Learned Collusion
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arXiv
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866912399531966464 |
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| author | Compte, Olivier |
| author_facet | Compte, Olivier |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2304_12647 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Learned Collusion Compte, Olivier Theoretical Economics Artificial Intelligence Computer Science and Game Theory 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. |
| title | Learned Collusion |
| topic | Theoretical Economics Artificial Intelligence Computer Science and Game Theory |
| url | https://arxiv.org/abs/2304.12647 |