Manipulating Information Providers Access to Information in Auctions

Information purchasing is a crucial issue that auctioneers have to consider when running auctions, in particular in auction settings where the auctioned item’s value is affected by a common value element. In such settings it is reasonable to assume the existence of a self-interested information provider. The main contribution of the information provider may be the elimination of some uncertainty associated with the common value of the auctioned item. The existence of an information provider does not necessarily impose the use of its services. Moreover, in cases in which the auctioneer decides to purchase information, it is not always beneficial for him to disclose it. In this work, we focus on environment settings where the information that may purchased still involves some uncertainty. The equilibrium analysis is provided with illustrations that highlight some non-intuitive behaviors. In particular, we show that in some cases it is beneficial for the auctioneer to initially limit the level of detail and precision of the information he may purchase. This can be achieved, for example, by limiting the information provider’s access to some of the data required to determine the exact common value. This result is non-intuitive especially in light of the fact that the auctioneer is the one who decides whether or not to use the services of the information provider; hence having the option to purchase better information may seem advantageous.

[1]  S. Board,et al.  Revealing information in auctions: the allocation effect , 2009 .

[2]  E. Maasland,et al.  Auction Theory , 2021, Springer Texts in Business and Economics.

[3]  Margo I. Seltzer,et al.  Virtual worlds: fast and strategyproof auctions for dynamic resource allocation , 2003, EC '03.

[4]  P. Reny,et al.  On the failure of the linkage principle in multi-unit auctions , 1999 .

[5]  Esther David,et al.  Truthful and efficient mechanisms for Website dependent advertising auctions , 2014, Multiagent Grid Syst..

[6]  Theo Offerman,et al.  Competitive Bidding in Auctions with Private and Common Values , 2000 .

[7]  Noam Nisan,et al.  Competitive analysis of incentive compatible on-line auctions , 2004, Theor. Comput. Sci..

[8]  J. Goeree,et al.  Efficiency in auctions with Private and Common Values , 2002 .

[9]  Juan José Ganuza,et al.  Signal Orderings Based on Dispersion and the Supply of Private Information in Auctions , 2010 .

[10]  Nicole Immorlica,et al.  Constrained signaling for welfare and revenue maximization , 2013, SECO.

[11]  Sarit Kraus,et al.  Bidding in sealed-bid and English multi-attribute auctions , 2006, Decis. Support Syst..

[12]  David Sarne,et al.  Sharing experiences to learn user characteristics in dynamic environments with sparse data , 2007, AAMAS '07.

[13]  David Sarne,et al.  On the choice of obtaining and disclosing the common value in auctions , 2014, Artif. Intell..

[14]  D.C. Parkes,et al.  Distributed implementations of Vickrey-Clarke-Groves mechanisms , 2004, Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems, 2004. AAMAS 2004..

[15]  Maja J. Mataric,et al.  Sold!: auction methods for multirobot coordination , 2002, IEEE Trans. Robotics Autom..

[16]  David C. Parkes,et al.  An options-based solution to the sequential auction problem , 2009, Artif. Intell..

[17]  M. Dufwenberg,et al.  Information disclosure in auctions: an experiment , 2002 .

[18]  Sarit Kraus,et al.  Auction Equilibrium Strategies for Task Allocation in Uncertain Environments , 2004, CIA.

[19]  David P. Myatt,et al.  On the Simple Economics of Advertising, Marketing, and Product Design , 2005 .

[20]  Sarit Kraus,et al.  Solving the Auction-Based Task Allocation Problem in an Open Environment , 2005, AAAI.

[21]  Paul R. Milgrom,et al.  A theory of auctions and competitive bidding , 1982 .

[22]  Peter Bro Miltersen,et al.  Send mixed signals: earn more, work less , 2012, EC '12.

[23]  Paul Klemperer,et al.  Auctions: Theory and Practice , 2004 .

[24]  Mohammad Taghi Hajiaghayi,et al.  Online auctions with re-usable goods , 2005, EC '05.

[25]  Moshe Tennenholtz,et al.  Tractable combinatorial auctions and b-matching , 2002, Artif. Intell..

[26]  Makoto Yokoo,et al.  Robust Combinatorial Auction Protocol against False-Name Bids , 2000, AAAI/IAAI.

[27]  Nicholas R. Jennings,et al.  Bidding strategies for realistic multi-unit sealed-bid auctions , 2008, Autonomous Agents and Multi-Agent Systems.

[28]  David C. Parkes,et al.  Chain: A Dynamic Double Auction Framework for Matching Patient Agents , 2007, J. Artif. Intell. Res..

[29]  P. Klemperer Auction Theory: A Guide to the Literature , 1999 .

[30]  George A. Akerlof The Market for “Lemons”: Quality Uncertainty and the Market Mechanism , 1970 .

[31]  Moshe Tennenholtz,et al.  Signaling Schemes for Revenue Maximization , 2012, TEAC.

[32]  Avshalom Elmalech,et al.  Search More, Disclose Less , 2013, AAAI.

[33]  Esther David,et al.  Strategy Proof Mechanism for Complex Task Allocations in Prior Consent for Subtasks Completion Environment , 2013, 2013 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT).

[34]  Péter Eso,et al.  Optimal Information Disclosure in Auctions and the Handicap Auction , 2007 .

[35]  Noam Nisan,et al.  Algorithms for Selfish Agents , 1999, STACS.

[36]  Evangelos Markakis,et al.  Auction-Based Multi-Robot Routing , 2005, Robotics: Science and Systems.

[37]  Anthony J. Bagnall,et al.  Autonomous Adaptive Agents for Single Seller Sealed Bid Auctions , 2006, Autonomous Agents and Multi-Agent Systems.

[38]  David A. McAllester,et al.  Decision-Theoretic Bidding Based on Learned Density Models in Simultaneous, Interacting Auctions , 2003, J. Artif. Intell. Res..

[39]  J. Ganuza,et al.  Ignorance Promotes Competition: An Auction Model with Endogenous Private Valuations , 2003 .