Learned Collusion

Olivier Compte

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arXiv · 2304.12647

Learned Collusion

Olivier Compte

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Abstract

Q-learning can be described as an all-purpose automaton that provides estimates (Q-values) of the continuation values associated with each available action and follows the naive policy of almost always choosing the action with highest Q-value. We consider a family of automata based on Q-values, whose policy may systematically favor some actions over others, for example through a bias that favors cooperation. We look for stable equilibrium biases, easily learned under converging logit/best-response dynamics over biases, not requiring any tacit agreement. These biases strongly foster collusion or cooperation across a rich array of payoff and monitoring structures, independently of initial Q-values.

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econ.TH cs.AI cs.GT

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Olivier Compte. 2023-04-25. Learned Collusion. https://arxiv.org/abs/2304.12647

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