Conference proceeding
Revenue Maximization with an Uncertainty-Averse Buyer
SODA'18: PROCEEDINGS OF THE TWENTY-NINTH ANNUAL ACM-SIAM SYMPOSIUM ON DISCRETE ALGORITHMS, pp.2050-2068
01 Jan 2018
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Most work in mechanism design assumes that buyers are risk neutral; some considers risk aversion arising due to a non-linear utility for money. Yet behavioral studies have established that real agents exhibit risk attitudes which cannot be captured by any expected utility model. We initiate the study of revenue-optimal mechanisms under behavioral models beyond expected utility theory. We adopt a model from prospect theory which arose to explain these discrepancies and incorporates agents under-weighting uncertain outcomes. In our model, an event occurring with probability x < 1 is worth strictly less to the agent than x times the value of the event when it occurs with certainty.
We present three main results. First, we characterize optimal mechanisms as menus of two-outcome lotteries. Second, we show that under a reasonable bounded-risk-aversion assumption, posted pricing obtains a constant approximation to the optimal revenue. Notably, this result is "risk-robust" in that it does not depend on the details of the buyer's risk attitude. Third, we consider dynamic settings in which the buyer's uncertainty about his future value may allow the seller to extract more revenue. In contrast to the positive result above, here we show it is not possible to achieve any constant-factor approximation to revenue using deterministic mechanisms in a risk-robust manner.
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Details
- Title
- Revenue Maximization with an Uncertainty-Averse Buyer
- Creators
- Shuchi Chawla - University of Wisconsin–MadisonKira Goldner - University of WashingtonJ. Benjamin Miller - University of Wisconsin–MadisonEmmanouil Pountourakis - Univ Texas Austin, Austin, TX 78712 USAAssoc Comp Machinery
- Publication Details
- SODA'18: PROCEEDINGS OF THE TWENTY-NINTH ANNUAL ACM-SIAM SYMPOSIUM ON DISCRETE ALGORITHMS, pp.2050-2068
- Conference
- SODA'18: TWENTY-NINTH ANNUAL ACM-SIAM SYMPOSIUM ON DISCRETE ALGORITHMS, 29th
- Publisher
- Assoc Computing Machinery
- Number of pages
- 19
- Grant note
- CCF-1733832; CCF-1350823; CCF-1216103; CCF-1331863; CCF-1320854; CCF-1617505; CCF-1420381 / NSF; National Science Foundation (NSF) Cisco graduate fellowship Microsoft Research PhD Fellowship
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Computer Science (Computing)
- Identifiers
- 991021869008804721
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Computer Science, Theory & Methods
- Mathematics, Applied