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LastMinute Bidding and the Rules for Ending SecondPrice Auctions: Evidence from eBay and Amazon Auctions on the Internet
, 2002
"... Auctions on the Internet provide a new source of data on how bidding is influenced by the detailed rules of the auction. Here we study the secondprice auctions run by eBay and Amazon, in which a bidder submits a reservation price and has this (maximum) price used to bid for him by proxy. That is, a ..."
Abstract

Cited by 281 (21 self)
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Auctions on the Internet provide a new source of data on how bidding is influenced by the detailed rules of the auction. Here we study the secondprice auctions run by eBay and Amazon, in which a bidder submits a reservation price and has this (maximum) price used to bid for him by proxy. That is, a bidder can submit his reservation price (called a proxy bid) early in the auction and have the resulting bid register as the minimum increment above the previous high bid. As subsequent reservation prices are submitted, the bid rises by the minimum increment until the secondhighest submitted reservation price is exceeded. Hence, an early bid with a reservation price higher than any other submitted during the auction will win the auction and pay only the minimum increment
Choosing Samples to Compute HeuristicStrategy Nash
 In Fifth Workshop on AgentMediated Electronic Commerce
, 2003
"... Auctions define games of incomplete information for which it is often too hard to compute the exact BayesianNash equilibrium. Instead, the infinite strategy space is often populated with heuristic strategies, such as myopic bestresponse to prices. Given these heuristic strategies, it can be usefu ..."
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Cited by 33 (0 self)
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Auctions define games of incomplete information for which it is often too hard to compute the exact BayesianNash equilibrium. Instead, the infinite strategy space is often populated with heuristic strategies, such as myopic bestresponse to prices. Given these heuristic strategies, it can be useful to evaluate the strategies and the auction design by computing a Nash equilibrium across the restricted strategy space. First, it is necessary to compute the expected payoff for each heuristic strategy profile. This step involves sampling the auction and averaging over multiple simulations, and its cost can dominate the cost of computing the equilibrium given a payoff matrix. In this paper, we propose two information theoretic approaches to determine the next sample through an interleaving of equilibrium calculations and payoff refinement. Initial experiments demonstrate that both methods reduce error in the computed Nash equilibrium as samples are performed at faster rates than naive uniform sampling. The second, faster method, has a lower metadeliberation cost and better scaling properties. We discuss how our sampling methodology could be used within experimental mechanism design.
Behavioral game theory: Thinking, learning and teaching
 JOURNAL OF RISK AND UNCERTAINTY
, 2001
"... ..."
Dynamic Incentive Mechanisms
"... Much of AI is concerned with the design of intelligent agents. A complementary challenge is to understand how to design “rules of encounter” (Rosenschein and Zlotkin 1994) by which to promote simple, robust and beneficial interactions between multiple intelligent agents. This is a natural developmen ..."
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Cited by 3 (1 self)
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Much of AI is concerned with the design of intelligent agents. A complementary challenge is to understand how to design “rules of encounter” (Rosenschein and Zlotkin 1994) by which to promote simple, robust and beneficial interactions between multiple intelligent agents. This is a natural development, as AI is increasingly used for automated decision making in realworld settings. As we extend the ideas of mechanism design from economic theory, the mechanisms (or rules) become algorithmic and many new challenges surface. Starting with a short background on mechanism design theory, the aim of this paper is to provide a nontechnical exposition of recent results on dynamic incentive mechanisms, which provide rules for the coordination of agents in sequential decision problems. The framework of dynamic mechanism design embraces coordinated decision making both in the context of uncertainty about the world external to an agent and also in regard to the dynamics of agent preferences. In addition to tracing some recent developments, we point to ongoing research challenges.
Choosing Samples to Compute HeuristicStrategy Nash
 In Fifth Workshop on AgentMediated Electronic Commerce
, 2003
"... Auctions define games of incomplete information for which it is often too hard to compute the exact BayesianNash equilibrium. Instead, the infinite strategy space is often populated with heuristic strategies, such as myopic bestresponse to prices. Given these heuristic strategies, it can be usef ..."
Abstract
 Add to MetaCart
Auctions define games of incomplete information for which it is often too hard to compute the exact BayesianNash equilibrium. Instead, the infinite strategy space is often populated with heuristic strategies, such as myopic bestresponse to prices. Given these heuristic strategies, it can be useful to evaluate the strategies and the auction design by computing a Nash equilibrium across the restricted strategy space. First, it is necessary to compute the expected payoff for each heuristic strategy profile. This step involves sampling the auction and averaging over multiple simulations, and its cost can dominate the cost of computing the equilibrium given a payoff matrix. In this paper, we propose two information theoretic approaches to determine the next sample through an interleaving of equilibrium calculations and payoff refinement. Initial experiments demonstrate that both methods reduce error in the computed Nash equilibrium as samples are performed at faster rates than naive uniform sampling. The second, faster method, has a lower metadeliberation cost and better scaling properties. We discuss how our sampling methodology could be used within experimental mechanism design.
Articles Dynamic Incentive Mechanisms
"... n Much of AI is concerned with the design of intelligent agents. A complementary challenge is to understand how to design “rules of encounter ” (Rosenschein and Zlotkin 1994) by which to promote simple, robust and beneficial interactions between multiple intelligent agents. This is a natural develop ..."
Abstract
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n Much of AI is concerned with the design of intelligent agents. A complementary challenge is to understand how to design “rules of encounter ” (Rosenschein and Zlotkin 1994) by which to promote simple, robust and beneficial interactions between multiple intelligent agents. This is a natural development, as AI is increasingly used for automated decision making in realworld settings. As we extend the ideas of mechanism design from economic theory, the mechanisms (or rules) become algorithmic and many new challenges surface. Starting with a short background on mechanism design theory, the aim of this paper is to provide a nontechnical exposition of recent results on dynamic