scholarly journals Detecting Collusive Shill Bidding

2007 ◽  
Author(s):  
Jarrod Trevathan ◽  
Wayne Read
Keyword(s):  
Shill Bidding

Shill Bidding and Trust

2018 ◽  
Author(s):  
Bryan C. McCannon ◽  
Eduardo Minuci
Keyword(s):  
Shill Bidding

scholarly journals Adverse Selection and Auction Design for Internet Display Advertising

2016 ◽  
Vol 106 (10) ◽  
pp. 2852-2866 ◽  
Author(s):  
Nick Arnosti ◽  
Marissa Beck ◽  
Paul Milgrom
Keyword(s):  
Adverse Selection ◽  
Auction Design ◽  
Display Advertising ◽  
Shill Bidding ◽  
Heavy Tailed Distributions ◽  
Price Auction ◽  
Second Price Auction ◽  
Heavy Tailed ◽  
The Cost ◽  
Second Price Auctions

We model an online display advertising environment in which “performance” advertisers can measure the value of individual impressions, whereas “brand” advertisers cannot. If advertiser values for ad opportunities are positively correlated, second-price auctions for impressions can be inefficient and expose brand advertisers to adverse selection. Bayesian-optimal auctions have other drawbacks: they are complex, introduce incentives for false-name bidding, and do not resolve adverse selection. We introduce “modified second bid” auctions as the unique auctions that overcome these disadvantages. When advertiser match values are drawn independently from heavy-tailed distributions, a modified second bid auction captures at least 94.8 percent of the first-best expected value. In that setting and similar ones, the benefits of switching from an ordinary second-price auction to the modified second bid auction may be large, and the cost of defending against shill bidding and adverse selection may be low. (JEL D44, D82, L86, M37)

Detecting Shill Bidding in Online English Auctions

2009 ◽  
pp. 446-470 ◽  
Author(s):  
Jarrod Trevathan
Keyword(s):  
Feasible Solution ◽  
Online Auctions ◽  
Search Space ◽  
English Auctions ◽  
Detection Mechanism ◽  
Major Threat ◽  
Shill Bidding ◽  
Legal Implications

Shill bidding is where spurious bids are introduced into an auction to drive up the final price for the seller, thereby defrauding legitimate bidders. While shilling is recognized as a problem, presently there is little or no established means of defense against shills. This chapter presents an algorithm to detect the presence of shill bidding in online auctions. It observes bidding patterns over a series of auctions, providing each bidder a score indicating the likelihood of his/her potential involvement in shill behavior. The algorithm has been tested on data obtained from a series of realistic simulated auctions, and commercial online auctions. The algorithm is able to prune the search space required to detect which bidders are likely to be shills. This has significant practical and legal implications for commercial online auctions where shilling is considered a major threat. This chapter presents a framework for a feasible solution, which acts as a detection mechanism and a deterrent.

scholarly journals STATISTICAL PROPERTIES OF ONLINE AUCTIONS

2006 ◽  
Vol 17 (10) ◽  
pp. 1485-1493 ◽  
Author(s):  
ALIREZA NAMAZI ◽  
ANDREAS SCHADSCHNEIDER
Keyword(s):  
Power Law ◽  
Dynamic Properties ◽  
Online Auctions ◽  
Statistical Properties ◽  
Simple Method ◽  
Bidding Strategies ◽  
Shill Bidding ◽  
Observed Behavior

We characterize the statistical properties of a large number of online auctions run on eBay. Both stationary and dynamic properties, like distributions of prices, number of bids etc., as well as relations between these quantities are studied. The analysis of the data reveals surprisingly simple distributions and relations, typically of power-law form. Based on these findings we introduce a simple method to identify suspicious auctions that could be influenced by a form of fraud known as shill bidding. Furthermore the influence of bidding strategies is discussed. The results indicate that the observed behavior is related to a mixture of agents using a variety of strategies.

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