Event Detail

The aim of this talk will be to convey some of the ways that familiar ideas and tools from randomized and probabilistic computation might bear on issues of philosophical interest, focusing especially on questions about cognition, representation, and reasoning. A first question is when it would ever make sense for an agent to employ randomization in the course of decision making. Drawing on ideas from game theory, reinforcement learning, and statistics, we tentatively propose a unified answer to the question. A second set of questions revolves around the idea of characterizing an agent’s implicit causal knowledge of the world by appeal to the explicit causal structure of a probabilistic algorithm. This second topic raises novel questions about the logic of counterfactuals beyond the usual propositional setting. Much of this is work in progress.