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Shillin’ like a Villian: Fraud in Online Auction Markets. Han Yang Lee and Tom Xu. ECON1465, Fall 2011 Brown University. Introduction. - PowerPoint PPT Presentation
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Shillin’ like a Villian:Fraud in Online Auction Markets
HAN YANG LEE AND TOM XU
ECON1465, Fall 2011Brown University
A shill typically refers to someone who purposely gives the impression that he or she is an enthusiastic independent customer of a seller that he or she is secretly working for.
Competitive Behavior (1) A shill tends to bid exclusively in auctions only held by
one particular seller.(2) A shill tends to have a high bid frequency. (3) A shill has few or no winnings for the auctions
participated in.(4) It is advantageous for a shill to bid within a small time
period after a legitimate bid. (5) A shill usually bids the minimum amount required to
outbid a legitimate bidder.(6) A shill's goal is to try and stimulate bidding.
Introduction
Reserve Price ShillingSeller shills in order to avoid auction house fees.
Competitive ShillingSeller shills to drive up the value of the final bid.
Premium BidA bid that is higher than other bids for the same item in different auctions.
Ebay Fee Structure
Types of Shills
Barbaro, S. and Bracht, B. (2006). “Shilling, Squeezing, Sniping: Explaining late bidding in online second-price auctions,” working paper.Engelbrecht-Wiggans, R. (1987). On Optimal Reservation Prices in Auctions. Management Science 33 (6), 763-770.R. J. Kauffman and C. A. Wood. Running up the bid: detecting, predicting, and preventing reserve price shilling in online auctions. In International Conference on Electronic Commerce, Pittsburgh, PA, 2003R.J. Kauffman and C.A. Wood, The effects of shilling on final bid prices in online auctions, Electronic Commerce Research and Applications 4(1) Spring (2005) 21–34.Myerson, R.B. (1981). Optimal Auction Design. Mathematics of Operations Research 6. 58-73.J. Trevathan and W. Read Detecting Shill Bidding in Online English Auctions. Technical Report, James Cook University, May 2006.Wang, Wenli, Zoltan Hidvegi, and Andrew Whinston. Shill-Proof Fee (SPF) Schedule: the Sunscreen against Seller Self-Collusion in Online English Auctions.
Existing Literature
Final Price Exploration 1. Winner's Curse Hypothesis (NE)2. Value Signal Hypothesis (E)3. Rational Bidder Hypothesis (NE)
Seller Behavior Exploration1. Repeatable Shilling Behavior Hypothesis (E)2. Experienced Seller Hypothesis (NE)3. Reputation Hypothesis (NE)
Explored Hypothesis through Empirical Data
Detection Algorithm
Strengths: -Attempts to find a bidding pattern-Finds a "shill score" for each bidder 1. Bidder/Seller relationship 2. Aggressive bids 3. Bidder winning percentage 4. Bid speed 5. Incremental Bid 6. Earliness of Bid Weakness: -Sellers can change strategy, but will be less efficient
Shill-Proof Fee
Strengths: -Alters fee structure-Instead of final fee based on reserve price + final price, it will be based on fixed listing price + (final price - reserve price)-Sellers become indifferent-Does not affect honest sellers/buyers
Weakness:-Theoretical assumption of IPV-SPF commission rate must be calculated per auction
Possible Solutions
Proposed Experiment - Summary
1. Seller assigned item from v = [50, 500], uniform distribution (discrete whole values)
2. Recommend reserve price = .5v (UB) 1. Seller then sets reserve price
1. Auction time is 2 min.2. First 30 seconds only seller can bid
1. Auction then continues2. Final price determines payoff for
seller
1. After each round seller assigned % based off of final price – reserve price fee, normalized to v
2. Minimal reserve price is 13. Higher % per round = higher payout for participants4. Three groups, ten participants per group, [10, 15] rounds5. Two stages as each participant partakes in two groups6. Fee structure similar to that of eBay
BehaviorBehavior
Each auction has 10 bidders, automatedWhen a bidder wants to bid, timing of bid [1, 10] seconds, uniformly distributedEach bidder has a private value assigned each round
N(v, (.1v)2) – rounded to nearest whole value
Auction has minimum incremental bid of .01v
Proposed Experiment - Bidders
Shill Proof Fee
-Adopt simplified SPF recommended in Wang et al. -Since all variables set, commission fee will be fixed (sellers will not know) -To start the experiment, each seller will know the fee changes from the control group
Detection Algorithm
-Simplification of model from Trevathan and Read
-Depending on the difference between recommend reserve and actual, we set a probability of detection
-If caught, sellers are forbidden to participate in next round
Proposed Experiment - Groups
1) Believe that the SPF will be more effective as it is a preventative measure
2) We hypothesize an early spike in shilling but then gradual decrease as seller realize that shilling has no effect
3) Cost of SPF potentially lower than detection algorithm
4) Changing incentives, may be more efficient than punishment
5) Potential for interesting results in Stage 1 vs. Stage 2
Hypothesis of Experiment Results
Concern of lack of incentive for eBay to prevent shillingEvidence that large sellers often shillLight punishment for offendersShilling might generate large revenues
Experimental results can potentially give insightSeller behavior: incentive changes vs. punishment
Further ResearchEmpirical cost dataSmaller auction housesCompetitive bidding preventionBuyer behavior - sniping
Conclusion