The emergence of bluff in poker-like games
Andrea Guazzini, Daniele Vilone

TL;DR
This paper introduces adaptive learning models for poker-like games demonstrating how bluffing strategies naturally develop, are rational, and can be evolutionarily stable, with simple algorithms leading to successful bluffing behavior.
Contribution
It presents simple adaptive learning models showing how bluffing strategies emerge and stabilize in poker-like games, highlighting their rational and evolutionary viability.
Findings
Agents learn to bluff using simple algorithms
The best bluffing strategies often lead to winning
Bluffing can be rational and evolutionarily stable
Abstract
We 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.
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Taxonomy
TopicsEvolutionary Game Theory and Cooperation · Game Theory and Applications · Opinion Dynamics and Social Influence
