Incentive design for learning in user-recommendation systems with time-varying states

TL;DR

This paper studies how to design incentives in user-recommendation systems to encourage truthful information sharing among strategic users with private signals, enabling effective learning of a time-varying state.

Contribution

It formulates the problem as a decentralized stochastic control problem and proposes incentive schemes that align user incentives with the system's learning objectives.

Findings

Optimal policies for the team problem are characterized.

Incentive schemes ensure truthful signal revelation with minimal payments.

The gap between strategic and team objectives is kept small.

Abstract

We consider the problem of how strategic users with asymmetric information can learn an underlying time varying state in a user-recommendation system. Users who observe private signals about the state, sequentially make a decision about buying a product whose value varies with time in an ergodic manner. We formulate the team problem as an instance of decentralized stochastic control problem and characterize its optimal policies. With strategic users, we design incentives such that users reveal their true private signals, so that the gap between the strategic and team objective is small and the overall expected incentive payments are also small.