A Simple and Approximately Optimal Mechanism for an Additive Buyer

We consider a monopolist seller with n heterogeneous items, facing a single buyer. The buyer has a value for each item drawn independently according to (non-identical) distributions, and her value for a set of items is additive. The seller aims to maximize his revenue. We suggest using the a priori better of two simple pricing methods: selling the items separately, each at its optimal price, and bundling together, in which the entire set of items is sold as one bundle at its optimal price. We show that for any distribution, this mechanism achieves a constant-factor approximation to the optimal revenue. Beyond its simplicity, this is the first computationally tractable mechanism to obtain a constant-factor approximation for this multi-parameter problem. We additionally discuss extensions to multiple buyers and to valuations that are correlated across items.

[1]  Roger B. Myerson,et al.  Optimal Auction Design , 1981, Math. Oper. Res..

[2]  Jeremy I. Bulow,et al.  Auctions versus Negotiations , 1996 .

[3]  Daniel R. Vincent,et al.  Multidimensional Mechanism Design: Revenue Maximization and the Multiple-Good Monopoly , 2004 .

[4]  John Thanassoulis,et al.  Haggling over substitutes , 2004, J. Econ. Theory.

[5]  Lawrence M. Ausubel,et al.  The Lovely but Lonely Vickrey Auction , 2004 .

[6]  Shuchi Chawla,et al.  Algorithmic pricing via virtual valuations , 2007, EC '07.

[7]  Alejandro M. Manelli,et al.  Multidimensional Mechanism Design: Revenue Maximization and the Multiple-Good Monopoly , 2004, J. Econ. Theory.

[8]  Tim Roughgarden,et al.  Simple versus optimal mechanisms , 2009, SECO.

[9]  Jason D. Hartline Approximation and Mechanism Design , 2010 .

[10]  Shuchi Chawla,et al.  The power of randomness in bayesian optimal mechanism design , 2010, EC '10.

[11]  Shuchi Chawla,et al.  Multi-parameter mechanism design and sequential posted pricing , 2010, BQGT.

[12]  Yang Cai,et al.  Extreme-Value Theorems for Optimal Multidimensional Pricing , 2011, 2011 IEEE 52nd Annual Symposium on Foundations of Computer Science.

[13]  S. Matthew Weinberg,et al.  Matroid prophet inequalities , 2012, STOC '12.

[14]  Noam Nisan,et al.  The menu-size complexity of auctions , 2013, EC '13.

[15]  Andrew Chi-Chih Yao,et al.  On revenue maximization for selling multiple independently distributed items , 2013, Proceedings of the National Academy of Sciences.

[16]  Yang Cai,et al.  Simple and Nearly Optimal Multi-Item Auctions , 2012, SODA.

[17]  Richard Cole,et al.  The sample complexity of revenue maximization , 2014, STOC.

[18]  Xi Chen,et al.  The Complexity of Optimal Multidimensional Pricing , 2013, SODA.

[19]  Christos Tzamos,et al.  The Complexity of Optimal Mechanism Design , 2012, SODA.

[20]  Shuchi Chawla,et al.  The power of randomness in Bayesian optimal mechanism design , 2015, Games Econ. Behav..

[21]  Tim Roughgarden,et al.  Revenue maximization with a single sample , 2015, Games Econ. Behav..

[22]  Andrew Chi-Chih Yao,et al.  An n-to-1 Bidder Reduction for Multi-item Auctions and its Applications , 2014, SODA.

[23]  Mohammad Taghi Hajiaghayi,et al.  Revenue Maximization for Selling Multiple Correlated Items , 2014, ESA.

[24]  S. Matthew Weinberg,et al.  Pricing lotteries , 2015, J. Econ. Theory.

[25]  Michal Feldman,et al.  Combinatorial Auctions via Posted Prices , 2014, SODA.

[26]  Xi Chen,et al.  On the Complexity of Optimal Lottery Pricing and Randomized Mechanisms , 2015, 2015 IEEE 56th Annual Symposium on Foundations of Computer Science.

[27]  S. Matthew Weinberg,et al.  Simple Mechanisms for a Subadditive Buyer and Applications to Revenue Monotonicity , 2018, ACM Trans. Economics and Comput..

[28]  S. Hart,et al.  Maximal revenue with multiple goods: Nonmonotonicity and other observations , 2015 .

[29]  Aviad Rubinstein,et al.  Settling the Complexity of Computing Approximate Two-Player Nash Equilibria , 2016, 2016 IEEE 57th Annual Symposium on Foundations of Computer Science (FOCS).

[30]  Nikhil R. Devanur,et al.  A duality-based unified approach to Bayesian mechanism design , 2016, SECO.

[31]  Anna R. Karlin,et al.  A Prior-Independent Revenue-Maximizing Auction for Multiple Additive Bidders , 2016, WINE.

[32]  Shuchi Chawla,et al.  Mechanism Design for Subadditive Agents via an Ex Ante Relaxation , 2016, EC.

[33]  Tim Roughgarden,et al.  Learning Simple Auctions , 2016, COLT.

[34]  Maria-Florina Balcan,et al.  Sample Complexity of Automated Mechanism Design , 2016, NIPS.

[35]  Nikhil R. Devanur,et al.  The sample complexity of auctions with side information , 2015, STOC.

[36]  Christos Tzamos,et al.  Does Information Revelation Improve Revenue? , 2016, EC.

[37]  Moshe Babaioff,et al.  The menu-size complexity of revenue approximation , 2016, STOC.

[38]  Yang Cai,et al.  Approximating Gains from Trade in Two-sided Markets via Simple Mechanisms , 2017, EC.

[39]  S. Matthew Weinberg,et al.  The Competition Complexity of Auctions: A Bulow-Klemperer Result for Multi-Dimensional Bidders , 2016, EC.

[40]  Bo Li,et al.  From Bayesian to Crowdsourced Bayesian Auctions , 2017, ArXiv.

[41]  Vasilis Syrgkanis A Sample Complexity Measure with Applications to Learning Optimal Auctions , 2017, NIPS.

[42]  Yang Cai,et al.  Learning Multi-Item Auctions with (or without) Samples , 2017, 2017 IEEE 58th Annual Symposium on Foundations of Computer Science (FOCS).

[43]  C. Daskalakis,et al.  Strong Duality for a Multiple‐Good Monopolist , 2017 .

[44]  Rann Smorodinsky,et al.  On Variants of Network Flow Stability , 2017, WINE.

[45]  Yang Cai,et al.  Simple mechanisms for subadditive buyers via duality , 2016, STOC.

[46]  Gabriel D. Carroll Robustness and Separation in Multidimensional Screening , 2017 .

[47]  S. Matthew Weinberg,et al.  A Simple and Approximately Optimal Mechanism for a Buyer with Complements: Abstract , 2016, EC.

[48]  Approximate revenue maximization with multiple items , 2012, J. Econ. Theory.

[49]  Maria-Florina Balcan,et al.  A General Theory of Sample Complexity for Multi-Item Profit Maximization , 2017, EC.

[50]  Xi Chen,et al.  On the Complexity of Simple and Optimal Deterministic Mechanisms for an Additive Buyer , 2018, SODA.

[51]  Yang Cai,et al.  The Best of Both Worlds: Asymptotically Efficient Mechanisms with a Guarantee on the Expected Gains-From-Trade , 2018, EC.

[52]  Hu Fu,et al.  The Value of Information Concealment , 2018, SODA.

[53]  Nick Gravin,et al.  Separation in Correlation-Robust Monopolist Problem with Budget , 2018, SODA.

[54]  Yu Cheng,et al.  A Simple Mechanism for a Budget-Constrained Buyer , 2018, WINE.

[55]  Michal Feldman,et al.  99% Revenue via Enhanced Competition , 2018, EC.

[56]  Siqi Liu,et al.  On the Competition Complexity of Dynamic Mechanism Design , 2018, SODA.

[57]  Yannai A. Gonczarowski Bounding the menu-size of approximately optimal auctions via optimal-transport duality , 2017, STOC.

[58]  S. Matthew Weinberg,et al.  Optimal (and benchmark-optimal) competition complexity for additive buyers over independent items , 2019, STOC.

[59]  S. Matthew Weinberg,et al.  Prior independent mechanisms via prophet inequalities with limited information , 2019, Games Econ. Behav..

[60]  S. Weinberg,et al.  Approximation Schemes for a Buyer with Independent Items via Symmetries , 2019, ArXiv.

[61]  Adam Wierman,et al.  Third-Party Data Providers Ruin Simple Mechanisms , 2020, Proc. ACM Meas. Anal. Comput. Syst..

[62]  David Simchi-Levi,et al.  Reaping the Benefits of Bundling under High Production Costs , 2015, AISTATS.