Game-theoretic Mechanisms for Eliciting Accurate Information

Boi Faltings

IJCAI 2023poster8 citations

Game-theoretic Mechanisms for Eliciting Accurate Information

Abstract

Artificial Intelligence often relies on information obtained from others through crowdsourcing, federated learning, or data markets. It is crucial to ensure that this data is accurate. Over the past 20 years, a variety of incentive mechanisms have been developed that use game theory to reward the accuracy of contributed data. These techniques are applicable to many settings where AI uses contributed data. This survey categorizes the different techniques and their properties, and shows their limits and tradeoffs. It identifies open issues and points to possible directions to address these.

Survey: Game Theory and Economic ParadigmsSurvey: Machine LearningSurvey: Humans and AI

BibTeX

@inproceedings{ijcai2023p740,
  title     = {Game-theoretic Mechanisms for Eliciting Accurate Information},
  author    = {Faltings, Boi},
  booktitle = {Proceedings of the Thirty-Second International Joint Conference on
               Artificial Intelligence, {IJCAI-23}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {Edith Elkind},
  pages     = {6601--6609},
  year      = {2023},
  month     = {8},
  note      = {Survey Track},
  doi       = {10.24963/ijcai.2023/740},
  url       = {https://doi.org/10.24963/ijcai.2023/740},
}