Essays in robust mechanism and contract design
Abstract/Contents
- Abstract
- In this thesis, we propose solutions to three problems in the area of robust mechanism design. The first two problems concern revenue maximization by a seller facing several potential buyers whose knowledge of the probability distribution of buyers' valuations is scarce. The third problem concerns contracting under unknown production technology. More specifically: In Chapter 2 (first substantive chapter), we consider the following model. An indivisible object may be sold to one of n agents who know their valuations of the object. The seller would like to use a revenue-maximizing mechanism but her knowledge of the values' distribution is limited: she knows only the means (which may be different) an upper bound for valuations. Valuations may be correlated. Using a constructive approach based on duality, we prove that a mechanism that maximizes the worst-case expected revenue among all deterministic dominant-strategy incentive compatible, ex post individually rational mechanisms takes the following form: (1) the bidders submit bids; (2) for each bidder, a bidder-specific linear function of the bid is calculated (we call it a ``linear score''); (3) the object is awarded to the agent with the highest score, provided it's nonnegative; (4) the winning bidder pays the minimal amount he would need to bid to still win in the auction. The set of optimal mechanisms includes other mechanisms but all those have to be close to the optimal linear score auction in a certain sense. When means are high, all optimal mechanisms share the linearity property. Second-price auction without a reserve is an optimal mechanism when the number of symmetric bidders is sufficiently high. In Chapter 3, we consider a related problem in which the valuations are constrained to be independent draws from a partially known distribution. The seller knows one or two moments of the distribution. We ask what would be a reserve-price in a second-price auction that maximizes worst-case expected revenue. Using a technique different from Chapter 2, we prove that it is always optimal to set the reserve price to seller's own valuation. However, the maxmin reserve price may not be unique. If the number of bidders is sufficiently high, all prices below the seller's valuation, including zero, are also optimal. In the final chapter, we seek a robust solution of a hidden-action, rather than a hidden-information problem. A principal is uncertain about a technology mapping an agent's effort to the distribution of output. The agent is risk neutral and there is a participation constraint but no limited liability constraint. Transfers can be costly. An example of this setting is the case where the principal is a society trying to properly incentivize a firm to carry out innovation. We first show that when the principal employs minimax-regret criterion in the face of the technological uncertainty, an optimal contract is affine. We then characterize the full set of optimal contracts. A contract is optimal if and only if it lies within certain affine, increasing bounds that collapse to a point when output reaches its maximum value
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2020; ©2020 |
| Publication date | 2020; 2020 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Suzdaltsev, Aleksei |
|---|---|
| Degree supervisor | Iancu, Dan |
| Thesis advisor | Iancu, Dan |
| Thesis advisor | Carroll, Gabriel |
| Thesis advisor | Skrzypacz, Andrzej, 1973- |
| Degree committee member | Carroll, Gabriel |
| Degree committee member | Skrzypacz, Andrzej, 1973- |
| Associated with | Stanford University, Graduate School of Business. |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Aleksei Suzdaltsev |
|---|---|
| Note | Submitted to the Graduate School of Business |
| Thesis | Thesis Ph.D. Stanford University 2020 |
| Location | electronic resource |
Access conditions
- Copyright
- © 2020 by Aleksei Suzdaltsev
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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