Dynamic Discrete Choice and Inverse Reinforcement Learning: Inferring Preferences and Beliefs From Human Behavior
动态离散选择与逆强化学习:从人类行为推断偏好和信念
This article surveys two deeply connected literatures that approach the same fundamental problem from different disciplinary traditions: dynamic discrete choice (DDC) in structural econometrics and inverse reinforcement learning (IRL) in machine learning. Both seek to infer the preferences of decision makers from observed sequential behavior, assuming that individuals act to maximize an expected reward function within a dynamic, uncertain environment formalized as a Markov…