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JOURNAL OF ECONOMIC DYNAMICS & CONTROL; 2008; 32(10):3275 - 3293

Q-learning agents in a Cournot oligopoly model

Authors: Waltman L, Kaymak U
Affiliations: Erasmus Univ

Abstract:
Q-learning is a reinforcement learning model from the field of artificial intelligence. We study the use of Q-learning for modeling the learning behavior of firms in repeated Cournot oligopoly games. Based on computer simulations, we show that Q-learning firms generally learn to collude with each other, although full collusion usually does not emerge. We also present some analytical results. These results provide insight into the underlying mechanism that causes collusive behavior to emerge. Q-learning is one of the few learning models available that can explain the emergence of collusive behavior in settings in which there is no punishment mechanism and no possibility for explicit communication between firms. (C) 2008 Elsevier B.V. All rights reserved.

Publication type:
Article in Journal

  
Authors (1)
  
MSc L. (Ludo) Waltman
Centre for Science and Technology Studies
Researcher. MSc in Economics & Informatics from Erasmus University Rotterdam (2005). Currently working on a PhD thesis in economics at Erasmus University Rotterdam. Working at CWTS from June 2009. Involved in various science mapping projects. Main research interests are science mapping and ...