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Fairness Incentives for Myopic Agents

Published: 20 June 2017 Publication History

Abstract

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 qualifie ones [8].
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, ~{O}(\sqrtk3T), and for the d-dimensional linear contextual setting ~{O}(d\sqrtk3T)). 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.

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References

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cover image ACM Conferences
EC '17: Proceedings of the 2017 ACM Conference on Economics and Computation
June 2017
740 pages
ISBN:9781450345279
DOI:10.1145/3033274
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 20 June 2017

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EC '17: ACM Conference on Economics and Computation
June 26 - 30, 2017
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EC '17 Paper Acceptance Rate 75 of 257 submissions, 29%;
Overall Acceptance Rate 664 of 2,389 submissions, 28%

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  • (2023)Incentivizing exploration with linear contexts and combinatorial actionsProceedings of the 40th International Conference on Machine Learning10.5555/3618408.3619675(30570-30583)Online publication date: 23-Jul-2023
  • (2023)Incentivizing Exploration in Linear Contextual Bandits under Information GapProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3608794(415-425)Online publication date: 14-Sep-2023
  • (2022)Socially-Optimal Mechanism Design for Incentivized Online LearningIEEE INFOCOM 2022 - IEEE Conference on Computer Communications10.1109/INFOCOM48880.2022.9796676(1828-1837)Online publication date: 2-May-2022
  • (2021)Incentivising Exploration and Recommendations for Contextual Bandits with PaymentsMulti-Agent Systems and Agreement Technologies10.1007/978-3-030-66412-1_11(159-170)Online publication date: 5-Jan-2021
  • (2020)Structured linear contextual banditsProceedings of the 37th International Conference on Machine Learning10.5555/3524938.3525775(9026-9035)Online publication date: 13-Jul-2020
  • (2020)Metric-free individual fairness in online learningProceedings of the 34th International Conference on Neural Information Processing Systems10.5555/3495724.3496665(11214-11225)Online publication date: 6-Dec-2020
  • (2020)Bayesian Incentive-Compatible Bandit ExplorationOperations Research10.1287/opre.2019.1949Online publication date: 2-Jul-2020
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  • (2020)Multi-Round Cooperative Search Games with Multiple PlayersJournal of Computer and System Sciences10.1016/j.jcss.2020.05.003Online publication date: May-2020
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