Internet auctions with artificial adaptive agents: A study on market design

Authored by John Duffy, M. Utku Uenver

Date Published: 2008-08

DOI: 10.1016/j.jebo.2007.03.007

Sponsors: Turkish Academy of Sciences (TUBA) United States National Science Foundation (NSF)

Platforms: Delphi

Model Documentation: Other Narrative Mathematical description

Model Code URLs: Model code not found

Abstract

We develop a model of internet auctions with the aim of understanding how rules for ending such auctions (a “hard”- or “soft”-close) affect bidding behavior. We model bidding strategies using finite automata and report results from simulations involving populations of artificial bidders who update their strategies using a genetic algorithm. Our model is shown to deliver late or early bidding behavior, depending on whether the auction has a hard- or soft-close rule in accordance with the empirical evidence. We report on other interesting proper-ties of our model and offer some conclusions from a market design point of view. (c) 2008 Elsevier B.V. All rights reserved.

Tags

Agent-based models Genetic algorithm finite automata internet auctions sniping