Switching Behavior in Online Auctions: Empirical Observations and Predictive Implications

Proceedings of the 2013 Winter Simulation Conference, R. Pasupathy, S.-H. Kim, A. Tolk, R. Hill, and M. E. Kuhl, eds., 2013

Robert H. Smith School Research Paper

12 Pages Posted: 16 Aug 2013 Last revised: 22 Jun 2014

See all articles by Wei Guo

Wei Guo

University of Maryland - College of Computer, Mathematical and Natural Sciences

William Rand

North Carolina State University

Wolfgang Jank

University of Maryland - Decision and Information Technologies Department

Date Written: April 15, 2013

Abstract

There has been substantial work exploring strategies, both theoretical and empirical, for selling and buying in online auctions. However, much of this work has considered single auctions in isolation, partially because it is hard to examine multiple simultaneous auctions using traditional math modeling approaches. In reality, many auctions occur simultaneously, so there is competition not just among bidders, but also among auctions. In this paper, we use simulation to explore bidders’ switching behavior between auctions for similar products. Using an empirical dataset, we first examine the distribution of switching and associated bidding behavior in real auctions. We use this data to create an agent-based model that reproduces the price process observed in the empirical data. Using this model we then explore the effects of: (1) different switching distributions, (2) the switching rule, i.e., which auction to switch to, and (3) different auction start rates. In the end, we show that in order to maximize the final price and to minimize the price disparity, auction platforms should encourage users to switch to a low-price auction that is ending soon.

Keywords: agent-based modeling, auctions, switching, empirical, simultaneous auctions, bidding, behavior

Suggested Citation

Guo, Wei and Rand, William and Jank, Wolfgang, Switching Behavior in Online Auctions: Empirical Observations and Predictive Implications (April 15, 2013). Proceedings of the 2013 Winter Simulation Conference, R. Pasupathy, S.-H. Kim, A. Tolk, R. Hill, and M. E. Kuhl, eds., 2013; Robert H. Smith School Research Paper. Available at SSRN: https://ssrn.com/abstract=2310903

Wei Guo

University of Maryland - College of Computer, Mathematical and Natural Sciences ( email )

2300 Symons Hall,
University of Maryland
College Park, MD 20742-3255
United States

William Rand (Contact Author)

North Carolina State University ( email )

Raleigh, NC 27695
United States

Wolfgang Jank

University of Maryland - Decision and Information Technologies Department ( email )

Robert H. Smith School of Business
4300 Van Munching Hall
College Park, MD 20742
United States
301-405-1118 (Phone)

HOME PAGE: http://www.smith.umd.edu/faculty/wjank/

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