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Dynamics and computation in functional shifts (2004)

by J Namikawa, T Hashimoto
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A model for learning to segment temporal sequences, utilizing a mixture of RNN experts . . .

by Jun Namikawa, Jun Tani - NEURAL NETWORKS , 2008
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Abstract - Cited by 6 (3 self) - Add to MetaCart
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Meta-evolutionary game dynamics for mathematical modelling of rules dynamics

by Takashi Hashimoto, Yuya Kumagai - Advances in Artificial , 2003
"... Abstract. This paper proposes an evolutionary-game-theory model, called meta-evolutionary game dynamics, for studying the dynamics of rules and individual behaviour. Although there are two game theoretical views of rules, i.e., seeing rules as game forms and as equilibria of games, endogenous change ..."
Abstract - Cited by 1 (1 self) - Add to MetaCart
Abstract. This paper proposes an evolutionary-game-theory model, called meta-evolutionary game dynamics, for studying the dynamics of rules and individual behaviour. Although there are two game theoretical views of rules, i.e., seeing rules as game forms and as equilibria of games, endogenous changes of rules are not modelled effectively by either of these two views. We introduce a model for integrating the two views, in which the interaction rules of replicator equations change dynamically. Computer simulations of an example of the model that include mutation and extinction of both strategies and games show (1) an intermittent change of strategy distributions, (2) a continual transition from a dominant strategy to another, and (3) metastable states distinct from the Nash equilibria. We discuss the notion of evolutionary stability of games and some natural examples showing rule dynamics. We conclude that meta-evolutionary game dynamics enables the study of the endogenous dynamic of rules. Our model contributes, therefore, the development of game theory approach to the dynamics of rules. Keywords: Dynamics of rules; Mathematical modelling; Meta-evolutionary game dynamics; Replicator equations; Evolutionary stable games

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by Jun Tani, Key Words , 706
"... A model for learning to segment temporal sequences, utilizing a mixture of RNN experts together with adaptive variance ..."
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A model for learning to segment temporal sequences, utilizing a mixture of RNN experts together with adaptive variance
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