Snipers, Shills, and Sharks eBay and Human Behavior

Every day on eBay, millions of people buy and sell a vast array of goods, from rare collectibles and antiques to used cars and celebrity memorabilia. The internet auction site is remarkably easy to use, which accounts in part for its huge popularity. But how does eBay really work, and how does it compare to other kinds of auctions? These are questions that led Ken Steiglitz--computer scientist, collector of ancient coins, and a regular eBay user--to examine the site through the revealing lens of auction theory. The result is this book, in which Steiglitz shows us how human behaviors in open markets like eBay can be substantially more complex than those predicted by standard economic theory. In these pages we meet the sniper who outbids you in an auction's closing seconds, the early bidder who treats eBay as if it were an old-fashioned outcry auction, the shill who bids in league with the seller to artificially inflate the price--and other characters as well. Steiglitz guides readers through the fascinating history of auctions, how they functioned in the past and how they work today in online venues like eBay. Drawing on cutting-edge economics as well as his own stories from eBay, he reveals practical auction strategies and introduces readers to the fundamentals of auction theory and the mathematics behind eBay. Complete with exercises and a detailed appendix, this book is a must for sophisticated users of online auctions, and essential reading for students seeking an accessible introduction to the study of auction theory.eBay, auctions, auction theory, human behavior, open markets, strategies, English auctions, Vickrey auctions

Frugality in path auctions

We consider the problem of picking (buying) an inexpensive $s-t$ path in a graph where edges are owned by independent (selfish) agents, and the cost of an edge is known to its owner only. We study the problem of finding frugal mechanisms for this task, i.e. we investigate the payments the buyer must make in order to buy a path. First, we show that any mechanism with (weakly) dominant strategies (or, equivalently, any truthful mechanism) for the agents can force the buyer to make very large payments. Namely, for every such mechanism, the buyer can be forced to pay $c(P) + \frac{1}{2}k(c(Q)-c(P))$, where $c(P)$ is the cost of the shortest path, $c(Q)$ is the cost of the second-shortest path, and $k$ is the number of edges in $P$. This extends the previous work of Archer and Tardos}, who showed a similar lower bound for a subclass of truthful mechanisms called min-function mechanisms. Our lower bounds have no such limitations on the mechanism. Motivated by this lower bound, we study mechanisms for this problem providing Bayes-Nash equilibrium strategies for the agents. In this class, we identify the optimal mechanism with regard to total payment. We then demonstrate a separation in terms of average overpayments between the classical VCG mechanism and the optimal mechanism showing that under various natural distributions of edge costs, the optimal mechanism pays at most logarithmic factor more than the actual cost, whereas VCG pays $\sqrt{k}$ times the actual cost. On the other hand, we also show that the optimal mechanism does incur at least a constant factor overpayment in natural distributions of edge costs. Since our mechanism is optimal, this gives a lower bound on all mechanisms with Bayes-Nash equilibria

Pairwise Competition and the Replicator Equation

Spite in Hamilton's sense is defined as the willingness to harm oneself in order to harm another more. The standard replicator dynamic predicts that evolutionarily stable strategies are payo#-maximizing equilibria of the underlying game, and hence rules out the evolution of spiteful behavior. We propose a modified replicator dynamic, where selection is based on local outcomes, rather than on the population "state", as in standard models. We show that under this new model spite can evolve readily. The new dynamic suggests conditions under which spite in animals might be found

Agent-Based Simulation of Dynamic Online Auctions

is increasing with the popularization of online auctions. Applications include designing auction mechanisms, bidding strategies, and server systems. We describe simulations of a typical online auction, where the duration is fixed, and the second-highest price is continuously posted and determines the winner's payment. We modeled agents of exactly two types, idealizations and simplifications of those observed in practice: early bidders, who can bid any time during the auction period, and snipers, who wait till the last moments to bid. This allows us to study the interactions of the two types of bidders during the course of auctions, and the effects of the two strategies on the probability of winning, the final price, and the formation of price consensus in iterated auctions. Results show that 1) early bidders can win with a lower price on average than snipers, but much less often; 2) the late bidding strategy of snipers is effective; and 3) in iterated auctions, adjustment feedback of motivational parameters can lead to effective price consensus with small fluctuations