Paper Overview
A No-Regret Framework for Adaptive Incentive Design
Takeaway: The paper introduces a no-Regret Adaptive Incentive Design framework (RAID) that learns agents' private costs from repeated strategic responses while steering the Nash equilibrium toward a social optimum.
Why It Matters
In energy, traffic, digital platforms, and other networked systems, a planner often cannot directly prescribe the participants' actions but instead influences them through incentive signals such as pricing, subsidies, tolls, and taxes. One challenge is that agents' costs are private, so the planner must learn it online.
Technical Contribution
- Formulates No-Regret Adaptive Incentive Design for nonlinear games with continuous actions and private agent costs.
- Constructs a strongly consistent estimator for agent costs.
- Designs switching incentive policies that alternate between exploration and exploitation and achieve almost-sure regret guarantees.
- Extends the method to an endogenous-noise response model using repeated sampling.
Who Should Read This
Researchers working on incentive design, game theory, adaptive control, online learning, smart grids, traffic networks, and strategic multi-agent systems.
Links
@misc{vasileiou2026noregret,
title = {{A No-Regret Framework for Adaptive Incentive Design}},
author = {Vasileiou, G. and Zhang, L. and Zhang, S.},
year = {2026},
eprint = {2606.02529},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2606.02529}
}