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) Software programs are increasingly being adopted by firms to price their goods and services, and this tendency is likely to continue. 1 In this paper, we ask whether pricing algorithms may "autonomously" learn to collude. The possibility arises because of the recent evolution of the software, from rule-based to reinforcement learning programs. The new programs, powered by Artificial Intelligence (AI), are indeed much more autonomous than their precursors. They can develop their pricing strategies from scratch, engaging in active experimentation and adapting to changing environments. In this learning process, they require little or no external guidance.In the light of these developments, concerns have been voiced, by scholars and policymakers alike, that AI pricing algorithms may raise their prices above the competitive level in a coordinated fashion, even if they have not been specifically

We develop a model of advertising markets in an environment where consumers may switch (or "multi-home") across publishers. Consumer switching generates inefficiency in the process of matching advertisers to consumers, because advertisers may not reach some consumers and may impress others too many times. We find that when advertisers are heterogeneous in their valuations for reaching consumers, the switching-induced inefficiency leads lower-value advertisers to advertise on a limited set of publishers, reducing the effective demand for advertising and thus depressing prices. As the share of switching consumers expands (e.g., when consumers adopt the internet for news or increase their use of aggregators), ad prices fall. We demonstrate that increased switching creates an incentive for publishers to invest in quality as well as extend the number of unique users, because larger publishers are favored by advertisers seeking broader "reach" (more unique users) while avoiding inefficient duplication.

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)