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AgentHuman Interactions in the Continuous Double Auction
 IN PROCEEDINGS OF THE 17TH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE
, 2001
"... The Continuous Double Auction (CDA) is the dominant market institution for realworld trading of equities, commodities, derivatives, etc. We describe a series of laboratory experiments that, for the first time, allow human subjects to interact with software bidding agents in a CDA. Our bidding ..."
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Cited by 100 (2 self)
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The Continuous Double Auction (CDA) is the dominant market institution for realworld trading of equities, commodities, derivatives, etc. We describe a series of laboratory experiments that, for the first time, allow human subjects to interact with software bidding agents in a CDA. Our bidding agents use strategies based on extensions of the GjerstadDickhaut and ZeroIntelligencePlus algorithms. We find that agents consistently obtain significantly larger gains from trade than their human counterparts. This was unexpected because both humans and agents have approached theoretically perfect efficiency in prior allhuman or allagent CDA experiments. Another unexpected finding is persistent farfromequilibrium trading, in sharp contrast to the robust convergence observed in previous allhuman or allagent experiments. We consider possible explanations for our empirical findings, and speculate on the implications for future agenthuman interactions in electronic markets.
Analyzing complex strategic interactions in multiagent systems.
 Proceedings of 2002 Workshop on GameTheoretic and DecisionTheoretic Agents (GTDT02),
, 2002
"... Abstract We develop a model for analyzing complex games with repeated interactions, for which a full gametheoretic analysis is intractable. Our approach treats exogenously specified, heuristic strategies, rather than the atomic actions, as primitive, and computes a heuristicpayoff table specifyin ..."
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Cited by 76 (3 self)
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Abstract We develop a model for analyzing complex games with repeated interactions, for which a full gametheoretic analysis is intractable. Our approach treats exogenously specified, heuristic strategies, rather than the atomic actions, as primitive, and computes a heuristicpayoff table specifying the expected payoffs of the joint heuristic strategy space. We analyze two games based on (i) automated dynamic pricing and (ii) continuous double auction. For each game we compute Nash equilibria of previously published heuristic strategies. To determine the most plausible equilibria, we study the replicator dynamics of a large population playing the strategies. In order to account for errors in estimation of payoffs or improvements in strategies, we also analyze the dynamics and equilibria based on perturbed payoffs.
CoEvolutionary Auction Mechanism Design: A Preliminary Report
 In Agent Mediated Electronic Commerce IV
, 2002
"... Auctions can be thought of as resource allocation mechanisms. ..."
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Cited by 42 (8 self)
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Auctions can be thought of as resource allocation mechanisms.
Strategic sequential bidding in auctions using dynamic programming
 In Proceedings of the first International Joint Conference on Autonomous Agents and MultiAgent Systems
, 2002
"... We develop a general framework in which realtime Dynamic Programming (DP) can be used to formulate agent bidding strategies in a broad class of auctions characterized by sequential bidding and continuous clearing. In this framework, states are represented primarily by an agent’s holdings, and trans ..."
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Cited by 40 (1 self)
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We develop a general framework in which realtime Dynamic Programming (DP) can be used to formulate agent bidding strategies in a broad class of auctions characterized by sequential bidding and continuous clearing. In this framework, states are represented primarily by an agent’s holdings, and transition probabilities are estimated from the market event history, along the lines of the “belief function ” approach of Gjerstad and Dickhaut [7]. We use the belief function, combined with a forecast of how it changes over time, as an approximate statetransition model in the DP formulation. The DP is then solved from scratch each time the agent has an opportunity to bid. The resulting algorithm optimizes cumulative longterm discounted profitability, whereas most previous strategies such as GjerstadDickhaut (GD) merely optimize immediate profits. We test our algorithm in a simplified model of a Continuous Double Auction (CDA). Our results show that the DPbased approach reproduces the behavior of GD for small discount parameter γ, and is clearly superior for large values of γ close to 1. We suggest that this algorithm may offer the best performance of any published CDA bidding strategy. The framework our algorithm provides is extensible and can accommodate many market and research aspects.
Zero is Not Enough: On The Lower Limit of Agent Intelligence for Continuous Double Auction Markets
, 1997
"... Gode and Sunder's (1993) results from using "zerointelligence" (zi) traders, that act randomly within a structured market, appear to imply that convergence to the theoretical equilibrium price in continuous doubleauction markets is determined more by market structure than by the ..."
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Cited by 37 (3 self)
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Gode and Sunder's (1993) results from using "zerointelligence" (zi) traders, that act randomly within a structured market, appear to imply that convergence to the theoretical equilibrium price in continuous doubleauction markets is determined more by market structure than by the intelligence of the traders in that market. However, it is demonstrated here that the average transaction prices of zi traders can vary significantly from the theoretical equilibrium value when the market supply and demand are asymmetric, and that the degree of difference from equilibrium is predictable from a priori probabilistic analysis. In this sense, it is shown here that Gode and Sunder's results are artefacts of their experimental regime. Following this, `zerointelligenceplus' (zip) traders are introduced: like zi traders, these simple agents make stochastic bids. Unlike zi traders, they employ an elementary form of machine learning. Groups of zip traders interacting in experimental markets...
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 34 (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.
Explorations in evolutionary design of online auction market mechanisms
 Electronic Commerce Research and Applications
, 2003
"... online auction marketplaces, emarketplaces, automated market mechanism design, traderagents, ZIP traders, genetic algorithms This paper describes the use of a genetic algorithm (GA) to find optimal parametervalues for trading agents that operate in virtual online auction “emarketplaces”, where t ..."
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Cited by 30 (4 self)
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online auction marketplaces, emarketplaces, automated market mechanism design, traderagents, ZIP traders, genetic algorithms This paper describes the use of a genetic algorithm (GA) to find optimal parametervalues for trading agents that operate in virtual online auction “emarketplaces”, where the rules of those marketplaces are also under simultaneous control of the GA. The aim is to use the GA to automatically design new mechanisms for agentbased emarketplaces that are more efficient than online markets designed by (or populated by) humans. The space of possible auctiontypes explored by the GA includes the Continuous Double Auction (CDA) mechanism (as used in most of the world’s financial exchanges), and also two purely onesided mechanisms. Surprisingly, the GA did not always settle on the CDA as an optimum. Instead, novel hybrid auction mechanisms were evolved, which are unlike any existing market mechanisms. In this paper we show that, when the market supply and demand schedules undergo sudden “shock ” changes partway through the evaluation process, twosided hybrid market mechanisms can evolve which may be unlike any humandesigned auction and yet may also be significantly more efficient than any humandesigned market mechanism.
An evolutionary gametheoretic comparision of two double auction market designs
 In AgentMediated Electronic Commerce VI
, 2004
"... Abstract. In this paper we describe an analysis of two double auction markets— the clearing house auction and the continuous double auction. The complexity of these institutions is such that they defy analysis using traditional gametheoretic techniques, and so we use heuristicstrategy approximatio ..."
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Cited by 28 (13 self)
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Abstract. In this paper we describe an analysis of two double auction markets— the clearing house auction and the continuous double auction. The complexity of these institutions is such that they defy analysis using traditional gametheoretic techniques, and so we use heuristicstrategy approximation to provide an approximated gametheoretic analysis. As well as finding heuristicstrategy equilibria for these mechanisms, we subject them to an evolutionary gametheoretic analysis which allows us to quantify which equilibria are more likely to occur. We then weight the design objectives for each mechanism according to the probability distribution over equilibria, which allows us to provide more realistic estimates for the efficiency of each mechanism. 1
Characterizing Effective Auction Mechanisms: Insights from the 2007 TAC Market Design Competition
"... This paper analyzes the entrants to the 2007 TAC Market Design competition. It presents a classification of the entries to the competition, and uses this classification to compare these entries. The paper also attempts to relate market dynamics to the auction rules adopted by these entries and their ..."
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Cited by 26 (14 self)
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This paper analyzes the entrants to the 2007 TAC Market Design competition. It presents a classification of the entries to the competition, and uses this classification to compare these entries. The paper also attempts to relate market dynamics to the auction rules adopted by these entries and their adaptive strategies via a set of posttournament experiments. Based on this analysis, the paper speculates about the design of effective auction mechanisms, both in the setting of this competition and in the more general case. Categories and Subject Descriptors