Artificial Intelligence, Algorithmic Pricing, and Collusion
Emilio Calvano, Giacomo Calzolari, Vincenzo Denicolò, Sergio Pastorello
American Economic Review · 2020 · Volume 110 · Issue 10
Abstract
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.
Keywords
Firm Behavior: Theory, Market Structure, Pricing, and Design: Oligopoly and Other Forms of Market Imperfection, Search, Learning, Information and Knowledge, Communication, Belief, Unawareness, Monopoly, Monopolization Strategies, Oligopoly and Other Imperfect Markets
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