Geodesic
Optimal incentive strategy in a discounted stochastic Stackelberg game
Contributions to game theory and management, Tome 12 (2019), pp. 273-281.

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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. 
@article{CGTM_2019_12_a15,
     author = {Dmitry B. Rokhlin and Gennady A. Ougolnitsky},
     title = {Optimal incentive strategy in a discounted stochastic {Stackelberg} game},
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     pages = {273--281},
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     volume = {12},
     year = {2019},
     language = {en},
     url = {http://geodesic.mathdoc.fr/item/CGTM_2019_12_a15/}
}
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Dmitry B. Rokhlin; Gennady A. Ougolnitsky. Optimal incentive strategy in a discounted stochastic Stackelberg game. Contributions to game theory and management, Tome 12 (2019), pp. 273-281. http://geodesic.mathdoc.fr/item/CGTM_2019_12_a15/

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