The emergence of bluff in poker-like games

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dc.creatorGuazzini, Andrea
dc.creatorVilone, Daniele
dc.date2009-01-22
dc.date.accessioned2026-07-07T12:32:53Z
dc.date.available2026-07-07T12:32:53Z
dc.descriptionWe present a couple of adaptive learning models of poker-like games, by means of which we show how bluffing strategies emerge very naturally, and can also be rational and evolutively stable. Despite their very simple learning algorithms, agents learn to bluff, and the best bluffing player is usually the winner.
dc.descriptionIn a slightly different version, this article is a chapter of the Ph.D. thesis of Andrea Guazzini
dc.identifierhttps://arxiv.org/abs/0901.3365
dc.identifierhttp://arxiv.org/abs/0901.3365
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/216937
dc.subjectPhysics and Society
dc.subjectPopular Physics
dc.titleThe emergence of bluff in poker-like games
dc.typetext

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