Network imitation dynamics in population games on community networks

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Network imitation dynamics in population games on community networks. / Como, Giacomo; Fagnani, Fabio; Zino, Lorenzo.

In: IEEE Transactions on Control of Network Systems, 2020.

Research output: Contribution to journalArticle

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TY - JOUR

T1 - Network imitation dynamics in population games on community networks

AU - Como, Giacomo

AU - Fagnani, Fabio

AU - Zino, Lorenzo

PY - 2020

Y1 - 2020

N2 - We study the asymptotic behavior of deterministic, continuous-time imitation dynamics for population games over networks. The basic assumption of this learning mechanism --- encompassing the replicator dynamics --- is that players belonging to a single population exchange information through pairwise interactions, whereby they get aware of the actions played by the other players and the corresponding rewards. Using this information, they can revise their current action, imitating the one of the players they interact with. The pattern of interactions regulating the learning process is determined by a community structure. First, the set of equilibrium points of such network imitation dynamics is characterized. Second, for the class of potential games and for undirected and connected community networks, global asymptotic convergence is proved. In particular, our results guarantee convergence to a Nash equilibrium from every fully supported initial population state in the special case when the Nash equilibria are isolated and fully supported. Examples and numerical simulations are offered to validate the theoretical results and counterexamples are discussed for scenarios when the assu

AB - We study the asymptotic behavior of deterministic, continuous-time imitation dynamics for population games over networks. The basic assumption of this learning mechanism --- encompassing the replicator dynamics --- is that players belonging to a single population exchange information through pairwise interactions, whereby they get aware of the actions played by the other players and the corresponding rewards. Using this information, they can revise their current action, imitating the one of the players they interact with. The pattern of interactions regulating the learning process is determined by a community structure. First, the set of equilibrium points of such network imitation dynamics is characterized. Second, for the class of potential games and for undirected and connected community networks, global asymptotic convergence is proved. In particular, our results guarantee convergence to a Nash equilibrium from every fully supported initial population state in the special case when the Nash equilibria are isolated and fully supported. Examples and numerical simulations are offered to validate the theoretical results and counterexamples are discussed for scenarios when the assu

KW - Asymptotic stability

KW - Convergence

KW - Distributed Learning

KW - Evolutionary Game Theory

KW - Games

KW - Imitation Dynamics

KW - Learning systems

KW - Network Systems

KW - Population Games

KW - Sociology

KW - Stability analysis

KW - Statistics

UR - http://www.scopus.com/inward/record.url?scp=85096087774&partnerID=8YFLogxK

U2 - 10.1109/TCNS.2020.3032873

DO - 10.1109/TCNS.2020.3032873

M3 - Article

AN - SCOPUS:85096087774

JO - IEEE Transactions on Control of Network Systems

JF - IEEE Transactions on Control of Network Systems

SN - 2325-5870

ER -