Imitation dynamics in population games are a class of evolutionary game-theoretic models, widely used to study decision-making processes in social groups. Different from other models, imitation dynamics allow players to have minimal information on the structure of the game they are playing, and are thus suitable for many applications, including traffic management, marketing, and disease control. In this work, we study a general case of imitation dynamics where the structure of the game and the imitation mechanisms change in time due to external factors, such as weather conditions or social trends. These changes are modeled using a continuous-time Markov jump process. We present tools to identify the dominant strategy that emerges from the dynamics through methodological analysis of the function parameters. Numerical simulations are provided to support our theoretical findings.

On imitation dynamics in population games with Markov switching / Cunha, Rafael; Zino, Lorenzo; Cao, Ming. - ELETTRONICO. - (2021), pp. 722-727. (Intervento presentato al convegno 2021 European Control Conference (ECC) tenutosi a Delft, Netherlands nel 29 Giugno - 2 Luglio 2021) [10.23919/ECC54610.2021.9655147].

On imitation dynamics in population games with Markov switching

Lorenzo Zino;
2021

Abstract

Imitation dynamics in population games are a class of evolutionary game-theoretic models, widely used to study decision-making processes in social groups. Different from other models, imitation dynamics allow players to have minimal information on the structure of the game they are playing, and are thus suitable for many applications, including traffic management, marketing, and disease control. In this work, we study a general case of imitation dynamics where the structure of the game and the imitation mechanisms change in time due to external factors, such as weather conditions or social trends. These changes are modeled using a continuous-time Markov jump process. We present tools to identify the dominant strategy that emerges from the dynamics through methodological analysis of the function parameters. Numerical simulations are provided to support our theoretical findings.
2021
978-9-4638-4236-5
978-1-6654-7945-5
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2972361