We consider se ings in which we wish to incentivize myopic agents (such as Airbnb landlords, who may emphasize short-term pro ts and property safety) to treat arriving clients fairly, in order to prevent overall discrimination against individuals or groups. We model such se ings in both classical and contextual bandit models in which the myopic agents maximize rewards according to current empirical averages, but are also amenable to exogenous payments that may cause them to alter their choices. Our notion of fairness asks that more quali ed individuals are never (probabilistically) preferred over less quali ed ones [8] . We investigate whether it is possible to design inexpensive subsidy or payment schemes for a principal to motivate myopic agents to play fairly in all or almost all rounds. When the principal has full information about the state of the myopic agents, we show it is possible to induce fair play on every round with a subsidy scheme of total cost o(T ) (for the classic se ing with k arms,Õ ( √ k 3 T ), and for the d-dimensional linear contextual se ingÕ (d √ k 3 T )). If the principal has much more limited information (as might o en be the case for an external regulator or watchdog), and only observes the number of rounds in which members from each of the k groups were selected, but not the empirical estimates maintained by the myopic agent, the design of such a scheme becomes more complex. We show both positive and negative results in the classic and linear bandit se ings by upper and lower bounding the cost of fair subsidy schemes.
REF investigate whether it is possible to design payment schemes for a principal to motivate myopic agents to play fairly in almost all rounds.
Fairness Incentives for Myopic Agents
EC '17
Mathematics Economics Computer Science