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Conference proceeding
Optimal Mechanism Design with Risk-Loving Agents
WEB AND INTERNET ECONOMICS, WINE 2018, v 11316, pp 375-392
01 Jan 2018
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Abstract
One of the most celebrated results in mechanism design is Myerson's characterization of the revenue optimal auction for selling a single item. However, this result relies heavily on the assumption that buyers are indifferent to risk. In this paper we investigate the case where the buyers are risk-loving, i.e. they prefer gambling to being rewarded deterministically. We use the standard model for risk from expected utility theory, where risk-loving behavior is represented by a convex utility function.
We focus our attention on the special case of exponential utility functions. We characterize the optimal auction and show that randomization can be used to extract more revenue than when buyers are risk-neutral. Most importantly, we show that the optimal auction is simple: the optimal revenue can be extracted using a randomized take-it-or-leave-it price for a single buyer and using a loser-pay auction, a variant of the all-pay auction, for multiple buyers. Finally, we show that these results no longer hold for convex utility functions beyond exponential.
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Details
- Title
- Optimal Mechanism Design with Risk-Loving Agents
- Creators
- Evdokia Nikolova - The University of Texas at AustinEmmanouil Pountourakis - The University of Texas at AustinGer Yang - The University of Texas at Austin
- Contributors
- G Christodoulou (Editor)T Harks (Editor)
- Publication Details
- WEB AND INTERNET ECONOMICS, WINE 2018, v 11316, pp 375-392
- Series
- Lecture Notes in Computer Science
- Publisher
- Springer Nature
- Number of pages
- 18
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Computer Science
- Web of Science ID
- WOS:000865933000025
- Scopus ID
- 2-s2.0-85059006740
- Other Identifier
- 991021869008604721
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Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Computer Science, Interdisciplinary Applications
- Computer Science, Theory & Methods
- Economics
- Operations Research & Management Science