Reinforcement Learning Based Computationally Efficient Conditional Choice Simulation Estimation of Dynamic Discrete Choice Models
基于强化学习的高效动态离散选择模型条件选择模拟估计
Dynamic discrete choice (DDC) models have found widespread application in marketing. However, estimating these becomes challenging in "big data" settings with high-dimensional state-action spaces. To address this challenge, this paper develops a Reinforcement Learning (RL)-based two-step ("computationally light") Conditional Choice Simulation (CCS) estimation approach that combines the scalability of machine learning with the transparency, explainability, and…