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