Incentivizing Exploration with Selective Data Disclosure

We study the design of rating systems that incentivize (more) efficient social learning among self-interested agents. Agents arrive sequentially and are presented with a set of possible actions, each of which yields a positive reward with an unknown probability. A disclosure policy sends messages about the rewards of previously-chosen actions to arriving agents. These messages can alter agents' incentives towards exploration, taking potentially sub-optimal actions for the sake of learning more about their rewards. Prior work achieves much progress with disclosure policies that merely recommend an action to each user, without any other supporting information, and sometimes recommend exploratory actions. All this work relies heavily on standard, yet very strong rationality assumptions. However, these assumptions are quite problematic in the context of the motivating applications: recommendation systems such as Yelp, Amazon, or Netflix, and macthing markets such as AirBnB. It is very unclear whether users would know and understand a complicated disclosure policy announced by the principal, let alone trust the principal to faithfully implement it. (The principal may deviate from the announced policy either intentionally, or due to insufficient information about the users, or because of bugs in implementation.) Even if the users understand the policy and trust that it was implemented as claimed, they might not react to it rationally, particularly given the lack of supporting information and the possibility of being singled out for exploration. For example, users may find such disclosure policies unacceptable and leave the system.