No Screening is More Efficient with Multiple Objects

We study efficient mechanism design for allocating multiple heterogeneous objects. We aim to maximize the residual surplus, the total value generated from an allocation minus the costs for screening agents' values. We discover a robust trend indicating that no-screening mechanisms such as serial dictatorship with exogenous priority order tend to perform better as the variety of goods increases. We analyze the underlying reasons by characterizing efficient mechanisms in a stylized environment. We also apply an automated mechanism design approach to numerically derive efficient mechanisms and validate the trend in general environments. Building on this implication, we propose the register-invite-book system (RIB) as an efficient system for scheduling vaccination against pandemic diseases.

Deviation-Based Learning

We propose deviation-based learning, a new approach to training recommender systems. In the beginning, the recommender and rational users have different pieces of knowledge, and the recommender needs to learn the users' knowledge to make better recommendations. The recommender learns users' knowledge by observing whether each user followed or deviated from her recommendations. We show that learning frequently stalls if the recommender always recommends a choice: users tend to follow the recommendation blindly, and their choices do not reflect their knowledge. Social welfare and the learning rate are improved drastically if the recommender abstains from recommending a choice when she predicts that multiple arms will produce a similar payoff.

On Statistical Discrimination as a Failure of Social Learning: A Multi-Armed Bandit Approach

We analyze statistical discrimination using a multi-armed bandit model where myopic firms face candidate workers arriving with heterogeneous observable characteristics. The association between the worker's skill and characteristics is unknown ex ante; thus, firms need to learn it. In such an environment, laissez-faire may result in a highly unfair and inefficient outcome---myopic firms are reluctant to hire minority workers because the lack of data about minority workers prevents accurate estimation of their performance. Consequently, minority groups could be perpetually underestimated---they are never hired, and therefore, data about them is never accumulated. We proved that this problem becomes more serious when the population ratio is imbalanced, as is the case in many extant discrimination problems. We consider two affirmative-action policies for solving this dilemma: One is a subsidy rule that is based on the popular upper confidence bound algorithm, and another is the Rooney Rule, which requires firms to interview at least one minority worker for each hiring opportunity. Our results indicate temporary affirmative actions are effective for statistical discrimination caused by data insufficiency.