dsGameSolver: A Python Program for Computing Markov Perfect Equilibria of Dynamic Stochastic Games

Abstract

dsGameSolver is based on the homotopy method developed in Eibelshaeuser and Poensgen (2019). It is the first program capable of computing a Markov perfect equilibrium of any finite dynamic stochastic game -- subject to the usual restrictions on numerical accuracy and working memory. dsGameSolver supports general games with state-player-specific numbers of actions and individual discount factors, covering virtually any game of complete information. Furthermore, it is remarkably performant and able to handle games with thousands of state-player-actions. Finally, dsGameSolver allows users with basic programming skills in Python to formulate complex games with ease and to directly use the results for subsequent illustration and analysis.

Keywords: Equilibrium computation, Homotopy continuation, Quantal response, Logit choice, Limiting equilibrium

JEL Classification: C63, C73