This paper develops a formal framework to assess policies of learning algorithms in economic games. We investigate whether reinforcementlearning agents with collusive pricing policies can successfully extrapolate collusive behavior from training to the market. We find that in testing environments collusion consistently breaks down. Instead, we observe static Nash play. We then show that restricting algorithms' strategy space can make algorithmic collusion robust, because it limits overfitting to rival strategies. Our findings suggest that policy-makers should focus on firm behavior aimed at coordinating algorithm design in order to make collusive policies robust.

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This paper studies dynamic monopoly pricing for a broad class of Coasian and Non-Coasian settings. We show that the driving force behind pricing dynamics is the seller's incentive to trade up consumers to higher-valued consumption options. In Coasian settings, consumers can be traded up from the static optimum, and pricing dynamics arise until all trading-up opportunities are exhausted. In Non-Coasian settings, consumers cannot be traded up from the static optimum, and no pricing dynamics arise. Hence, dynamic monopoly pricing can be characterized by checking for trading-up opportunities in the static optimum.