We propose a new theory of exclusive dealing. The theory is based on the assumption that a dominant firm has a competitive advantage over its rivals, and that the buyers' willingness to pay for the product is private information. In this setting, the dominant firm can impose contractual restrictions on buyers without necessarily compensating them, implying that exclusive dealing contracts can be both profitable and anticompetitive. We discuss the general implications of the theory for competition policy and illustrate by examples its applicability to antitrust cases.

We analyze firms that compete by means of exclusive contracts and market-share discounts (conditional on the seller's share of customers' total purchases). With incomplete information about demand, firms have a unilateral incentive to use these contractual arrangements to better extract buyers' informational rents. However, exclusive contracts intensify competition, thus reducing prices and profits and (in all Pareto undominated equilibria) increasing welfare. Market-share discounts, by contrast, produce a double marginalization effect that leads to higher prices and harms buyers. We discuss the implications of these results for competition policy.

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.