Multiagent Evaluation Mechanisms

Authors

  • Tal Alon Technion
  • Magdalen Dobson Carnegie Mellon University
  • Ariel Procaccia Carnegie Mellon University
  • Inbal Talgam-Cohen Technion
  • Jamie Tucker-Foltz University of Cambridge

DOI:

https://doi.org/10.1609/aaai.v34i02.5543

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.

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Published

2020-04-03

How to Cite

Alon, T., Dobson, M., Procaccia, A., Talgam-Cohen, I., & Tucker-Foltz, J. (2020). Multiagent Evaluation Mechanisms. Proceedings of the AAAI Conference on Artificial Intelligence, 34(02), 1774-1781. https://doi.org/10.1609/aaai.v34i02.5543

Issue

Section

AAAI Technical Track: Game Theory and Economic Paradigms