Communication in Economic Mechanisms"
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Implementation Theory*
Chapter 5 IMPLEMENTATION THEORY* ERIC MASKIN Institute for Advanced Study, Princeton, NJ, USA TOMAS SJOSTROM Department of Economics, Pennsylvania State University, University Park, PA, USA Contents Abstract 238 Keywords 238 1. Introduction 239 2. Definitions 245 3. Nash implementation 247 3.1. Definitions 248 3.2. Monotonicity and no veto power 248 3.3. Necessary and sufficient conditions 250 3.4. Weak implementation 254 3.5. Strategy-proofness and rich domains of preferences 254 3.6. Unrestricted domain of strict preferences 256 3.7. Economic environments 257 3.8. Two agent implementation 259 4. Implementation with complete information: further topics 260 4.1. Refinements of Nash equilibrium 260 4.2. Virtual implementation 264 4.3. Mixed strategies 265 4.4. Extensive form mechanisms 267 4.5. Renegotiation 269 4.6. The planner as a player 275 5. Bayesian implementation 276 5.1. Definitions 276 5.2. Closure 277 5.3. Incentive compatibility 278 5.4. Bayesian monotonicity 279 * We are grateful to Sandeep Baliga, Luis Corch6n, Matt Jackson, Byungchae Rhee, Ariel Rubinstein, Ilya Segal, Hannu Vartiainen, Masahiro Watabe, and two referees, for helpful comments. Handbook of Social Choice and Welfare, Volume 1, Edited by K.J Arrow, A.K. Sen and K. Suzumura ( 2002 Elsevier Science B. V All rights reserved 238 E. Maskin and T: Sj'str6m 5.5. Non-parametric, robust and fault tolerant implementation 281 6. Concluding remarks 281 References 282 Abstract The implementation problem is the problem of designing a mechanism (game form) such that the equilibrium outcomes satisfy a criterion of social optimality embodied in a social choice rule. -
An Invitation to Market Design
An Invitation to Market Design Scott Duke Kominers Alexander Teytelboym Vincent P. Crawford Working Paper 18-019 An Invitation to Market Design Scott Duke Kominers Harvard Business School Alexander Teytelboym University of Oxford Vincent P. Crawford University of Oxford Working Paper 18-019 Copyright © 2017 by Scott Duke Kominers, Alexander Teytelboym, and Vincent P. Crawford Working papers are in draft form. This working paper is distributed for purposes of comment and discussion only. It may not be reproduced without permission of the copyright holder. Copies of working papers are available from the author. An Invitation to Market Design∗ Scott Duke Kominers† Alexander Teytelboym‡ Vincent P. Crawford§ September 4, 2017 Abstract Market design seeks to translate economic theory and analysis into practical solutions to real-world problems. By redesigning both the rules that guide market transactions and the infrastructure that enables those transactions to take place, market designers can address a broad range of market failures. In this paper, we illustrate the process and power of market design through three examples: the design of medical residency matching programs; a scrip system to allocate food donations to food banks; and the recent “Incentive Auction” that reallocated wireless spectrum from television broadcasters to telecoms. Our lead examples show how effective market design can encourage participation, reduce gaming, and aggregate information, in order to improve liquidity, efficiency, and equity in markets. We also discuss a number of fruitful applications of market design in other areas of economic and public policy. Keywords: matching, auctions, trading, scrip, liquidity, efficiency, equity, allocation rules, marketplaces, market design JEL Classification: D47, C78, D44, D82, D02, D51, D71, D61, D62, D63 ∗We are deeply grateful to Thomas R. -
Simple Versus Optimal Contracts
EC’19 Session 3c: Pricing and Contracts Simple versus Optimal Contracts PAUL DÜTTING, London School of Economics, UK TIM ROUGHGARDEN, Columbia University, USA INBAL TALGAM-COHEN, Technion, Israel We consider the classic principal-agent model of contract theory, in which a principal designs an outcome- dependent compensation scheme to incentivize an agent to take a costly and unobservable action. When all of the model parameters—including the full distribution over principal rewards resulting from each agent action—are known to the designer, an optimal contract can in principle be computed by linear programming. In addition to their demanding informational requirements, however, such optimal contracts are often complex and unintuitive, and do not resemble contracts used in practice. This paper examines contract theory through the theoretical computer science lens, with the goal of developing novel theory to explain and justify the prevalence of relatively simple contracts, such as linear (pure commission) contracts. First, we consider the case where the principal knows only the first moment of each action’s reward distribution, and we prove that linear contracts are guaranteed to be worst-case optimal, ranging over all reward distributions consistent with the given moments. Second, we study linear contracts from a worst-case approximation perspective, and prove several tight parameterized approximation bounds. CCS Concepts: • Theory of computation → Algorithmic game theory and mechanism design; • Ap- plied computing → Law; Economics. Additional Key Words and Phrases: principal-agent model, model uncertainty, max-min robustness ACM Reference Format: Paul Dütting, Tim Roughgarden, and Inbal Talgam-Cohen. 2019. Simple versus Optimal Contracts. In ACM EC ’19: ACM Conference on Economics and Computation (EC ’19), June 24–28, 2019, Phoenix, AZ, USA. -
Optimal Pricing Mechanisms with Unknown Demand
Optimal Pricing Mechanisms with Unknown Demand By Ilya Segal∗ August 15, 2002 Abstract The standard profit-maximizing multi-unit auction intersects the submitted de- mand curve with a preset reservation supply curve, which is determined using the distribution from which the buyers’ valuations are drawn. However, when this dis- tribution is unknown, a preset supply curve cannot maximize monopoly profits. The optimal pricing mechanism in this situation sets a price to each buyer on the basis of the demand distribution inferred statistically from other buyers’ bids. The resulting profit converges to the optimal monopoly profit with known demand as the num- berofbuyersgoestoinfinity, and convergence can be substantially faster than with sequential price experimentation. *Department of Economics, Stanford University, Stanford, CA 94305-6072 (email: [email protected]). I am especially indebted to Paul Milgrom, who raised the question moti- vating this research and offered several invaluable suggestions. I am also grateful to Susan Athey, Guido Imbens, Jonathan Levin, Eric Maskin, Steven Tadelis, Hal Varian, Edward Vytlacil, Michael Whinston, and the participants of seminars at Stanford and of the 2002 Decentralization Conference and 2002 Stanford Institute for Theoretical Economics Summer Workshop for their invaluable comments. Vinicius Carrasco provided excellent research assistance. Recent advances in information technology, most notably the Internet, enabled the use of economic allocation mechanisms that had been impractical before. Many goods tradi- tionally sold at posted prices are now sold in auction-like mechanisms, in which buyers express their preferences by submitting bids. Some Internet websites, such as eBay.com, use traditional auction mechanisms, such as the English auction. -
Discovering Auctions: Contributions of Paul Milgrom and Robert Wilson∗
Discovering Auctions: Contributions of Paul Milgrom and Robert Wilson∗ Alexander Teytelboym† ○r Shengwu Li ‡ ○r Scott Duke Kominers§ ○r Mohammad Akbarpour¶ ○r Piotr Dworczak‖ March 13, 2021 Abstract The 2020 Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel was awarded to Paul R. Milgrom and Robert B. Wilson for \improvements to auction theory and inventions of new auction formats." In this survey article, we review the contributions of the laureates, emphasizing the subtle interplay between deep theoreti- cal questions and practical design challenges that resulted in one of the most successful fields of economics. ∗We are grateful to Tommy Andersson, Susan Athey, Larry Ausubel, Martin Bichler, Eric Budish, Hung- po Chao, Peter Cramton, Constantinos Daskalakis, Robert Day, Jerry Green, John Hatfield, Bengt Holm- strom, Paul Klemperer, David Kreps, Jonathan Levin, Kevin Leyton-Brown, Preston McAfee, Meg Meyer, Rad Niazadeh, Shmuel Oren, Michael Ostrovsky, David Parkes, Marzena Rostek, Alvin Roth, Tim Rough- garden, Amin Saberi,and Ilya Segal for helpful conversations and comments; and especially to Paul Milgrom and Robert Wilson for taking the time to discuss their work with us and for being such extraordinary teachers and mentors. Additionally, Kominers gratefully acknowledges the support the National Science Foundation (grant SES-1459912) and both the Ng Fund and the Mathematics in Economics Research Fund of the Harvard Center of Mathematical Sciences and Applications. This work was also supported by the Economic and Social Research Council grant number ES/R007470/1. †Department of Economics and St. Catherine's College, University of Oxford. Email: [email protected] ‡Department of Economics, Harvard University.