Estimating Functional Agent-Based Models: An Application to Bid Shading in Online Markets Format

GECCO 2011, Dublin, Ireland, July 12-16, 2011

Robert H. Smith School Research Paper No. RHS 06-134

9 Pages Posted: 21 May 2011

See all articles by Wei Guo

Wei Guo

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

Wolfgang Jank

University of Maryland - Decision and Information Technologies Department

William Rand

North Carolina State University

Date Written: May 19, 2011

Abstract

Bid shading is a common strategy in online auctions to avoid the "winner’s curse". While almost all bidders shade their bids, at least to some degree, it is impossible to infer the degree and volume of shaded bids directly from observed bidding data. In fact, most bidding data only allows us to observe the resulting price process, i.e. whether prices increase fast (due to little shading) or whether they slow down (when all bidders shade their bids). In this work, we propose an agent-based model that simulates bidders with different bidding strategies and their interaction with one another. We calibrate that model (and hence estimate properties about the propensity and degree of shaded bids) by matching the emerging simulated price process with that of the observed auction data using genetic algorithms. From a statistical point of view, this is challenging because we match functional draws from simulated and real price processes. We propose several competing fitness functions and explore how the choice alters the resulting ABM calibration. We apply our model to the context of eBay auctions for digital cameras and show that a balanced fitness function yields the best results.

Keywords: internet auctions, agent-based modeling, calibration, business, simulation, genetic algorithms

Suggested Citation

Guo, Wei and Jank, Wolfgang and Rand, William, Estimating Functional Agent-Based Models: An Application to Bid Shading in Online Markets Format (May 19, 2011). GECCO 2011, Dublin, Ireland, July 12-16, 2011; Robert H. Smith School Research Paper No. RHS 06-134. Available at SSRN: https://ssrn.com/abstract=1846639

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

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/

William Rand (Contact Author)

North Carolina State University ( email )

Raleigh, NC 27695
United States

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