# Adaptive Incentive Design

arXiv: https://arxiv.org/abs/1806.05749 · HTML: https://mlsanity.com/paper/1806.05749/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jun 2018.

## Built on by (most cited first, 6 of 6)

- [Can Reinforcement Learning Find Stackelberg-Nash Equilibria in General-Sum Markov Games with Myopic Followers?](https://mlsanity.com/paper/2112.13521/) (2021)
- [Information Design in Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2305.06807/) (2023)
- [Principal-Agent Bandit Games with Self-Interested and Exploratory Learning Agents](https://mlsanity.com/paper/2412.16318/) (2024)
- [Equilibrium Bandits: Learning Optimal Equilibria of Unknown Dynamics](https://mlsanity.com/paper/2302.13653/) (2023)
- [FAIRO: Fairness-aware Adaptation in Sequential-Decision Making for Human-in-the-Loop Systems](https://mlsanity.com/paper/2307.05857/) (2023)
- [FinA: Fairness of Adverse Effects in Decision-Making of Human-Cyber-Physical-System](https://mlsanity.com/paper/2311.03468/) (2023)

## Abstract (arXiv)

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.