Frequency tolerance5.2 Leibniz’s dictum5.3 Single case chance5.4 Missing values5.5 Explanation5.6 Counterfactual chances6 Chance and Credence6.1 An indefeasible indifference intuition6.2 Articulating the credence principle6.3 Appraising the credence principle7 Confirmation8 Laws9 Summing Up. (shrink)

In this paper I provide a frequentist philosophical-methodological solution for the stopping rule problem presented by Lindley & Phillips in 1976, which is settled in the ecological realm of testing koalas’ sex ratio. I deliver criteria for discerning a stopping rule, an evidence and a model that are epistemically more appropriate for testing the hypothesis of the case studied, by appealing to physical notion of probability and by analyzing the content of possible formulations of evidence, assumptions of models and meaning (...) of the ecological hypothesis. First, I show the difference in the evidence taken into account in different frequentist sampling procedures presented in the problem. Next, I discuss the inapplicability of the Carnapian principle of total evidence in deciding which formulation of evidence associated with a given sampling procedure and statistical model is epistemically more appropriate for testing the hypothesis in question. Then I propose a double-perspective (evidence and model) frequentist solution based on the choice of evidence which better corresponds to the investigated ecological hypothesis, as well as on the choice of a model that embraces less unrealistic ontological assumptions. Finally, I discuss two perspectives of the stopping rule dependence. (shrink)

In this paper a theory of finitistic and frequentistic approximations — in short: f-approximations — of probability measures P over a countably infinite outcome space N is developed. The family of subsets of N for which f-approximations converge to a frequency limit forms a pre-Dynkin system $${{D\subseteq\wp(N)}}$$. The limiting probability measure over D can always be extended to a probability measure over $${{\wp(N)}}$$, but this measure is not always σ-additive. We conclude that probability measures can be regarded as idealizations (...) of limiting frequencies if and only if σ-additivity is not assumed as a necessary axiom for probabilities. We prove that σ-additive probability measures can be characterized in terms of so-called canonical and in terms of so-called full f-approximations. We also show that every non-σ-additive probability measure is f-approximable, though neither canonically nor fully f-approximable. Finally, we transfer our results to probability measures on open or closed formulas of first-order languages. (shrink)

I will open the first part of this paper by trying to elucidate the frequentist foundations of RCTs. I will then present a number of methodological objections against the viability of these inferential principles in the conduct of actual clinical trials. In the following section, I will explore the main ethical issues in frequentist trials, namely those related to randomisation and the use of stopping rules. In the final section of the first part, I will analyse why RCTs were accepted (...) for regulatory purposes. I contend that their main virtue, from a regulatory viewpoint, is their impartiality, which is grounded in randomisation and fixed rules for the interpretation of the experiment. Thus the question will be whether Bayesian trials can match or exceed the achievements of frequentist RCTs in all these respects. In the second part of the paper, I will first present a quick glimpse of the introduction of Bayesianism in the field of medical experiments, followed by a summary presentation of the basic tenets of a Bayesian trial. The point here is to show that there is no such thing as “a” Bayesian trial. Bayesianism can ground many different approaches to medical experiments and we should assess their respective virtues separately. Thus I present two actual trials, planned with different goals in mind, and assess their respective epistemic, ethical and regulatory merits. In a tentative conclusion, I contend that, given the constraints imposed by our current regulatory framework, impartiality should preside over the design of clinical trials, even at the expense of many of their inferential and ethical virtues. (shrink)

Frequentist inference typically is described in terms of hypothetical repeated sampling but there are advantages to an interpretation that uses a single random sample. Contemporary examples are given that indicate probabilities for random phenomena are interpreted as classical probabilities, and this interpretation of equally likely chance outcomes is applied to statistical inference using urn models. These are used to address Bayesian criticisms of frequentist methods. Recent descriptions of p-values, confidence intervals, and power are viewed through the lens of classical probability (...) based on a single random sample from the population. (shrink)

The main objective of the paper is to propose a frequentist interpretation of probability in the context of model-based induction, anchored on the Strong Law of Large Numbers (SLLN) and justifiable on empirical grounds. It is argued that the prevailing views in philosophy of science concerning induction and the frequentist interpretation of probability are unduly influenced by enumerative induction, and the von Mises rendering, both of which are at odds with frequentist model-based induction that dominates current practice. The differences between (...) the two perspectives are brought out with a view to defend the model-based frequentist interpretation of probability against certain well-known charges, including [i] the circularity of its definition, [ii] its inability to assign ‘single event’ probabilities, and [iii] its reliance on ‘random samples’. It is argued that charges [i]–[ii] stem from misidentifying the frequentist ‘long-run’ with the von Mises collective. In contrast, the defining characteristic of the long-run metaphor associated with model-based induction is neither its temporal nor its physical dimension, but its repeatability (in principle); an attribute that renders it operational in practice. It is also argued that the notion of a statistical model can easily accommodate non-IID samples, rendering charge [iii] simply misinformed. (shrink)

This is the sequel to my “Fifteen Arguments Against Finite Frequentism” ( Erkenntnis 1997), the second half of a long paper that attacks the two main forms of frequentism about probability. Hypothetical frequentism asserts: The probability of an attribute A in a reference class B is p iff the limit of the relative frequency of A ’s among the B ’s would be p if there were an infinite sequence of B ’s. I offer fifteen arguments against this analysis. I (...) consider various frequentist responses, which I argue ultimately fail. I end with a positive proposal of my own, ‘hyper-hypothetical frequentism’, which I argue avoids several of the problems with hypothetical frequentism. It identifies probability with relative frequency in a hyperfinite sequence of trials. However, I argue that this account also fails, and that the prospects for frequentism are dim. (shrink)

I investigate the extent to which perspectival realism agrees with frequentist statistical methodology and philosophy, with an emphasis on J. Neyman’s views. Based on the example of the stopping rule problem, I show how PR can naturally be associated with frequentist statistics in general. I also show that there are some aspects of Neyman’s thought that seem to confirm PR and others that disconfirm it. I argue that epistemic PR is consistent with Neyman’s frequentism to a satisfactory degree and that (...) on the grounds of Neyman’s frequentist methodology one is dealing with genuine and non-trivial perspectives. I also argue that universality and normativity of PR are weakened when analysed from the point of view of Neyman’s statistical methodology. (shrink)

This article calls into question the charge that frequentist testing is susceptible to the base-rate fallacy. It is argued that the apparent similarity between examples like the Harvard Medical School test and frequentist testing is highly misleading. A closer scrutiny reveals that such examples have none of the basic features of a proper frequentist test, such as legitimate data, hypotheses, test statistics, and sampling distributions. Indeed, the relevant error probabilities are replaced with the false positive/negative rates that constitute deductive calculations (...) based on known probabilities among events. As a result, the ampliative dimension of frequentist induction—learning from data about the underlying data-generating mechanism—is missing. *Received August 2009; revised January 2010. †To contact the author, please write to: Department of Economics, Virginia Tech, Blacksburg, VA 24061; e-mail: [email protected]. (shrink)

Probability theory is important not least because of its relevance for decision making, which also means: its relevance for the single case. The frequency theory of probability on its own is irrelevant in the single case. However, Howson and Urbach argue that Bayesianism can solve the frequentist's problem: frequentist-probability information is relevant to Bayesians. The present paper shows that Howson and Urbach's solution cannot work, and indeed that no Bayesian solution can work. There is no way to make frequentist probability (...) relevant. (shrink)

Philosophy is the search for the large patterns of the world and of the large patterns of experience, perceptual, theoretical, . . . , aesthetic, and practical - the patterns that, regardless of specific contents, characterize the main types of experience. In this book I carry out my search for the large patterns of practical experience: the experience of deliberation, of recognition of duties and their conflicts, of attempts to guide other person's conduct, of deciding to act, of influencing the (...) physical world with one's doings, and the like. This is the experience that makes possible our social life, the formulation of plans for teamwork, the building of institutions, the development of nations, and the adoption of the ideal of morality. Here I develop a network of theories about the most fundamental aspects of practical thinking: what is thought in such thinking; what makes that thinking correct; what makes it practical; and the structure of the doings that accrue to the world when such thinking is effective. I have attempted to build each theory in sufficient detail, so that it il luminates its subject matter with a certain degree of fullness. But I have also aimed at producing an harmonious system of theories, so that the grand pattern of practical thinking can be admired, not only for the beauty of the separate structures of its parts, but also for its architectonic unity. Chapter 1 gives the details of the many facets of this project and discusses some methodological techniques. (shrink)

Thinking through the body: educating for the humanities -- The body as background -- Self-knowledge and its discontents: from Socrates to somaesthetics -- Muscle memory and the somaesthetic pathologies of everyday life -- Somaesthetics in the philosophy classroom: a practical approach -- Somaesthetics and the limits of aesthetics -- Somaesthetics and Burke's sublime -- Pragmatism and cultural politics: from textualism to somaesthetics -- Body consciousness and performance -- Somaesthetics and architecture: a critical option -- Photography as performative process -- (...) Asian ars erotica and the question of sexual aesthetics -- Philosophy as awakened life : everyday aesthetics of embodiment in American transcendentalism and Japanese Zen practice -- Somatic style. (shrink)

Alfred North Whitehead has never gone out of print, but for a time he was decidedly out of fashion in the English-speaking world. In a splendid work that serves as both introduction and erudite commentary, Isabelle Stengersâe"one of todayâe(tm)s leading philosophers of scienceâe"goes straight to the beating heart of Whiteheadâe(tm)s thought. The product of thirty yearsâe(tm) engagement with the mathematician-philosopherâe(tm)s entire canon, this volume establishes Whitehead as a daring thinker on par with Gilles Deleuze, Felix Guattari, and Michel Foucault. Reading (...) the texts in broadly chronological order while highlighting major works, Stengers deftly unpacks Whiteheadâe(tm)s often complicated language, explaining the seismic shifts in his thinking and showing how he called into question all that philosophers had considered settled after Descartes and Kant. She demonstrates that the implications of Whiteheadâe(tm)s philosophical theories and specialized knowledge of the various sciences come yoked with his innovative, revisionist take on God. Whiteheadâe(tm)s God exists within a specific epistemological realm created by a radically complex and often highly mathematical language. âeoeTo think with Whitehead today,âe Stengers writes, âeoemeans to sign on in advance to an adventure that will leave none of the terms we normally use as they were.âe. (shrink)

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)