Artificial Intelligence, Algorithmic Pricing, and Collusion
By Emilio Calvano, Giacomo Calzolari, Vincenzo Denicolò, Sergio Pastorello. The American Economic Review, 2020
514 citations recorded by the source corpus.
Increasingly, algorithms are supplanting human decision-makers in pricing goods and services. To analyze the possible consequences, we study experimentally the behavior of algorithms powered by Artificial Intelligence (Q-learning) in a workhorse oligopoly model of repeated price competition. We find that the algorithms consistently learn to charge supracompetitive prices, without communicating with one another. The high prices are sustained by collusive strategies with a finite phase of punishment followed by a gradual return to cooperation. This finding is robust to asymmetries in cost or demand, changes in the number of players, and various forms of uncertainty. (JEL D21, D43, D83, L12, L13)
Subfields: Economics and Econometrics, Management Science and Operations Research
View Paper: https://www.semanticscholar.org/paper/ac1b26081b65d65888c96fa4d2e67a1930662db2
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Citations in the corpus
- Competition in Pricing Algorithms (2020, background): “Calvano et al. (2019) show that this concern applies to standard reinforcement-learning algorithms by demonstrating that they converge to supracompetive prices.”
- Economics, Fairness and Algorithmic Bias (2020, unknown): “Some economists suggests that machine-learning algorithms may achieve collusion more easily than humans even without the advantage of transparency because of the machines’ faster ability to learn through trial and error (Ezrachi and Stucke, 2016; Calvano et al., 2018).”
- Algorithmic Pricing and Competition: Empirical Evidence from the German Retail Gasoline Market (2023, background): “Reinforcement learning has been the focus of existing simulations-based evidence of the possibility of algorithmic collusion (Calvano et al 2020, Klein 2019).”