Algorithmic Collusion in Cournot Duopoly Market: Evidence from Experimental Economics
This addresses the threat of algorithmic collusion for researchers and policymakers, though it is incremental as it builds on existing concepts in experimental economics.
The paper tackles the problem of algorithmic collusion in a Cournot duopoly market by proposing an algorithm that extorts human rivals to collude, resulting in higher profits for the algorithm and reduced social welfare, with experimental confirmation of this threat.
Algorithmic collusion is an emerging concept in current artificial intelligence age. Whether algorithmic collusion is a creditable threat remains as an argument. In this paper, we propose an algorithm which can extort its human rival to collude in a Cournot duopoly competing market. In experiments, we show that, the algorithm can successfully extorted its human rival and gets higher profit in long run, meanwhile the human rival will fully collude with the algorithm. As a result, the social welfare declines rapidly and stably. Both in theory and in experiment, our work confirms that, algorithmic collusion can be a creditable threat. In application, we hope, the frameworks, the algorithm design as well as the experiment environment illustrated in this work, can be an incubator or a test bed for researchers and policymakers to handle the emerging algorithmic collusion.