Adaptive Incentive Design

TL;DR

This paper introduces an adaptive incentive design method using control and optimization techniques to learn and influence strategic agents' decision-making processes in dynamic environments.

Contribution

It develops a novel algorithm that simultaneously learns agents' utility functions and optimizes incentives, with proven convergence in various noise conditions.

Findings

Algorithm converges in noise-free settings

Algorithm converges under noisy conditions

Effective in guiding agents toward desired responses

Abstract

We apply control theoretic and optimization techniques to adaptively design incentives. In particular, we consider the problem of a planner with an objective that depends on data from strategic decision makers. The planner does not know the process by which the strategic agents make decisions. Under the assumption that the agents are utility maximizers, we model their interactions as a non-cooperative game and utilize the Nash equilibrium concept as well as myopic update rules to model the selection of their decision. By parameterizing the agents' utility functions and the incentives offered, we develop an algorithm that the planner can employ to learn the agents' decision-making processes while simultaneously designing incentives to change their response to a more desirable response from the planner's perspective. We provide convergence results for this algorithm both in the noise-free and noisy cases and present illustrative examples.