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<title>Best experienced payoff dynamics and cooperation in the centipede game</title>
<creator>Sandholm, William H.</creator>
<creator>Izquierdo, Segismundo S.</creator>
<creator>Izquierdo Millán, Luis Rodrigo</creator>
<subject>Evolutionary game theory</subject>
<subject>backward induction</subject>
<subject>centipede game</subject>
<subject>computational algebra</subject>
<description>We study population game dynamics under which each revising agent tests each&#xd;
of his strategies a fixed number of times, with each play of each strategy being&#xd;
against a newly drawn opponent, and chooses the strategy whose total payoff was&#xd;
highest. In the centipede game, these best experienced payoff dynamics lead to cooperative&#xd;
play. When strategies are tested once, play at the almost globally stable&#xd;
state is concentrated on the last few nodes of the game, with the proportions of&#xd;
agents playing each strategy being largely independent of the length of the game.&#xd;
Testing strategies many times leads to cyclical play.</description>
<date>2020-03-09</date>
<date>2020-03-09</date>
<date>2019-11</date>
<type>info:eu-repo/semantics/article</type>
<identifier>1933-6837</identifier>
<identifier>http://hdl.handle.net/10259/5236</identifier>
<identifier>10.3982/TE3565</identifier>
<language>eng</language>
<relation>Theoretical Economics. 2019, V. 14, n. 4, p. 1347–1386</relation>
<relation>https://econtheory.org/ojs/index.php/te/issue/view/45</relation>
<relation>info:eu-repo/grantAgreement/MINECO/ECO2017-83147-&#xd;
C2-2-P</relation>
<relation>info:eu-repo/grantAgreement/MECD/PRX15/00362</relation>
<relation>info:eu-repo/grantAgreement/MECD/PRX16/00048</relation>
<rights>http://creativecommons.org/licenses/by-nc/4.0/</rights>
<rights>info:eu-repo/semantics/openAccess</rights>
<rights>Atribución-NoComercial 4.0 Internacional</rights>
<publisher>Econometric Society</publisher>
</thesis></metadata></record></GetRecord></OAI-PMH>