Penn has considerable strength in philosophy of science and related areas of science studies. Faculty are especially strong in the philosophy of the life and social sciences, the relations between the history of philosophy and the history of science, and the history of the philosophy of science.

Philosophy of the Natural Sciences and Mathematics

Faculty work on diverse topics in the philosophy of natural sciences, but areas of special interest include philosophy of biology (Santana, Spencer, Weisberg), learning theory (Bicchieri, Weinstein), the history of biology and psychology (Detlefsen, Santana, Spencer), philosophy of chemistry (Weisberg), and the public understanding of science (Weisberg). A number of faculty also work directly in areas of the natural and formal sciences, including cognitive science (Bicchieri, Weinstein, Weisberg), modeling (Bicchieri, Santana, Singer, and Weisberg), and computer science (Weinstein). Santana, Spencer, Singer, and Weisberg also work on many central topics in the philosophy of science, including explanation, the structure of theories, confirmation, and the social structure of science.

Philosophy of Social Science

Our faculty are also interested in some of the central questions in contemporary social science.  

Cristina Bicchieri is interested in how norms may emerge and become stable, why an established norm may suddenly be abandoned, how it is possible that inefficient or unpopular norms survive, and what motivates people to obey norms. In order to answer some of these questions, she has combined evolutionary and game-theoretic tools with models of decision-making drawn from cognitive and social psychology.

Daniel J. Singer has also used formal models to understand various social phenomena. For instance, some of his work has used formal modeling to examine scientific communities, political polarization, and the merits of democracy.

Michael Weisberg has developed agent-based models that explains how scientific communities coordinate in producing scientific results, and how the division of cognitive labor affects the success of the scientific enterprise. In particular, Weisberg has developed an agent-based computational model in which different research approaches (strategies) are distributed within an epistemic landscape, each approach having its own epistemic payoff. A natural question to ask is how much attention scientists should pay to what other scientists are doing and the costs/benefits of doing so. Weisberg shows that the most effective communities (i.e., those with the highest payoff) are a combination of trendsetters (who open new research paths) and followers (who imitate the most successful members of the scientific community).