Author:
Alon Tal,Dobson Magdalen,Procaccia Ariel,Talgam-Cohen Inbal,Tucker-Foltz Jamie
Abstract
We consider settings where agents are evaluated based on observed features, and assume they seek to achieve feature values that bring about good evaluations. Our goal is to craft evaluation mechanisms that incentivize the agents to invest effort in desirable actions; a notable application is the design of course grading schemes. Previous work has studied this problem in the case of a single agent. By contrast, we investigate the general, multi-agent model, and provide a complete characterization of its computational complexity.
Publisher
Association for the Advancement of Artificial Intelligence (AAAI)
Cited by
26 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. Optimal Decision Making Under Strategic Behavior;Management Science;2024-02-23
2. Addressing Strategic Manipulation Disparities in Fair Classification;Equity and Access in Algorithms, Mechanisms, and Optimization;2023-10-30
3. Authors’ Biographies/Index;The Societal Impacts of Algorithmic Decision-Making;2023-09-07
4. Bibliography;The Societal Impacts of Algorithmic Decision-Making;2023-09-07
5. Mitigating Bias in Algorithmic Decision-making: Evaluating Claims and Practices;The Societal Impacts of Algorithmic Decision-Making;2023-09-07