DOCSLIB.ORG

A Simple Example to Illustrate the Linkage Principle 1 Introduction 2

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
A Simple Example to Illustrate the Linkage Principle 1 Introduction 2 A Simple Example to Illustrate the Linkage Principle Daniel Quint∗ University of Wisconsin April 2016 Abstract. I present a numerical example illustrating the revenue-superiority of an \open" over a \closed" auction format (the linkage principle), and use it to calculate the magnitude of the effect. 1 Introduction One of the classic results of Milgrom and Weber's (1982) seminal paper is that in a symmetric, affiliated setting, an ascending auction will yield higher expected revenue if bidders are aware at each point during the auction of how many of their competitors are still bidding. This result { one of a set of related insights often collectively labeled the \linkage principle" { is well known: Loertscher, Marx and Wilkening (2015), for example, quote FCC economist Evan Kwerel (in the introduction to Milgrom (2004)) as citing it as a key consideration in the design of early U.S. spectrum auctions. However, the mechanism by which it operates is a bit subtle, and I can't recall ever seeing an explicit example illustrating it, or calculating its magnitude. Here, I offer one. 2 Model 2.1 Valuations Let N be the number of bidders. Suppose there's an underlying state θ 2 f0; 1g, equally likely, with common values vi = θ. Each bidder gets a signal si which is i:i:d: conditional on θ, with density ( αsα−1 if θ = 1 f(sjθ) = α(1 − s)α−1 if θ = 0 and therefore CDF ( sα if θ = 1 F (sjθ) = 1 − (1 − s)α if θ = 0 ∗Department of Economics, University of Wisconsin. 1180 Observatory Drive, Madison, WI 53706, USA. Tel.: (608) 263-2515. E-mail: [email protected]. Parts of this work were done while I was a visitor at eBay Research Labs in San Jose, CA, and at the European University Institute in Florence, Italy. I'm thankful to ERL and EUI for the support, and to Dominic Coey, Brad Larsen, and Kane Sweeney for inspiring this note and for helpful comments. 1 conditional on the true state, with α > 1. Note that an increase in α makes signals \more infor- mative" about the realization of θ. While it's not immediately apparent, since f(sjθ) satisfies the 1 (1) (2) (3) (N) monotone likelihood ratio property, fθ; s1; : : : ; sN g are affiliated. Let s > s > s > ··· > s denote the order statistics of the bidders' signals. 2.2 Full-information versus no-information button auctions As in Milgrom and Weber (1982), I model ascending auctions as button auctions, with either full information (every bidder knows who else is active and at what price the inactive bidders dropped out) or no information (bidders receive no feedback other than that the auction is still in progress until it is over). Milgrom and Weber refer to the former as an English auction, and the latter as a second-price auction. Symmetric, monotonic equilibria for both are established in Theorems 6 and 10 of their paper, and the revenue ranking result is Theorem 11. 2.3 Bidding in the no-information case When there is no feedback during the auction, each bidder bids the expected value the asset would have if he was exactly tied for the highest signal, or b(s) = Pr(θ = 1js(1) = s(2) = s) By Bayes' Law, this is 1 Pr(s(1) = s(2) = sjθ = 1) b(s) = 2 1 (1) (2) 1 (1) (2) 2 Pr(s = s = sjθ = 1) + 2 Pr(s = s = sjθ = 0) 1 N(N − 1)αsα−1 · αsα−1 · (sα)N−2 = 2 1 α−1 α−1 α N−2 1 α−1 α−1 α N−2 2 N(N − 1)αs · αs · (s ) + 2 N(N − 1)α(1 − s) · α(1 − s) · (1 − (1 − s) ) , ! (1 − s)α−1 (1 − s)α−1 1 − (1 − s)α N−2 = 1 1 + · · sα−1 sα−1 sα 2.4 Bidding in the full-information case In the full-information case, each losing bidder's signal is revealed by the price at which he drops out. Revenue depends on the bid function when only two active bidders remain, which depends on the revealed signals of the N − 2 bidders who have dropped out to that point. I write this bid (3) function as b(s; t), where t = (t3; t4; : : : ; tN ) are the revealed realizations of the order statistics s , 1 Thus, if we think of bidder values as being vi = E(θjfs1; : : : ; sng) instead of vi = θ, this example fits within the general affiliated/interdependent model of Milgrom and Weber (1982). 2 s(4), etc. This bid function is b(s; t) = Pr(θ = 1js(1) = s(2) = s; s(3−N) = t) , α−1 α−1 N α−1 ! (1 − s) (1 − s) Y (1 − ti) = 1 1 + · · sα−1 sα−1 tα−1 i=3 i 3 Results (2) 1 3.1 Example: expected revenue when N = 3, α = 2, and s = 2 1 For an example we can solve analytically, let N = 3, α = 2, and consider the realization 2 of the (2) 1 1 (2) 1 second-highest signal. Note that E(θjs = 2 ) = 2 , as s = 2 is equally likely under either state. (2) 1 In the no-information case, when s = 2 , the second-highest bidder drops out at a bid of , 1 ! 1 1 1 1 − 4 b( 2 ) = 1 1 + · · 1 = 0:25 1 1 4 so revenue is 0:25. In the full-information case, given the realized value of the lowest signal, the 1 bidder with type 2 bids up to , ! 1 1 1 − s(3) b( 1 ; s(3)) = 1 1 + · · = s(3) 2 1 1 s(3) (2) 1 (3) (2) 1 so expected revenue when s = 2 is E(s js = 2 ). Now, conditional on the realizations of θ (2) (3) and s , the distribution of s is just the marginal (prior) distribution of a signal si, conditional on θ, truncated above at s(2); so we can calculate expected revenue as 1 (3) (2) 1 (3) (2) 1 E b 2 ; s js = 2 = E s js = 2 (2) 1 (3) (2) 1 = Pr(θ = 1js = 2 ) · E(s js = 2 ; θ = 1) (2) 1 (3) (2) 1 + Pr(θ = 0js = 2 ) · E(s js = 2 ; θ = 0) 1 1 R 2 R 2 1 0 (2s)sds 1 0 2(1 − s)sds = · 1 + · 1 2 R 2 2 R 2 0 2sds 0 2(1 − s)ds 1 1 1 2 5 = · + · = ≈ 0:2778 2 3 2 9 18 So for this particular realization of s(2), expected revenue is 11% higher when the bidders learn the realization of s(3) from equilibrium bidding than when they do not. 3 3.2 What's going on To avoid ex post regret, the second-to-last bidder must give up before he would regret winning the item if his opponent dropped out. This therefore stops him from bidding beyond the expected (1) (2) 1 value of θ conditional on s = s = 2 , and whatever other information he has. If the signal of the lowest signal was observed, then its realization is also used to update the expected value of θ. If not, then it is not; by iterated expectations, this is the same, in expectation, as using the (3) (1) (2) 1 distribution of s that would be consistent with s = s = 2 . But since this is pessimistic (1) (2) 1 relative to the truth s > s = 2 , and since the signals are affiliated, this depresses the expected value of θ and therefore the second-highest bidder's bid. (2) 1 In numbers, as noted above, expected revenue with full information when s = 2 is (3) (2) 1 (2) 1 (3) (2) 1 E(s js = 2 ) = Pr(θ = 1js = 2 )E(s jθ = 1; s = 2 ) (2) 1 (3) (2) 1 + Pr(θ = 0js = 2 )E(s jθ = 0; s = 2 ) 1 1 1 2 5 = · + · = ; 2 3 2 9 18 while with no information, it's (1) (2) 1 (1) (2) 1 (3) E(θjs = s = ) = E (3) (1) (2) 1 E(θjs = s = ; s ) 2 s js =s = 2 2 (3) (1) (2) 1 = E(s js = s = 2 ) (1) (2) 1 (3) (1) (2) 1 = Pr(θ = 1js = s = 2 )E(s jθ = 1; s = s = 2 ) (1) (2) 1 (3) (1) (2) 1 + Pr(θ = 0js = s = 2 )E(s jθ = 0; s = s = 2 ) 1 1 3 2 1 = · + · = : 4 3 4 9 4 3.3 Ex ante expected revenue While that explains the mechanism through which this effect operates, in terms of magnitude, what we want is not revenue for a particular realization of signals, but expected revenue ex ante. For various values of α and N, I've calculated ex ante expected revenue for the two formats via numerical simulation, simulating 500,000 sets of signals and the resulting auction outcomes. Table 1 gives expected revenue (multiplied by 1; 000 for easier reading); keep in mind the expected value 1 of the asset is exactly 2 , so 500 minus revenue gives bidder surplus. Note that revenue goes to 500, regardless of N, as α approaches either 1 or 1. As α ! 1, the distributions F (sijθ = 1) and F (sijθ = 0) approach each other, so the signals become uninformative; 1 bidders then share the common belief that E(θ) = 2 and earn no information rents. When α gets large, signals are very informative, but therefore also highly correlated, so information rents once again go to 0.
Load more

Recommended publications
  • Robust Market Design: Information and Computation
    ROBUST MARKET DESIGN: INFORMATION AND COMPUTATION A DISSERTATION SUBMITTED TO THE DEPARTMENT OF COMPUTER SCIENCE AND THE COMMITTEE ON GRADUATE STUDIES OF STANFORD UNIVERSITY IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY Inbal Talgam-Cohen December 2015 Abstract A fundamental problem in economics is how to allocate precious and scarce resources, such as radio spectrum or the attention of online consumers, to the benefit of society. The vibrant research area of market design, recognized by the 2012 Nobel Prize in economics, aims to develop an engineering science of allocation mechanisms based on sound theoretical foundations. Two central assumptions are at the heart of much of the classic theory on resource allocation: the common knowledge and substitutability assumptions. Relaxing these is a prerequisite for many real-life applications, but involves significant informational and computational challenges. The starting point of this dissertation is that the computational paradigm offers an ideal toolbox for overcoming these challenges in order to achieve a robust and applicable theory of market design. We use tools and techniques from combinatorial optimization, randomized algo- rithms and computational complexity to make contributions on both the informa- tional and computational fronts: 1. We design simple mechanisms for maximizing seller revenue that do not rely on common knowledge of buyers' willingness to pay. First we show that across many different markets { including notoriously challenging ones in which the goods are heterogeneous { the optimal revenue benchmark can be surpassed or approximated by adding buyers or limiting supplies, and then applying the standard Vickrey (second-price) mechanism. We also show how, by removing the common knowledge assumption, the classic theory of revenue maximiza- tion expands to encompass the realistic but complex case in which buyers are interdependent in their willingness to pay.
    [Show full text]
  • Auction Theory
    Auction Theory Jonathan Levin October 2004 Our next topic is auctions. Our objective will be to cover a few of the main ideas and highlights. Auction theory can be approached from different angles – from the perspective of game theory (auctions are bayesian games of incomplete information), contract or mechanism design theory (auctions are allocation mechanisms), market microstructure (auctions are models of price formation), as well as in the context of different applications (procure- ment, patent licensing, public finance, etc.). We’re going to take a relatively game-theoretic approach, but some of this richness should be evident. 1 The Independent Private Value (IPV) Model 1.1 A Model The basic auction environment consists of: Bidders i =1,...,n • Oneobjecttobesold • Bidder i observes a “signal” Si F ( ), with typical realization si • [s, s], and assume F is continuous.∼ · ∈ Bidders’ signals S1,...,Sn are independent. • Bidder i’s value vi(si)=si. • Given this basic set-up, specifying a set of auction rules will give rise to a game between the bidders. Before going on, observe two features of the model that turn out to be important. First, bidder i’s information (her signal) is independent of bidder j’s information. Second, bidder i’s value is independent of bidder j’s information – so bidder j’s information is private in the sense that it doesn’t affect anyone else’s valuation. 1 1.2 Vickrey (Second-Price) Auction In a Vickrey, or second price, auction, bidders are asked to submit sealed bids b1,...,bn. The bidder who submits the highest bid is awarded the object, and pays the amount of the second highest bid.
    [Show full text]
  • Is the `Linkage Principle' Valid?: Evidence from the Field
    Center for Economic Institutions Working Paper Series No. 2010-4 “Is the ’Linkage Principle’ Valid?: Evidence from the Field” Sung-Jin Cho, Harry J. Paarsch and John Rust November 2010 Center for Economic Institutions Working Paper Series Institute of Economic Research Hitotsubashi University 2-1 Naka, Kunitachi, Tokyo, 186-8603 JAPAN http://cei.ier.hit-u.ac.jp/English/index.html Tel:+81-42-580-8405/Fax:+81-42-580-8333 Paper for Discussion: first draft, 19 December 2009; this draft, November 27, 2010. IS THE `LINKAGE PRINCIPLE' VALID?: EVIDENCE FROM THE FIELD BY SUNG-JIN CHO, HARRY J. PAARSCH, AND JOHN RUST Seoul National University, University of Melbourne, and University of Maryland We present field evidence involving experienced bidders that supports the link- age principle—specifically, the prediction that in affiliated-values auction environments the expected revenues generated at open-outcry, ascending-bid (English) auctions are higher than those under other auction formats that reveal less information to partic- ipants. Using field data from a large seller of automobiles which experimented with different selling formats, we find that the seller’s average revenues were significantly higher under an English auction than under a dynamic Internet auction that revealed far less information to bidders. 1. Introduction and Motivation. In an influential and classic paper, Milgrom and Weber [1982] de- rived a powerful result, and coined the term linkage principle to describe it. Simply put, in auction environ- ments having affiliated values, the linkage principle states that a seller can expect to increase revenues by providing more information to bidders, both before and during the auction.
    [Show full text]
  • On Framework and Hybrid Auction Approach to the Spectrum
    On Framework and Hybrid Auction Approach to the Spectrum Licensing Procedure Devansh Dikshit Y. Narahari Electronic Commerce Laboratory, Dept. of Computer Science and Automation, Indian Institute Science, Bangalore, India. Email: {devansh},{hari}@csa.iisc.ernet.in November 13, 2018 Abstract few years, many private parties have entered in the field of telecommunications. And as expected they require a slice Inspired by the recent developments in the field of Spec- of spectrum for that purpose. This also provides a wonder- trum Auctions, we have tried to provide a comprehensive ful opportunityfor the governmentas there is useable spec- framework for the complete procedure of Spectrum trum which is left unused with the government, with only Licensing. We have identified the various issues the a fraction of it being used for military and civil purposes Governments need to decide upon while designing the by the governments. The amount of this spectrum is usu- licensing procedure and what are the various options ally scarce hence the greater demand-supply gap. Along available in each issue. We also provide an in depth study with generating revenues, privatization of spectrum will of how each of this options impact the overall procedure also lead to a faster pace of developmentby the private par- along with theoretical and practical results from the past. ties which may not be always possible to do by the govern- Lastly we argue as to how we can combine the positives ment. This has lead to the sale/licensing of the spectrums two most widely used Spectrum Auctions mechanisms by various countries across the globe, using methods like into the Hybrid Multiple Round Auction mechanism being lotteries, auctions, tenders etc.
    [Show full text]
  • Optimal and Near-Optimal Mechanism Design with Interdependent Values
    Proceedings Article Optimal and Near-Optimal Mechanism Design with Interdependent Values TIM ROUGHGARDEN, Stanford University INBAL TALGAM-COHEN, Stanford University We study optimal and approximately-optimal mechanism design questions in the interdependent values model, which generalizes the standard setting of independent and private values. We focus our attention on ex post incentive compatible and individually rational mechanisms, and develop an analog of Myerson's optimal auction theory that applies to many interdependent settings of interest. We demonstrate two ap- plications for specific interdependent settings: First, a parallel result to the well-known optimality of the second-price auction with reserve for i.i.d. bidders, where the English auction replaces the second-price one. Second, we identify good prior-independent auctions | auctions with near-optimal expected revenue across a wide range of priors | for certain interdependent value settings. Categories and Subject Descriptors: [Theory of Computation]: Algorithmic game theory and mechanism design Additional Key Words and Phrases: interdependence; optimal auctions; prior-independence 1. INTRODUCTION The subject of this paper is optimal and approximately-optimal mechanism design, where bidders have interdependent values, and the mechanism has ex post incentive compatibility and individual rationality guarantees. Economic research on auctions has explored different valuation models over the past decades, which can roughly be divided into private versus interdependent values (for exam- ple, see [Krishna 2010, Chapters 2-5] versus [Krishna 2010, Chapters 6-10]). The more nacent research effort in theoretical computer science has focused largely on the restricted setting of independent private values, recently also venturing into the realm of correlated private values [Papadimitriou and Pierrakos 2011; Dobzinski et al.
    [Show full text]
  • Seller Cheap Talk in Common Value Auctions∗
    Seller cheap talk in common value auctions∗ Archishman Chakraborty† Nandini Gupta‡ Rick Harbaugh§ April 2003 Abstract Sellers benefit on average from revealing information about their goods to buyers, but the incentive to exaggerate undermines the credibility of seller statements. When multiple goods are being auctioned, we show that revealing a complete or partial ordering of the different goods by value can be credible. Ordinal cheap talk of this form is not susceptible to exaggeration because it simultaneously reveals favorable information about some goods and unfavorable information about other goods. Any informative ordering increases revenues in accordance with the linkage principle, and the complete ordering is asymptotically revenue- equivalent to full revelation as the number of goods becomes large. These results provide a new explanation in addition to bundling, complementarities, and versioning for how a seller benefitsfromthesaleofmultiplegoods. JEL Classification: D44, D82, L15. Key Words: linkage principle; winner’s curse; cheap talk; common value auction ∗We thank Esther Gal-Or, Ron Harstad, and Timothy Van Zandt for helpful comments. Earlier versions of this paper benefited from presentations at the SED 2000 Conference and the 2000 World Congress of the Econometric Society. †Corresponding author. Baruch College, CUNY, arch [email protected] ‡William Davidson Institute at the University of Michigan Business School, [email protected] §Claremont McKenna College, [email protected] 1Introduction When can a seller credibly reveal information to buyers? The linkage principle (Milgrom and Weber, 1982) shows that such revelation strengthens competition and, on average, increases seller revenues by narrowing information differences among buyers. But the importance of the linkage principle would seem limited by the seller’s incentive to only reveal good information or even lie about bad information.
    [Show full text]
  • Spectrum Auction Breakdown: HOW INCUMBENTS MANIPULATE FCC AUCTION RULES to BLOCK BROADBAND COMPETITION
    Working Paper #18 June 2007 Spectrum Auction Breakdown: HOW INCUMBENTS MANIPULATE FCC AUCTION RULES TO BLOCK BROADBAND COMPETITION By Dr. Gregory Rose* The most obvious possible distortion is that since firms’ joint profits in the telecom market are generally greater the fewer competitors there are in the market, it is worth more to any group of firms to prevent entry of an additional firm than the additional firm is willing to pay to enter. So too few firms may win spectrum, and these winners may each win too much, exactly as a “hands-off” policy to merger control will tend to create an overly concentrated industry . —Paul Klemperer, Auctions: Theory and Practice , (Princeton, 2004), 112 Summary of Findings and Recommendation Federal Communications Commission (FCC) spectrum auctions can seem arcane and technical, but in fact, auctions for exclusive licenses to use the public airwaves determine the future of American telecommunications. FCC auctions shape the competitive structure of markets and, ultimately, who controls entire industries—from broadcasting, to telephony, to wireless broadband services—that are increasingly central to U.S. productivity growth, consumer welfare, and global competitiveness. These auctions have complex rules, rules which are the subject of study by a branch of economics called game theory. And it is virtually an axiom of game theory that the rules determine who wins and who loses. The two studies summarized in this paper comprehensively examine the FCC’s 2006 Advanced Wireless Services (AWS-1) auction. They focus on two aspects of the way information was used by bidders to engage in collusive, anti-competitive, and demand-reduction behaviors.
    [Show full text]
  • Common Outside Options and the Continuation Value Effect
    Auctions in Markets: Common Outside Options and the Continuation Value Effect∗ Stephan Lauermann† Gábor Virág‡ September 23, 2010 Abstract We study auctions with outside options that are determined through actions taken in the external market. Such endogenous outside options have important consequences for auction design. In contrast to the case of exogenous outside options, auctions with less information revelation may yield higher revenues. Effects that favor non- transparent auctions include a small payoff difference between different states, a great value of information in the continuation problem, and imprecise signals of the bid- ders. The timing of information revelation is important: it is never optimal to reveal information after the auction, while it may be optimal before the auction. ∗We would like to thank Simon Board, Tilman Borgers, Johannes Hörner, Thomas Jeitschko, Kai-Uwe Kuhn, Philipp Kircher, Tymofiy Mylovanov, Daisuke Nakajima, Peter Norman, Marco Ottaviani, Romans Pancs, Lones Smith, and various audiences for their helpful comments. All remaining errors are ours. †University of Michigan, Department of Economics, [email protected] ‡corresponding author, University of Rochester, Department of Economics, [email protected] 1 1Introduction In many situations information learned in an auction may be valuable in subsequent mar- ket interaction. We study such auctions to obtain new insights into auction design. In particular, we consider a situation where the losing bidders have common outside options provided by an external market, and study whether transparent or opaque auctions yield higher revenues. We introduce a stylized model to highlight the main trade-offs that shape the optimal information policy of the auctioneer. There are two risk neutral bidders who participate in a second price auction with a post-auction market.
    [Show full text]
  • INFORMATION in MECHANISM DESIGN by Dirk Bergemann And
    INFORMATION IN MECHANISM DESIGN By Dirk Bergemann and Juuso Välimäki August 2005 COWLES FOUNDATION DISCUSSION PAPER NO. 1532 COWLES FOUNDATION FOR RESEARCH IN ECONOMICS YALE UNIVERSITY Box 208281 New Haven, Connecticut 06520-8281 http://cowles.econ.yale.edu/ Information in Mechanism Design Dirk Bergemann Juuso V•alim•akiy First Version: 06/15/2005 This Version: 08/15/2005 Preliminary Comments Welcome Abstract We survey the recent literature on the role of information for mechanism design. We specif- ically consider the role of endogeneity of and robustness to private information in mechanism design. We view information acquisition of and robustness to private information as two distinct but related aspects of information management important in many design settings. We review the existing literature and point out directions for additional future work Keywords: Mechanism Design, Information Acquisition, Ex Post Equilibrium, Robust Mechanism Design, Interdependent Values, Information Management. JEL Classification: C79, D82 Department of Economics, Yale University, 28 Hillhouse Avenue, New Haven, CT 06511, [email protected]. yDepartment of Economics, Helsinki School of Economics and University of Southampton, 00100 Helsinki, Finland, valimaki@hkkk. 1 Information in Mechanism Design August 19, 2005 2 Contents 1 Introduction 3 2 Setup 5 2.1 Payo Environment . 5 2.2 Information Acquisition . 6 3 Motivating Examples 7 3.1 Information Acquisition in Generalized VCG auctions . 7 3.2 Espionage in First-Price Auctions . 9 4 Information Management 10 4.1 Information Acquisition in Committees . 10 4.2 Information Acquisition in Auctions . 14 4.3 Dynamic Auctions . 17 4.4 Information Disclosure in Auctions . 18 4.5 Information in Principal-Agent Models .
    [Show full text]
  • Gregory Rose
    TACIT COLLUSION IN THE AWS-1 AUCTION: THE SIGNALING 1 PROBLEM Gregory Rose Economic Research Services April 20, 2007 1 The author is particularly grateful to Dr. Jesses A. Schwartz who graciously shared algorithms developed for his analysis of the PCS D, E, and F auctions. ABSTRACT This study utilizes the methodology developed by Peter Cramton and Jesse A. Schwartz in their 2002 paper on tacit collusion in the PCS D, E, and Block auction to identify signaling behaviors by bidders in the AWS-1 auction and measure their effects. The principal signaling behavior identified was retaliatory bidding, which occurred in the AWS-1 auction at a slightly higher level than in the PCS D, E, and F auction. Significant indirect demand reduction effects were observed in the AWS-1 auction which call into question whether the auction was revenue maximizing. The study concludes that signaling remains a serious problem for FCC spectrum auctions and recommends that anonymous bidding rules be adopted for the 700 MHz and all future FCC spectrum auctions. TABLE OF CONTENTS Prolegoumenon: A Tale of Anonymous Bidding………………………………………1 I. Signaling Behaviors Are a Threat to Revenue Maximization in FCC Auctions….4 A. Theoretical Evidence…………………………………………………………………4 B. The Cramton-Schwartz Empirical Studies of the PCS D, E, and F Block Auction……………………………………………………………………………………5 II. Methodology………………………………………………………………………….6 III. Retaliatory Bidding Occurred in the AWS-1 Auction……………………………8 IV. Demand Reduction Effects From Retaliatory Bidding Were Observed in the AWS-1
    [Show full text]
  • Expertise, Attribution, and Ad Blocking in the World of Online Marketing
    Expertise, Attribution, and Ad Blocking in the World of Online Marketing by Stylianos Despotakis A dissertation submitted to the Tepper School of Business in partial fulfillment of the requirements for the degree of Doctor of Philosophy at Carnegie Mellon University May 2018 Dissertation Committee: R. Ravi (Chair) Kannan Srinivasan Vibhanshu Abhishek Isa Hafalir Kaifu Zhang Abstract In this dissertation, we model and provide insights to some of the main challenges the world of online marketing currently faces. In the first chapter, we study the role of information asymmetry introduced by the presence of experts in online marketplaces and how it affects the strategic decisions of different parties in these markets. In the second chapter, we study the attribution problem in online advertising and examine optimal ways for advertisers to allocate their marketing budget across channels. In the third chapter, we explore the effects of modern ad blockers on users and online platforms. In the first chapter, we examine the effect of the presence of expert buyers on other buyers, the platform, and the sellers in online markets. We model buyer expertise as the ability to accurately predict the quality, or condition, of an item, modeled as its common value. We show that nonexperts may bid more aggressively, even above their expected valuation, to compensate for their lack of information. As a consequence, we obtain two interesting implications. First, auctions with a “hard close” may generate higher revenue than those with a “soft close”. Second, contrary to the linkage principle, an auction platform may obtain a higher revenue by hiding the item’s common-value information from the buyers.
    [Show full text]
  • Information Effects in Multi-Unit Dutch Auctions Joy A
    Chapman University Chapman University Digital Commons ESI Working Papers Economic Science Institute 2012 Information Effects in Multi-Unit Dutch Auctions Joy A. Buchanan Steven Gjerstad Chapman University, [email protected] David Porter Chapman University, [email protected] Follow this and additional works at: https://digitalcommons.chapman.edu/esi_working_papers Part of the Econometrics Commons, Economic Theory Commons, and the Other Economics Commons Recommended Citation Buchanan, J.A., Gjerstad, S., & Porter, D. (2012). Information effects in multi-unit Dutch auctions. ESI Working Paper 12-08. Retrieved from http://digitalcommons.chapman.edu/esi_working_papers/80 This Article is brought to you for free and open access by the Economic Science Institute at Chapman University Digital Commons. It has been accepted for inclusion in ESI Working Papers by an authorized administrator of Chapman University Digital Commons. For more information, please contact [email protected]. Information Effects in Multi-Unit Dutch Auctions Comments Working Paper 12-08 This working paper was later published as: Buchanan, J., Gjerstad, S., & Porter, D. (2016). Information effects in uniform price multi-unit Dutch auctions. Southern Economic Journal, 83(1), 126-145. doi: 10.1002/soej.12145 This article is available at Chapman University Digital Commons: https://digitalcommons.chapman.edu/esi_working_papers/80 Information Effects in Multi-Unit Dutch Auctions‡ Joy A. Buchanan, Steven Gjerstad† and David Porter† Revised January 2015 Abstract: We design a multi-unit descending-price (Dutch) auction mechanism that has applications for resource allocation and pricing problems. We address specific auction design choices by theoretically and experimentally determining optimal information disclosure along two dimensions. Bidders are either informed of the number of bidders in the auction, or know that it is one of two possible sizes; they also either know the number of units remaining for sale or are unaware of how many units have been taken by other bidders.
    [Show full text]