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  • 2016
  • Chapter
  • Proceedings of 2016 American Control Conference

Deriving an Optimally Deceptive Policy in Two-Player Iterated Games

By: Elisabeth Paulson and Christopher Griffin
  • Format:Electronic
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Abstract

We formulate the problem of determining an optimally deceptive strategy in a repeated game framework. We assume that two players are engaged in repeated play. During an initial time period, Player 1 may deceptively train his opponent to expect a specific strategy. The opponent computes a best response. The best response is computed on an optimally deceptive strategy that maximizes the first player's long-run payoff during actual game play. Player 1 must take into consideration not only his real payoff but also the cost of deception. We formulate the deception problem as a nonlinear optimization problem and show how a genetic algorithm can be used to compute an optimally deceptive play. In particular, we show how the cost of deception can lead to strategies that blend a target strategy (policy) and an optimally deceptive one.

Keywords

Deception; Strategy; Game Theory

Citation

Paulson, Elisabeth, and Christopher Griffin. "Deriving an Optimally Deceptive Policy in Two-Player Iterated Games." In Proceedings of 2016 American Control Conference. IEEE Press, 2016. (Developed with Booz Allen Hamilton.)
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About The Author

Elisabeth C. Paulson

Technology and Operations Management
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