Journal article
Eliciting truthful reports with partial signals in repeated games
Theoretical computer science, v 988, 114371
12 Mar 2024
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
We consider a repeated game where a player self-reports her usage of a service and is charged a payment accordingly by a center. The center observes a partial signal, representing part of the player's true consumption, which is generated from a publicly known distribution. The player can report any value that does not contradict the signal and the center issues a payment based on the reported information. Such problems find application in net metering billing in the electricity market, where a customer's actual consumption of the electricity network is masked and complete verification is impractical. When the underlying true value is relatively constant, we propose a penalty mechanism that elicits truthful self-reports. Namely, besides charging the player the reported value, the mechanism charges a penalty proportional to her inconsistent reports. We show how fear of uncertainty in the future incentivizes the player to be truthful today. For Bernoulli distributions, we give the complete analysis and optimal strategies given any penalty. Since complete truthfulness is not possible for continuous distributions, we give approximate truthful results by a reduction from Bernoulli distributions. We also extend our mechanism to a multi-player cost-sharing setting and give equilibrium results.
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Details
- Title
- Eliciting truthful reports with partial signals in repeated games
- Creators
- Yutong Wu - Department of Mechanical Engineering, The University of Texas at Austin, 204 E. Dean Keaton Street C2200, Austin, TX 78712-1591, USAAli Khodabakhsh - The University of Texas at AustinBo Li - Hong Kong Polytechnic UniversityEvdokia Nikolova - The University of Texas at AustinEmmanouil Pountourakis - Drexel University, Computer Science
- Publication Details
- Theoretical computer science, v 988, 114371
- Publisher
- Elsevier
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Computer Science
- Web of Science ID
- WOS:001155951300001
- Scopus ID
- 2-s2.0-85181766007
- Other Identifier
- 991021861312604721
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Computer Science, Theory & Methods