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A nested family of $$\varvec{k}$$ k -total effective rewards for positional games

Endre Boros, Khaled Elbassioni (), Vladimir Gurvich and Kazuhisa Makino
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Endre Boros: Rutgers University
Khaled Elbassioni: Masdar Institute of Science and Technology
Vladimir Gurvich: Rutgers University
Kazuhisa Makino: Research Institute for Mathematical Sciences (RIMS) Kyoto University

International Journal of Game Theory, 2017, vol. 46, issue 1, No 13, 263-293

Abstract: Abstract We consider Gillette’s two-person zero-sum stochastic games with perfect information. For each $$k \in \mathbb {N}=\{0,1,\ldots \}$$ k ∈ N = { 0 , 1 , … } we introduce an effective reward function, called k-total. For $$k = 0$$ k = 0 and 1 this function is known as mean payoff and total reward, respectively. We restrict our attention to the deterministic case. For all k, we prove the existence of a saddle point which can be realized by uniformly optimal pure stationary strategies. We also demonstrate that k-total reward games can be embedded into $$(k+1)$$ ( k + 1 ) -total reward games.

Keywords: Stochastic game with perfect information; Cyclic games; Two-person; Zero-sum; Mean payoff; Total reward (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1007/s00182-016-0532-z

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