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Contributions to Game Theory and Management, 2019, Volume 12, Pages 273–281
(Mi cgtm348)
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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.
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
Linking options:
https://www.mathnet.ru/eng/cgtm348 https://www.mathnet.ru/eng/cgtm/v12/p273
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Statistics & downloads: |
Abstract page: | 190 | Full-text PDF : | 45 | References: | 16 |
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