<?xml version="1.0" encoding="UTF-8"?><?xml-stylesheet type="text/xsl" href="static/style.xsl"?><OAI-PMH xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/ http://www.openarchives.org/OAI/2.0/OAI-PMH.xsd"><responseDate>2026-06-28T08:35:49Z</responseDate><request verb="GetRecord" identifier="oai:riubu.ubu.es:10259/7008" metadataPrefix="mods">https://riubu.ubu.es/oai/request</request><GetRecord><record><header><identifier>oai:riubu.ubu.es:10259/7008</identifier><datestamp>2024-05-10T11:55:29Z</datestamp><setSpec>com_10259_3830</setSpec><setSpec>com_10259_5086</setSpec><setSpec>com_10259_2604</setSpec><setSpec>col_10259_3832</setSpec></header><metadata><mods:mods xmlns:mods="http://www.loc.gov/mods/v3" xmlns:doc="http://www.lyncode.com/xoai" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-1.xsd">
<mods:name>
<mods:namePart>Izquierdo, Segismundo S.</mods:namePart>
</mods:name>
<mods:name>
<mods:namePart>Izquierdo Millán, Luis Rodrigo</mods:namePart>
</mods:name>
<mods:extension>
<mods:dateAvailable encoding="iso8601">2022-09-22T07:30:11Z</mods:dateAvailable>
</mods:extension>
<mods:extension>
<mods:dateAccessioned encoding="iso8601">2022-09-22T07:30:11Z</mods:dateAccessioned>
</mods:extension>
<mods:originInfo>
<mods:dateIssued encoding="iso8601">2022-09</mods:dateIssued>
</mods:originInfo>
<mods:identifier type="issn">0022-0531</mods:identifier>
<mods:identifier type="uri">http://hdl.handle.net/10259/7008</mods:identifier>
<mods:identifier type="doi">10.1016/j.jet.2022.105553</mods:identifier>
<mods:abstract>We consider a family of population game dynamics known as Best Experienced Payoff Dynamics. Under these dynamics, when agents are given the opportunity to revise their strategy, they test some of their possible strategies a fixed number of times. Crucially, each strategy is tested against a new randomly drawn set of opponents. The revising agent then chooses the strategy whose total payoff was highest in the test, breaking ties according to a given tie-breaking rule. Strict Nash equilibria are rest points of these dynamics, but need not be stable. We provide some simple formulas and algorithms to determine the stability or instability of strict Nash equilibria.</mods:abstract>
<mods:language>
<mods:languageTerm>eng</mods:languageTerm>
</mods:language>
<mods:accessCondition type="useAndReproduction">http://creativecommons.org/licenses/by/4.0/</mods:accessCondition>
<mods:accessCondition type="useAndReproduction">info:eu-repo/semantics/openAccess</mods:accessCondition>
<mods:accessCondition type="useAndReproduction">Atribución 4.0 Internacional</mods:accessCondition>
<mods:subject>
<mods:topic>Best experienced payoff</mods:topic>
</mods:subject>
<mods:subject>
<mods:topic>Procedural rationality</mods:topic>
</mods:subject>
<mods:subject>
<mods:topic>Payoff-sampling dynamics</mods:topic>
</mods:subject>
<mods:subject>
<mods:topic>Stability</mods:topic>
</mods:subject>
<mods:titleInfo>
<mods:title>Stability of Strict Equilibria in Best Experienced Payoff Dynamics: Simple Formulas and Applications</mods:title>
</mods:titleInfo>
<mods:genre>info:eu-repo/semantics/article</mods:genre>
</mods:mods></metadata></record></GetRecord></OAI-PMH>