The emergence of bluff in poker-like games

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.