# Fairness Incentives for Myopic Agents

arXiv: https://arxiv.org/abs/1705.02321 · HTML: https://mlsanity.com/paper/1705.02321/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 May 2017.

## Built on by (most cited first, 10 of 10)

- [Fair Exploration via Axiomatic Bargaining](https://mlsanity.com/paper/2106.02553/) (2021)
- [Incentivizing Exploration in Linear Bandits under Information Gap](https://mlsanity.com/paper/2104.03860/) (2021)
- [Incentive-Aware Recommender Systems in Two-Sided Markets](https://mlsanity.com/paper/2211.15381/) (2022)
- [Incentivizing Exploration with Linear Contexts and Combinatorial Actions](https://mlsanity.com/paper/2306.01990/) (2023)
- [Incentivizing Compliance with Algorithmic Instruments](https://mlsanity.com/paper/2107.10093/) (2021)
- [When Are Two Lists Better than One?: Benefits and Harms in Joint Decision-making](https://mlsanity.com/paper/2308.11721/) (2023)
- [Tractable Equilibrium Computation in Markov Games through Risk Aversion](https://mlsanity.com/paper/2406.14156/) (2024)
- [Multi-Round Human-AI Collaboration with User-Specified Requirements](https://mlsanity.com/paper/2602.17646/) (2026)
- [Socially-Optimal Mechanism Design for Incentivized Online Learning](https://mlsanity.com/paper/2112.14338/) (2021)
- [Geometry Meets Incentives: Sample-Efficient Incentivized Exploration with Linear Contexts](https://mlsanity.com/paper/2506.01685/) (2025)

## Abstract (arXiv)

We consider settings in which we wish to incentivize myopic agents (such as Airbnb landlords, who may emphasize short-term profits and property safety) to treat arriving clients fairly, in order to prevent overall discrimination against individuals or groups. We model such settings in both classical and contextual bandit models in which the myopic agents maximize rewards according to current empirical averages, but are also amenable to exogenous payments that may cause them to alter their choices. Our notion of fairness asks that more qualified individuals are never (probabilistically) preferred over less qualified ones [Joseph et al]. We investigate whether it is possible to design inexpensive subsidy or payment schemes for a principal to motivate myopic agents to play fairly in all or almost all rounds. When the principal has full information about the state of the myopic agents, we show it is possible to induce fair play on every round with a subsidy scheme of total cost o(T) (for the classic setting with k arms, tildeO(√k3T), and for the d-dimensional linear contextual setting tildeO(d√k3 T)). If the principal has much more limited information (as might often be the case for an external regulator or watchdog), and only observes the number of rounds in which members from each of the k groups were selected, but not the empirical estimates maintained by the myopic agent, the design of such a scheme becomes more complex. We show both positive and negative results in the classic and linear bandit settings by upper and lower bounding the cost of fair subsidy schemes.