An approximate dynamic programming approach to solving dynamic oligopoly models

In this article, we introduce a new method to approximate Markov perfect equilibrium in largescale Ericson and Pokes (1995)-style dynamic oligopoly models that are not amenable to exact solution due to the curse of dimensionality. The method is based on an algorithm that iterates an approximate best...

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Bibliographic Details
Published in:The Rand journal of economics 2012-06, Vol.43 (2), p.253-282
Main Authors: Farias, Vivek, Saure, Denis, Weintraub, Gabriel Y.
Format: Article
Language:English
Subjects:
Algorithms
Approximate values
Approximation
Architectural models
Architecture
Dynamic modeling
Dynamic programming
Economic equilibrium
Economic models
Incumbents
Linear programming
Markov analysis
Markovian processes
Mathematical functions
Mathematical programming
Oligopoly
Studies
Value theory
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Summary:In this article, we introduce a new method to approximate Markov perfect equilibrium in largescale Ericson and Pokes (1995)-style dynamic oligopoly models that are not amenable to exact solution due to the curse of dimensionality. The method is based on an algorithm that iterates an approximate best response operator using an approximate dynamic programming approach. The method, based on mathematical programming, approximates the value function with a linear combination of basis functions. We provide results that lend theoretical support to our approach. We introduce a rich yet tractable set of basis functions, and test our method on important classes of models. Our results suggest that the approach we propose significantly expands the set of dynamic oligopoly models that can be analyzed computationally.
ISSN:0741-6261
1756-2171
DOI:10.1111/j.1756-2171.2012.00165.x