Stochastic Games

Solan, Eilon

doi:10.1007/978-0-387-30440-3_522

Cited by 7 publications

(2 citation statements)

References 66 publications

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“…In Figure 2, the Robbins equilibrium (Robbins, 1918) concerns a population that is recombined with no selection and mutation and the balance evenly cover a population that is repeatedly moved without selection and crossing that leads to a point where the objective function value is canceled (Solan, 2009).…”

Section: Literature Review: Theorymentioning

confidence: 99%

Results of Evolution Supervised by Genetic Algorithms

et al. 2010

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The efficiency of a genetic algorithm is frequently assessed using a series of operators of evolution like crossover operators, mutation operators or other dynamic parameters. The present paper aimed to review the main results of evolution supervised by genetic algorithms used to identify solutions to agricultural and horticultural hard problems and to discuss the results of using a genetic algorithms on structure-activity relationships in terms of behavior of evolution supervised by genetic algorithms. A genetic algorithm had been developed and implemented in order to identify the optimal solution in term of estimation power of a multiple linear regression approach for structure-activity relationships. Three survival and three selection strategies (proportional, deterministic and tournament) were investigated in order to identify the best survivalselection strategy able to lead to the model with higher estimation power. The Molecular Descriptors Family for structure characterization of a sample of 206 polychlorinated biphenyls with measured octanol-water partition coefficients was used as case study. Evolution using different selection and survival strategies proved to create populations of genotypes living in the evolution space with different diversity and variability. Under a series of criteria of comparisons these populations proved to be grouped and the groups were showed to be statistically different one to each other. The conclusions about genetic algorithm evolution according to a number of criteria were also highlighted.

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Section: Literature Review: Theorymentioning

confidence: 99%

Results of Evolution Supervised by Genetic Algorithms

et al. 2010

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“…This result has been extended to the case of n-player games by Fink [2], Takahashi [3], Sobel [4]. The results in stochastic games of the recent 60 years, mostly for non-cooperative case, are discussed by Neyman and Sorin [5], Solan [6], and Solan and Vieille [7].…”

Section: Introductionmentioning

confidence: 98%

Cooperative Stochastic Games with Mean-Variance Preferences

Parilina

Akimochkin

2021

Mathematics

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In stochastic games, the player’s payoff is a stochastic variable. In most papers, expected payoff is considered as a payoff, which means the risk neutrality of the players. However, there may exist risk-sensitive players who would take into account “risk” measuring their stochastic payoffs. In the paper, we propose a model of stochastic games with mean-variance payoff functions, which is the sum of expectation and standard deviation multiplied by a coefficient characterizing a player’s attention to risk. We construct a cooperative version of a stochastic game with mean-variance preferences by defining characteristic function using a maxmin approach. The imputation in a cooperative stochastic game with mean-variance preferences is supposed to be a random vector. We construct the core of a cooperative stochastic game with mean-variance preferences. The paper extends existing models of discrete-time stochastic games and approaches to find cooperative solutions in these games.

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