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dc.contributor.authorJaleel, Hassan
dc.contributor.authorShamma, Jeff S.
dc.date.accessioned2021-01-14T12:10:37Z
dc.date.available2021-01-14T12:10:37Z
dc.date.issued2020
dc.identifier.citationJaleel, H., & Shamma, J. S. (2020). A Hierarchical Approach For The Stochastic Stability Analysis Of Evolutionary Dynamics. 2020 59th IEEE Conference on Decision and Control (CDC). doi:10.1109/cdc42340.2020.9304218
dc.identifier.isbn978-1-7281-7448-8
dc.identifier.issn0743-1546
dc.identifier.doi10.1109/CDC42340.2020.9304218
dc.identifier.urihttp://hdl.handle.net/10754/666908
dc.description.abstractWe propose a hierarchical approach for the stochastic stability analysis of evolutionary dynamics. Each layer in the hierarchy represents a compromise between computational effort and the resolution of information about the long-run behavior of evolutionary dynamics. Previously, we proposed a graphical reformulation of Evolutionarily Sable Strategy (ESS) analysis through which we identified a set of strategies that cannot be ESS. Moreover, we also computed a set of strategies that was guaranteed to contain the set of stochastically stable strategies. The previous analysis was developed by considering transitions resulting from single mutations only. We extend the graphical approach to higher order analysis by incorporating mutations of higher order and show that we can refine our solution estimate by identifying smaller subsets of strategies that contain the set of stochastically stable strategies. However, this refinement comes at a cost of increase in computational budget.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.urlhttps://ieeexplore.ieee.org/document/9304218/
dc.relation.urlhttps://ieeexplore.ieee.org/document/9304218/
dc.relation.urlhttps://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9304218
dc.rightsArchived with thanks to IEEE
dc.titleA Hierarchical Approach For The Stochastic Stability Analysis Of Evolutionary Dynamics
dc.typeConference Paper
dc.contributor.departmentComputer, Electrical and Mathematical Science and Engineering (CEMSE) Division
dc.contributor.departmentElectrical and Computer Engineering Program
dc.contributor.departmentRISC Laboratory
dc.conference.date14-18 Dec. 2020
dc.conference.name2020 59th IEEE Conference on Decision and Control (CDC)
dc.conference.locationJeju Island, Korea (South)
dc.eprint.versionPost-print
dc.contributor.institutionSyed Babar Ali School of Science & Engineering at Lahore University of Management Science (LUMS),Intelligent Machines & Sociotechnical Systems (iMaSS) Lab,Department of Electrical Engineering,Lahore,Pakistan
kaust.personShamma, Jeff S.


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