Learning how to regulate a polluter with unknown characteristics: An application of genetic algorithms to a game of dynamic pollution control
We consider, within the framework of a dynamic game, the problem of a regulator using taxes to force a polluting monopolist to act in a socially optimal way. Traditionally it has been assumed that, in such a case, the regulator will implement his Stackelberg solution. However, the Stackelberg solution presupposes that the regulator knows exactly all characteristics of the monopolist. We show here that the on-line use of a genetic algorithm may allow the regulator to approximate the Stackelberg solution, even when he has only extremely limited information about the monopolist. While the results presented here are still preliminary, the fast convergence towards the analytic solution appears to suggest that the approach may be of practical value in real situations.
KeywordsGenetic Algorithm Reaction Function Dynamic Game Stackelberg Game Pollution Stock
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