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dc.contributor.authorPark, Shinkyu
dc.contributor.authorMartins, Nuno C.
dc.contributor.authorShamma, Jeff S.
dc.date.accessioned2019-12-19T08:57:14Z
dc.date.available2019-12-19T08:57:14Z
dc.date.issued2019-03-05
dc.identifier.urihttp://hdl.handle.net/10754/660698
dc.description.abstractWe consider that at every instant each member of a population, which we refer to as an agent, selects one strategy out of a finite set. The agents are nondescript, and their strategy choices are described by the so-called population state vector, whose entries are the portions of the population selecting each strategy. Likewise, each entry of the so-called payoff vector is the reward attributed to a strategy. We consider that a finite-dimensional nonlinear dynamical system, denoted as payoff dynamics model (PDM), specifies a mechanism that determines the payoff as a causal map of the population state. A bounded-rationality protocol, which is often inspired on evolutionary biology principles, governs how each agent continually revises its strategy based on complete or partial knowledge of the population state and payoff. The population is protocol-homogeneous but is otherwise strategy-heterogeneous considering that the agents are allowed to select distinct strategies concurrently. A stochastic mechanism determines the instants when agents revise their strategies, but we consider that the population is large enough that, with high probability, the population state can be approximated with arbitrary accuracy uniformly over any finite horizon by a so-called (deterministic) mean population state. We propose an approach that takes advantage of passivity principles to obtain sufficient conditions determining, for a given protocol and PDM, when the mean population state is guaranteed to converge to a meaningful set of equilibria, which could be either an appropriately defined extension of Nash's for the PDM or a perturbed version of it.
dc.publisherarXiv
dc.relation.urlhttps://arxiv.org/pdf/1903.02018
dc.rightsArchived with thanks to arXiv
dc.titlePayoff Dynamics Model and Evolutionary Dynamics Model: Feedback and Convergence to Equilibria
dc.typePreprint
dc.contributor.departmentElectrical Engineering Program
dc.contributor.departmentCenter of Excellence for NEOM Research
dc.eprint.versionPre-print
dc.contributor.institutionMechanical and Aerospace Engineering, Princeton University, Princeton, NJ 08544, USA.
dc.contributor.institutionElectrical and Computer Engineering and the Institute for Systems Research, University of Maryland, College Park, MD, 20742, USA.
dc.identifier.arxivid1903.02018
kaust.personShamma, Jeff S.
refterms.dateFOA2019-12-19T08:58:04Z


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