Steering the aggregative behavior of noncooperative agents: a nudge framework

TL;DR

This paper introduces a nudge-based framework for guiding noncooperative agents' aggregate behavior using trust-influenced price signals, with proven convergence and trust maintenance.

Contribution

It proposes novel nudge mechanisms incorporating trust dynamics to steer agent behavior, with analytical guarantees of convergence and trust preservation.

Findings

Nudge mechanisms effectively steer behavior to desired outcomes.

Agents develop and maintain trust in the signals over time.

Convergence to target behavior is analytically proven.

Abstract

This paper considers the problem of steering the aggregative behavior of a population of noncooperative price-taking agents towards a desired behavior. Different from conventional pricing schemes where the price is fully available for design, we consider the scenario where a system regulator broadcasts a price prediction signal that can be different from the actual price incurred by the agents. The resulting reliability issues are taken into account by including trust dynamics in our model, implying that the agents will not blindly follow the signal sent by the regulator, but rather follow it based on the history of its accuracy, i.e, its deviation from the actual price. We present several nudge mechanisms to generate suitable price prediction signals that are able to steer the aggregative behavior of the agents to stationary as well as temporal desired aggregative behaviors. We provide analytical convergence guarantees for the resulting multi-components models. In particular, we prove that the proposed nudge mechanisms earn and maintain full trust of the agents, and the aggregative behavior converges to the desired one. The analytical results are complemented by a numerical case study of coordinated charging of plug-in electric vehicles.