Working Papers

Firms have been adopting AI learning algorithms to automatically set product prices and advertising auction bids on e-commerce platforms. When firms compete using such algorithms, one concern is that of tacit collusion—the algorithms learn to settle on higher than competitive prices which increase firm profits, but hurt consumers. We empirically investigate the impact of competing reinforcement learning algorithms to determine if they are always harmful to consumers, in a setting where firms learn to make two-dimensional decisions on pricing and advertising together. Our analysis uses a multi-agent reinforcement learning implementation of the Q-learning algorithm, which we calibrate to estimates from a large-scale dataset collected from Amazon.com. We find that learning algorithms can facilitate win-win-win outcomes that are beneficial for consumers, sellers, and even the platform when consumers have high search costs, i.e., the algorithms learn to collude on lower than competitive prices. The intuition is that algorithms learn to coordinate on lower bids, which lowers advertising costs, leading to lower prices for consumers and enlarging the demand on the platform. We collect and analyze a large-scale high-frequency keyword product search dataset from Amazon.com and estimate consumer search costs. We provide policy guidance by identifying product markets with higher consumer search costs that could benefit from tacit collusion, and markets where regulation on algorithmic pricing might be most needed. Further, we show that even if the platform responds strategically by adjusting the ad auction reserve price or the sales commission rate, the beneficial outcomes for both sellers and consumers are likely to persist.

Recently, the US mandated healthcare price transparency to facilitate easier comparison of healthcare prices. However, the potential effectiveness of this policy is an open question. We use a large-scale health insurance claims dataset to estimate the potential maximum savings from price transparency. We focus on short-term, demand-side estimates, where patients can shop around and switch to cheaper providers. We analyze the set "shoppable" services whose price information must be reported online. Initially, our data points to a large potential for savings due to a large degree of price dispersion. However, when viewed from the consumer shopping perspective, even the most optimistic estimates of potential savings become limited. The reasons are that the location and insurance network of the patient, the structure of healthcare insurance payments, and the information made available by the transparency rule lower patients’ incentive to save. We find that the best-case scenario for patients’ out-of-pocket savings from price - shopping is 3% of the total cost on average. Our analysis suggests that the existing estimates in the literature might be overestimated, as they overlook the consumer shopping perspective. Hence, patients’ potential savings and the demand-side impact of the transparency rule might not be as impactful as initially hoped for.

We analyze recommendation algorithms that firms can engineer to strategically provide information to consumers about products with uncertain matches to their tastes. Monopolists who cannot alter prices can design recommendation algorithms to oversell, i.e., that recommend products even if they are not a perfect fit, instead of algorithmically recommending perfectly matching products. However, when prices are endogenous or when competition is rampant, firms opt to reduce their overselling efforts and instead choose to fully reveal the product's match (i.e., maximize recall and precision). As competition strengthens, the algorithms will shift to demarket their products, i.e., under-recommend highly fitting products, in order to soften price competition. When a platform designs a recommendation algorithm for products sold by third-party sellers, we find that demarketing might be a more prevalent strategy of the platform. Additionally, we find that platforms bound by fairness constraints may gain lower profits compared to letting sellers compete, while discriminatory designs do not necessarily result in preferential outcomes for a specific seller.

Two informed contestants compete in a contest, and the organizer ex-ante designs a public anonymous disclosure policy to maximize contestants’ total effort. We fully characterize ridge distributions, under which the organizer achieves the first best outcome in equilibrium: the allocation is efficient, and the entire surplus goes to the organizer. When the prior is more positively correlated than ridge distributions, the first-best outcome is achievable by the signal that solely generates ridge distributions as posteriors.