How to Choose the Bidding Strategy in Continuous Double Auctions: Imitation Versus Take-The-Best Heuristics

Human-subject market experiments have established in a wide variety of environments that the Continuous Double Auction (CDA) guarantees the maximum efficiency (100 percent) and the transaction prices converge quickly to the competitive equilibrium price. Since in human-subject experiments we can not control the agents' behaviour, one would like to know if these properties (quick price convergence and high market efficiency) hold for alternative agents' bidding strategies. We go a step farther: we substitute human agents by artificial agents to calibrate the agents' behaviour . In this paper we demonstrate that price convergence and allocative market efficiency in CDA markets depend on the proportion of the bidding strategies (Kaplan, Zero-Intelligence Plus, and GD) that agents have on both market sides. As a result, price convergence may not be achieved. The interesting question to ask is: can convergence be assured if the agents choose their bidding strategies? Since humans are frugal we explore two fast & frugal heuristics (imitation versus take-the-best) to choose one of three bidding strategies in order to answer this question. We find that the take-the-best choice performs much better than the imitation heuristic in the three market environments analyzed. Our experiment can be interpreted as a test to see whether an individual learning outperforms social learning or individual rationality (take-the-best) outperforms ecological rationality (imitation), for a given relevant institution (the CDA) in alternative environments.

[1]  Adolfo López-Paredes,et al.  An Artificial Economics View of the Walrasian and Marshallian Stability , 2007 .

[2]  Adolfo López-Paredes,et al.  Strategic Behaviour in Continuous Double Auction , 2006 .

[3]  Rajarshi Das,et al.  Agent-Human Interactions in the Continuous Double Auction , 2001, IJCAI.

[4]  Dhananjay K. Gode,et al.  Allocative Efficiency of Markets with Zero-Intelligence Traders: Market as a Partial Substitute for Individual Rationality , 1993, Journal of Political Economy.

[5]  Nicolaas J. Vriend,et al.  An Illustration of the Essential Difference between Individual and Social Learning, and its Consequences for Computational Analyses , 1998 .

[6]  G Gigerenzer,et al.  Reasoning the fast and frugal way: models of bounded rationality. , 1996, Psychological review.

[7]  Shu-Heng Chen,et al.  Toward an Agent-Based Computational Modeling of Bargaining Strategies in Double Auction Markets with Genetic Programming , 2000, IDEAL.

[8]  Lev Muchnik,et al.  Markov Nets and the NetLab Platform: Application to Continuous Double Auction , 2007 .

[9]  C. Preist,et al.  Adaptive agents in a persistent shout double auction , 1998, ICE '98.

[10]  V. Smith.  An Experimental Study of Competitive Market Behavior , 1962, Journal of Political Economy.

[11]  E. Dimson,et al.  A brief history of market efficiency , 1998 .

[12]  V. Smith.  Constructivist and Ecological Rationality in Economics , 2003 .

[13]  Pınar Yolum,et al.  Hybrid models for achieving and maintaining cooperative symbiotic groups , 2009 .

[14]  Adolfo López-Paredes,et al.  Learning in Continuous Double Auction Market , 2006 .

[15]  D. Cliff,et al.  Zero is Not Enough: On The Lower Limit of Agent Intelligence For Continuous Double Auction Markets† , 1997 .

[16]  Araújo,et al.  An Evolutionary theory of economic change , 1983 .

[17]  Vernon L. Smith,et al.  Bidding and Auctioning Institutions: Experimental Results , 1976 .

[18]  Adolfo López-Paredes,et al.  Testing Marshallian and Walrasian Instability with an Agent-Based Model , 2008, Adv. Complex Syst..

[19]  R. Marks.  Market Design Using Agent-Based Models , 2006 .

[20]  G. Tesauro,et al.  Analyzing Complex Strategic Interactions in Multi-Agent Systems , 2002 .

[21]  A. Roth,et al.  Learning in Extensive-Form Games: Experimental Data and Simple Dynamic Models in the Intermediate Term* , 1995 .

[22]  Nicolaas J. Vriend,et al.  Imitation of successful behaviour in cournot markets , 2003 .

[23]  Rajarshi Das,et al.  High-performance bidding agents for the continuous double auction , 2001, EC '01.

[24]  Mario Paolucci,et al.  Intelligent Social Learning , 2001, J. Artif. Soc. Soc. Simul..

[25]  D. Cliff.  Evolutionary Optimization of Parameter Sets for Adaptive Software-Agent Traders in Continuous Double Auction Markets , 2001 .

[26]  Nicolaas J. Vriend,et al.  Evolving market structure: An ACE model of price dispersion and loyalty , 2001 .

[27]  R. Selten,et al.  Bounded rationality: The adaptive toolbox , 2000 .

[28]  C. A. Holt,et al.  5. Industrial Organization: A Survey of Laboratory Research , 1995 .

[29]  Marta Posada,et al.  Strategic Software Agents in Continuous Double Auction Under Dynamic Environments , 2006, IDEAL.

[30]  J. Dickhaut,et al.  Price Formation in Double Auctions , 1998 .

[31]  J. Ledyard,et al.  Theories of price formation and exchange in double oral auctions , 1986 .

[32]  Shu-Heng Chen,et al.  Trading Restrictions, Price Dynamics and allocative Efficiency in Double Auction Markets: Analysis Based on Agent-Based Modeling and Simulations , 2003, Adv. Complex Syst..

[33]  Steven E. Wallis,et al.  Axelrod Meets Cournot: Oligopoly and the Evolutionary Metaphor , 2002 .