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Experience-weighted attraction learning in network coordination games

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  • Fulin Guo
Abstract
This paper studies the action dynamics of network coordination games with bounded-rational agents. I apply the experience-weighted attraction (EWA) model to the analysis as the EWA model has several free parameters that can capture different aspects of agents' behavioural features. I show that the set of possible long-term action patterns can be largely different when the behavioural parameters vary, ranging from a unique possibility in which all agents favour the risk-dominant option to some set of outcomes richer than the collection of Nash equilibria. Monotonicity and non-monotonicity in the relationship between the number of possible long-term action profiles and the behavioural parameters are explored. I also study the question of influential agents in terms of whose initial predispositions are important to the actions of the whole network. The importance of agents can be represented by a left eigenvector of a Jacobian matrix provided that agents' initial attractions are close to some neutral level. Numerical calculations examine the predictive power of the eigenvector for the long-run action profile and how agents' influences are impacted by their behavioural features and network positions.

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  • Fulin Guo, 2023. "Experience-weighted attraction learning in network coordination games," Papers 2310.18835, arXiv.org.
    • Handle: RePEc:arx:papers:2310.18835
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    References listed on IDEAS

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