This article provides a fuller account of purely statistical pattern-level explanations-that is, those that explain macro-level events by invoking limit theorems. The statistical autonomous explanation account is modified via integration with the maximum entropy approach for generating limit distributions. This achieves two important results: (1) the range of statistical autonomous explanations is vastly extended and shown to range over many different kinds of limit distributions; (2) the modified account permits answers to questions about why these limit distribution patterns are so common in nature; why these patterns are robust; and also why these patterns are insensitive to most lower-level details pertaining to the characters or events that comprise the statistical ensemble. The modified account can be understood as a corrective for many extant accounts of statistical pattern-level explanations that fail to answer these crucial questions.

The ideas Darwin published in On the Origin of Species and The Descent of Man in the nineteenth century continue to have a major impact on our current understanding of the world in which we live and the place that humans occupy in it. Darwin’s theories constitute the core of the contemporary life sciences, and elicit enduring fascination as a potentially unifying basis for various branches of biology and the biomedical sciences. They can be used to understand the biological ground of human cognition, common behavioral patterns and disorders, and psychopathology more generally in psychology, psychiatry, and neuroscience. Perhaps the best known expression of this fact is Dobzhansky’s famous dictum that “nothing in biology makes sense except in the light of evolution” (Dobzhansky T. Am Zool 4:443–452, 1964: 449; Am Biol Teach 35:125–129, 1973: 125), and given that all human behavior supervenes on some biological basis, evolutionary thinking has a vast scope even just in this regard.

This year marks the 60th year anniversary of the publication of Niko Tinbergen’s “On aims and methods of ethology” which remains influential among today’s biologists and social scientists for its introduction of four questions for a complete explanation for animal behaviors. In this paper we argue that a large part of the lasting appeal to Tinbergen’s four questions was (and still is) the methodological commitment to treating organisms as objects as opposed to purposive agents. Tinbergen’s approach reinvigorated the discipline of ethology, allowing it to shed its teleological and anthropomorphic associations and to better cohere with a philosophy of science that favors inductive procedures, causal and mechanistic analytic techniques, and an emphasis on Darwinian explanations. While Tinbergen’s approach is still prized among today’s biological social scientists, it ignores an important feature of many social organisms, that they are not merely objects, they are also purposive agents. We explore the implications that a shift from treating organisms as objects to treating them as agents has on both how we should interpret and answer Tinbergen’s four questions. Updating Tinbergen’s four questions with agency in mind not only makes them more applicable to the biological investigation of animal behavior, but also strengthens the value and applicability of biology-oriented research programs in the social sciences.

Recently historians and philosophers of science have been interested in the role of statistics and probability in investigating population variation. The focus is typically on investigators applying statistics and probability to explain large scale phenomenon that arise out of the collective behavior of numerous and varied individuals. The case studies that inform this work come mostly from molecular physics and 20th century genetical versions of evolutionary theory. Charles Darwin's work on evolution is rarely mentioned in this context except to point out his shortcomings—he made evolutionary theory “ripe” for statistical investigations, but he was not a statistical thinker. But this is a mistake, Darwin was a statistical thinker. In this essay I describe two instances where Darwin utilized statistical methods to investigate evolution. In the light of these cases, we ought to revise our views about Darwin's scientific methodology, in particular, how he came to develop his ideas about evolution and about the nature of his “population thinking”. Furthermore, Darwin's cases provide us with an expanded view about what constitutes “statistical thinking” in the biological sciences. In the examples we will find Darwin using statistical measures of type frequencies to detect large scale ensemble effects, confirm hypotheses by comparing between expected and observed averages, and applying the astronomer's law of error to explain evolutionary trends.

Natural selection is commonly seen not just as an explanation for adaptive evolution, but as the inevitable consequence of "heritable variation in fitness among individuals". Although it remains embedded in biological concepts, such a formalisation makes it tempting to explore whether this precondition may be met not only in life as we know it, but also in other physical systems. This would imply that these systems are subject to natural selection and may perhaps be investigated in a biological framework, where properties are typically examined in light of their putative functions. Here we relate the major questions that were debated during a three-day workshop devoted to discussing whether natural selection may take place in non-living physical systems. We start this report with a brief overview of research fields dealing with "life-like" or "proto-biotic" systems, where mimicking evolution by natural selection in test tubes stands as a major objective. We contend the challenge may be as much conceptual as technical. Taking the problem from a physical angle, we then discuss the framework of dissipative structures. Although life is viewed in this context as a particular case within a larger ensemble of physical phenomena, this approach does not provide general principles from which natural selection can be derived. Turning back to evolutionary biology, we ask to what extent the most general formulations of the necessary conditions or signatures of natural selection may be applicable beyond biology. In our view, such a cross-disciplinary jump is impeded by reliance on individuality as a central yet implicit and loosely defined concept. Overall, these discussions thus lead us to conjecture that understanding, in physico-chemical terms, how individuality emerges and how it can be recognised, will be essential in the search for instances of evolution by natural selection outside of living systems.

Several analyses of biological function — for example, those of Williams, Millikan, and Kitcher — identify an item’s function with what natural selection designed it to do. Allen and Bekoff have disagreed, claiming that natural design is a special case of biological function. I argue that Allen and Bekoff’s account of natural design is unduly restrictive and that it fails to mark a principled distinction between function and design. I distinguish two approaches to the phenomenon of natural design — the “trait-centered” approach of Allen and Bekoff and the “organism-centered” approach — and defend the latter. When design is understood according to the organism-centered approach, biological function and design are co-instantiated phenomena.

In this paper, we argue that rather than exclusively focusing on trying to determine if an idealized model fits a particular account of scientific explanation, philosophers of science should also work on directly analyzing various explanatory schemas that reveal the steps and justification involved in scientists’ use of highly idealized models to formulate explanations. We develop our alternative methodology by analyzing historically important cases of idealized statistical modeling that use a three-step explanatory schema involving idealization, mathematical operation, and explanatory interpretation.

Over the last six decades there has been a consistent trend in the philosophy literature to emphasize the role of causes in scientific explanation. The emphasis on causes even pervades discussions of non-causal explanations. For example, the concern of a recent paper by Marc Lange (2013b) is whether purported cases of statistical explanation are "really statistical" or really causal. Likewise, Michael Strevens (2011) argues that the main task of statistical idealizations is to distinguish between the causal factors that make a difference to the phenomenon to be explained and those that do not. But, the philosophy literature poorly reflects the history of the development of statistical explanation in the sciences. Francis Galton's (19th century) explanation for the laws of heredity is our case. Galton's statistical explanation was both innovative for his time and influential to our contemporary sciences. The key points to understanding Galton's statistical explanation for reversion is that it is autonomous from the real-world biological properties that make up an instance of reversion while still being approximately true of many real-world biological phenomena. Ours is an expanded discussion of ideas originated in Hacking (1990) and Sober (1980). We will articulate these features and compare our account with that of Lange and Strevens.

Over the past fifteen years there has been a considerable amount of debate concerning what theoretical population dynamic models tell us about the nature of natural selection and drift. On the causal interpretation, these models describe the causes of population change. On the statistical interpretation, the models of population dynamics models specify statistical parameters that explain, predict, and quantify changes in population structure, without identifying the causes of those changes. Selection and drift are part of a statistical description of population change; they are not discrete, apportionable causes. Our objective here is to provide a definitive statement of the statistical position, so as to allay some confusions in the current literature. We outline four commitments that are central to statisticalism. They are: 1. Natural Selection is a higher order effect; 2. Trait fitness is primitive; 3. Modern Synthesis (MS)-models are substrate neutral; 4. MS-selection and drift are model-relative.

Shapiro and Sober ([2007]) claim that Walsh, Ariew, Lewens, and Matthen (henceforth WALM) give a mistaken, a priori defense of natural selection and drift as epiphenomenal. Contrary to Shapiro and Sober's claims, we first argue that WALM's explanatory doctrine does not require a defense of epiphenomenalism. We then defend WALM's explanatory doctrine by arguing that the explanations provided by the modern genetical theory of natural selection are 'autonomous-statistical explanations' analogous to Galton's explanation of reversion to mediocrity and an explanation of the diffusion of gases. We then argue that whereas Sober's theory of forces is an adequate description of Darwin's theory, WALM's explanatory doctrine is required to understand how the modern genetical theory of natural selection explains large-scale statistical regularities.

The debate about whether some attribute is ‘by nature’ or ‘by nurture’ has a long history and it covers numerous topics. For instance, Socrates proposed that our ideas of complex concepts come from memories that are innate within us. Even today thinkers believe that some of our ideas are part of our nature rather than our nurture. This theory has social and policy implications. If intellectual quotient (IQ) is a fixed part of nature, is it worthwhile to contribute tax dollars to improve one's nurturing environment? Or, more generally, some think that understanding human nature might affect how we ought to live. Influenced by Darwin and developments in genetics, the nature/nurture debate has reduced to a debate about whether our attributes are ‘genetic’ or ‘environmental’. Yet, the implications of the genetic theories of human nature are not obvious since genes alone do not produce any attributes. Key concepts: ‘Nativists’ employ ‘poverty of stimulus’ arguments to demonstrate that an idea or cognitive ability could not have been learned. ‘Empiricists’ believe that our beliefs about the world come from our perceptual connection to the world and not from our natures. IQ is likely to be influenced by nurturing environments. Likely, there is no such thing as the singular good life; rather there are numerous valid conceptions of the good life. The gene/environment dichotomy is false but that does not mean we cannot distinguish between robust and plastic developmental events. Keywords: nature; nurture; genetics; language; knowledge

Recently advocates of the propensity interpretation of fitness have turned critics. To accommodate examples from the population genetics literature they conclude that fitness is better defined broadly as a family of propensities rather than the propensity to contribute descendants to some future generation. We argue that the propensity theorists have misunderstood the deeper ramifications of the examples they cite. These examples demonstrate why there are factors outside of propensities that determine fitness. We go on to argue for the more general thesis that no account of fitness can satisfy the desiderata that have motivated the propensity account.

We have argued elsewhere that natural selection is not a cause of evolution, and that a resolution-of-forces (or vector addition) model does not provide us with a proper understanding of how natural selection combines with other evolutionary influences. These propositions have come in for criticism recently, and here we clarify and defend them. We do so within the broad framework of our own 'hierarchical realization model' of how evolutionary influences combine.

Abstract According to Ernst Mayr, population thinking is a metaphysical theory. Mayr's essentialism, amounts to the view that types, including conceptual categories, are real while individual variation is illusionary. In contrast, population thinking entails the opposite view: Types are not real in nature, only individuals exist. According to Sober, the explanatory goal for essentialists is to find an underlying order that unites and underlies the variation one sees in nature. Population thinking as a methodological doctrine states that regularities that occur in populations such as extinction, speciation, and adaptation emerge from the collective activities of individuals. Such population phenomena were unknown until new statistical measures introduced by Laplace provided the proper resolution to detect population changes. There are two types of population thinkers, statisticians, and force theorists. Malthus and Darwin were force theorists.

Charles Darwin, James Clerk Maxwell, and Francis Galton were all aware, by various means, of Aldolphe Quetelet’s pioneering work in statistics. Darwin, Maxwell, and Galton all had reason to be interested in Quetelet’s work: they were all working on some instance of how large-scale regularities emerge from individual events that vary from one another; all were rejecting the divine interventionistic theories of their contemporaries; and Quetelet’s techniques provided them with a way forward. Maxwell and Galton all explicitly endorse Quetelet’s techniques in their work; Darwin does not incorporate any of the statistical ideas of Quetelet, although natural selection post-twentieth century synthesis has. Why not Darwin? My answer is that by the time Darwin encountered Malthus’s law of excess reproduction he had all he needed to answer about large scale regularities in extinctions, speciation, and adaptation. He didn’t need Quetelet.