Reactivity in Online Auctions: Understanding Bidding Behavior through Reactive Transitions

Internet systems are a typical scenario where sequences of interactions arise. Modeling the factors that drive the dynamics of an online auction, for example, is complex, since successive interactions become a loop-feedback mechanism, that we call reactivity, that is, the user behavior affects the auction negotiation and vice-versa. In this paper we briefly describes our methodology for characterizing online auctions, considering reactivity. We present the reactive transitions, that is the approach we adopt to model reactivity in online auctions. The reactive transition models the reactivity function, providing a way to discover the bidding behavior's patterns. We also validate our model using actual bidding data from eBay. The results show rich details to understand bidding behavior, that can be used to design support-decision agents and simulate e-markets.

[1]  K. Reynolds,et al.  Determinants of internet auction success and closing price: An exploratory study , 2003 .

[2]  J. Laffont,et al.  ECONOMETRICS OF FIRST-PRICE AUCTIONS , 1995 .

[3]  Yves Lechevallier,et al.  Pre-Processing and Clustering Complex Data in E-Commerce Domain , 2005 .

[4]  Vasudeva Akula,et al.  Two-level workload characterization of online auctions , 2007, Electron. Commer. Res. Appl..

[5]  J. Kagel,et al.  Winner’s Curse , 2014 .

[6]  Kenneth N. Brown,et al.  The Trading Agent Competition as a test problem for Constraint Solving under Change and Uncertainty , 2006 .

[7]  Ramakrishnan Srikant,et al.  Fast Algorithms for Mining Association Rules in Large Databases , 1994, VLDB.

[8]  Roger B. Myerson,et al.  Optimal Auction Design , 1981, Math. Oper. Res..

[9]  Paul R. Milgrom,et al.  Auctions and Bidding: A Primer , 1989 .

[10]  Shirley Dex,et al.  JR 旅客販売総合システム(マルス)における運用及び管理について , 1991 .

[11]  Hans-Hermann Bock Data mining tasks and methods: Classification: the goal of classification , 2002 .

[12]  H. Simon,et al.  Models of My Life , 1991 .

[13]  D.A. Menasce,et al.  Towards workload characterization of auction sites , 2003, 2003 IEEE International Conference on Communications (Cat. No.03CH37441).

[14]  Adriano M. Pereira,et al.  Characterization of Online Auctions: Correlating Negotiation Patterns and Bidding Behavior , 2007, 2007 Latin American Web Conference (LA-WEB 2007).

[15]  Stefano Fiori Simon's Bounded Rationality. Origins and use in economic theory , 2005 .

[16]  Wagner Meira,et al.  Characterization of Online Auctions: Correlating Negotiation Patterns and Bidding Behavior , 2007 .

[17]  Paul A. Pavlou,et al.  Evidence of the Effect of Trust Building Technology in Electronic Markets: Price Premiums and Buyer Behavior , 2002, MIS Q..

[18]  Alok Gupta,et al.  User heterogeneity and its impact on electronic auction market design: an empirical exploration , 2004 .

[19]  Robert F. Easley,et al.  Jump Bidding Strategies in Internet Auctions , 2004, Manag. Sci..

[20]  Alok Gupta,et al.  Replicating Online Yankee Auctions to Analyze Auctioneers' and Bidders' Strategies , 2003, Inf. Syst. Res..

[21]  Ali Hortaçsu,et al.  Winner's Curse, Reserve Prices and Endogenous Entry: Empirical Insights from Ebay Auctions , 2003 .

[22]  Tomasz Imielinski,et al.  Mining association rules between sets of items in large databases , 1993, SIGMOD Conference.

[23]  J. Kagel,et al.  Auctions: A Survey of Experimental Research, 1995 - 2008* , 2008 .

[24]  Michael R. Baye,et al.  The Economics of the Internet and E-commerce , 2002, Advances in Applied Microeconomics.

[25]  H. Simon,et al.  Models of Bounded Rationality: Empirically Grounded Economic Reason , 1997 .

[26]  Daniel A. Menascé,et al.  Scaling for E-Business: Technologies, Models, Performance, and Capacity Planning , 2000 .

[27]  A. Roth,et al.  Last-Minute Bidding and the Rules for Ending Second-Price Auctions: Evidence from eBay and Amazon Auctions on the Internet , 2002 .