Collin Rice

Minimal Model Explanations

This article discusses minimal model explanations, which we argue are distinct from various causal, mechanical, difference-making, and so on, strategies prominent in the philosophical literature. We contend that what accounts for the explanatory power of these models is not that they have certain features in common with real systems. Rather, the models are explanatory because of a story about why a class of systems will all display the same large-scale behavior because the details that distinguish them are irrelevant. This story explains patterns across extremely diverse systems and shows how minimal models can be used to understand real systems.

Hypothetical Pattern Idealization and Explanatory Models

Highly idealized models, such as the Hawk-Dove game, are pervasive in biological theorizing. We argue that the process and motivation that leads to the introduction of various idealizations into these models is not adequately captured by Michael Weisberg’s taxonomy of three kinds of idealization. Consequently, a fourth kind of idealization is required, which we call hypothetical pattern idealization. This kind of idealization is used to construct models that aim to be explanatory but do not aim to be explanations.

Autonomous-Statistical Explanations and Natural Selection

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 ofgases. 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 themodern genetical theory of natural selection explains large-scale statistical regularities. 1 Introduction 2 Shapiro and Sober’s ‘Epiphenomenalism Do’s and Don’ts’ 3 WALM’s Explanatory Doctrine 4 Galton’s Autonomous-Statistical Explanation 5 A Second Example: The Statistical Explanation of the Diffusion of Gases 6 Distinguishing Two Theories of Evolution by Natural Selection 7 A Possible Objection: Are Statistical Laws Sufficient for Explanation? 8 Conclusion 1 Introduction 2 Shapiro and Sober’s ‘Epiphenomenalism Do’s and Don’ts’ 3 WALM’s Explanatory Doctrine 4 Galton’s Autonomous-Statistical Explanation 5 A Second Example: The Statistical Explanation of the Diffusion of Gases 6 Distinguishing Two Theories of Evolution by Natural Selection 7 A Possible Objection: Are Statistical Laws Sufficient for Explanation? 8 Conclusion

Massive Modularity, Content Integration, and Language

One of the obstacles facing massive modularity is how a pervasively modular mind might generate non-domain-specific thoughts by integrating the content produced by various domain-specific modules. Peter Carruthers has recently argued that the operations of the language faculty are constitutive of the process by which the human mind is able to integrate content from heterogeneous conceptual domains. In this article, I first argue that Carruthers’s data do not provide support for either of two possible interpretations of his thesis. In addition, I provide empirical and theoretical reasons for thinking content integration is performed external to the language faculty.