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JOURNALS // Contributions to Game Theory and Management // Archive

Contributions to Game Theory and Management, 2019 Volume 12, Pages 273–281 (Mi cgtm348)

Optimal incentive strategy in a discounted stochastic Stackelberg game

Dmitry B. Rokhlin, Gennady A. Ougolnitsky

I. I. Vorovich Institute of Mathematics, Mechanics and Computer Sciences of Southern Federal University, 8a, Milchakova, Rostov-on-Don, Russia

Abstract: We consider a game where manager's (leader's) aim is to maximize the gain of a large corporation by the distribution of funds between $m$ producers (followers). The manager selects a tuple of $m$ non-negative incentive functions, and the producers play a discounted stochastic game, which results in a Nash equilibrium. Manager's aim is to maximize her related payoff over the class of admissible incentive functions. It is shown that this problem is reduced to a Markov decision process.

Language: English



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