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Heterogeneous update mechanisms in evolutionary games: mixing innovative and imitative dynamics

Amaral, Marco A, Javarone, Marco A. (2018) Heterogeneous update mechanisms in evolutionary games: mixing innovative and imitative dynamics. Physical Review E: Statistical, Nonlinear, and Soft Matter Physics, 97 (4). ISSN 1063-651X. (doi:10.1103/PhysRevE.97.042305) (KAR id:66539)


Innovation and evolution are two processes of paramount relevance for social and biological systems. In general, the former allows the introduction of elements of novelty, while the latter is responsible for the motion of a system in its phase space. Often, these processes are strongly related, since an innovation can trigger the evolution, and the latter can provide the optimal conditions for the emergence of innovations. Both processes can be studied by using the framework of Evolutionary Game Theory, where evolution constitutes an intrinsic mechanism. At the same time, the concept of innovation requires an opportune mathematical representation. Notably, innovation can be modeled as a strategy, or can constitute the underlying mechanism which allows agents to change strategy. Here, we analyze the second case, investigating the behavior of a heterogeneous population, composed of imitative and innovative agents. Imitative agents change strategy only by imitating that of their neighbors, whereas innovative ones change strategy without the need of a copying source. The proposed model is analyzed by means of analytical calculations and numerical simulations in different topologies. Remarkably, results indicate that the mixing of mechanisms can be detrimental to cooperation near phase transitions. In those regions, the spatial reciprocity from imitative mechanisms is destroyed by innovative agents, leading to the downfall of cooperation. Our investigation sheds some light on the complex dynamics emerging from the heterogeneity of strategy revision methods, highlighting the role of innovation in evolutionary games.

Item Type: Article
DOI/Identification number: 10.1103/PhysRevE.97.042305
Uncontrolled keywords: evolutionary game theory; evolution; innovation; statistical physics
Subjects: Q Science > QC Physics > QC173.45 Condensed Matter
Q Science > QC Physics > QC20 Mathematical Physics
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: M.A. Javarone
Date Deposited: 26 Mar 2018 12:15 UTC
Last Modified: 04 Mar 2024 17:16 UTC
Resource URI: (The current URI for this page, for reference purposes)

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Javarone, Marco A..

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