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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
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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.
Funding agency Grant number
Russian Science Foundation 17-19-01038
The research is supported by the Russian Science Foundation, project 17-19-01038.
Document Type: Article
Language: English
Citation: Dmitry B. Rokhlin, Gennady A. Ougolnitsky, “Optimal incentive strategy in a discounted stochastic Stackelberg game”, Contributions to Game Theory and Management, 12 (2019), 273–281
Citation in format AMSBIB
\Bibitem{RokOug19}
\by Dmitry~B.~Rokhlin, Gennady~A.~Ougolnitsky
\paper Optimal incentive strategy in a discounted stochastic Stackelberg game
\jour Contributions to Game Theory and Management
\yr 2019
\vol 12
\pages 273--281
\mathnet{http://mi.mathnet.ru/cgtm348}
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  • https://www.mathnet.ru/eng/cgtm/v12/p273
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