Identification Of Behavioral Signature: Benford’s Law In Online Auctions

Abstract

For a forensic implication of online auction market earnings, we study networked overlapping online auctions and underlying agent strategy. Bidder type identification provides efficient prior information for price formation process. Especially early-stage recognition of specific bidder types enables faster ex-ante revenue estimation. Characterizing behavioral pattern of bidding strategies, we identify unique digital signatures of heterogeneous bidder types. Given that the bidder types impose direct impact on the revenue, we further extend the conceptual domain to the potential bidding fraud which undermines overall revenue structure. We highlight the method of agent signature identification through Benford’s law and power law. In our findings, there exists a bidder class which confirms the distributional pattern of Benford’s law and their revenue impact is significant. Explicit characterization is conducted based on the power law. Participating agent strategy reflects their agent cost, surplus as well as market earnings.