Inferentialism, Bayesianism and Scientific Explanation

点击翻译按钮获取中文摘要

Explanation constitutes a cornerstone of scientific rationality. But what is valid explanatory reasoning? And when is valid explanatory reasoning a guide to truth? Although these questions have generated much debate, no unanimously accepted answers have emerged. Our research program aims to break the deadlock in the debate between different philosophical theories of explanation and to develop a new account of explanatory reasoning. To do so, the proposed research program synthesizes approaches from philosophy of language (inferentialism) and formal epistemology (Bayesianism). First, we will appeal to inferentialism to justify the interpretation of explanations as arguments that facilitate the inference to the explanandum by providing reasons. Secondly, we will use Bayesianism to quantitatively reconstruct the dynamics of explanatory reasoning in probabilistic terms. Finally, we will investigate the possibility to systematically integrate the two approaches in order to vindicate the rationality of an explanatory pattern commonly used in scientific practice, viz. inference to the best explanation. Our results will be informed by an in depth analysis of two case studies: one from economics, viz. the explanation of the stylized facts of finance, and one from biology, viz. the explanation of biological functions, whose explanatory value is hard to account for by reference to traditional theories of scientific explanation. The combined use of inferentialism and Bayesianism and the application to the case studies will allow us to tackle the traditional task of interpreting scientific explanation from an innovative perspective, both normatively and scientifically informed.