Setting Fair Incentives to Maximize Improvement
Saba Ahmadi, Hedyeh Beyhaghi, Avrim Blum, Keziah Naggita

TL;DR
This paper develops algorithms to set short-term goals for agents to maximize overall improvement and fairness, considering different improvement capacities and addressing non-monotonic effects of target level placement.
Contribution
It introduces algorithms for optimal target level setting under social welfare and fairness, handling both common and individualized improvement capacities, and extends to learning scenarios.
Findings
Algorithms achieve near-optimal improvement for social welfare and fairness.
Existence of approximately optimal target placements in the common capacity model.
Counterexamples show structural limitations in the individualized capacity model.
Abstract
We consider the problem of helping agents improve by setting short-term goals. Given a set of target skill levels, we assume each agent will try to improve from their initial skill level to the closest target level within reach or do nothing if no target level is within reach. We consider two models: the common improvement capacity model, where agents have the same limit on how much they can improve, and the individualized improvement capacity model, where agents have individualized limits. Our goal is to optimize the target levels for social welfare and fairness objectives, where social welfare is defined as the total amount of improvement, and fairness objectives are considered where the agents belong to different underlying populations. A key technical challenge of this problem is the non-monotonicity of social welfare in the set of target levels, i.e., adding a new target level may…
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Taxonomy
TopicsExperimental Behavioral Economics Studies · Income, Poverty, and Inequality · Economic theories and models
