Using Evolutionary Game-Theory to Analyse the Performance of Trading Strategies in a Continuous Double Auction Market

In agent-based computational economics, many different trading strategies have been proposed. Given the kinds of market that such trading strategies are employed in, it is clear that the performance of the strategies depends heavily on the behavior of other traders. However, most trading strategies are studied in homogeneous populations, and those tests that have been carried out on heterogeneous populations are limited to a small number of strategies. In this paper we extend the range of strategies that have been exposed to a more extensive analysis, measuring the performance of eight trading strategies using an approach based on evolutionary game theory.

[1]  John Dickhaut,et al.  Price Formation in Double Auctions , 2001, E-Commerce Agents.

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

[3]  Leigh Tesfatsion,et al.  Market power and efficiency in a computational electricity market with discriminatory double-auction pricing , 2001, IEEE Trans. Evol. Comput..

[4]  Peter McBurney,et al.  An evolutionary game-theoretic comparison of two double-auction market designs , 2004, AAMAS'04.

[5]  J. Mcmillan.  Reinventing the Bazaar: A Natural History of Markets , 2002 .

[6]  Mark A. Satterthwaite,et al.  The Bayesian theory of the k-double auction: Santa Fe Institute Studies in the Sciences of Complexity , 2018 .

[7]  Daniel Friedman,et al.  Evolutionary economics goes mainstream: A review of the theory of learning in games , 1998 .

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

[9]  Vernon L. Smith,et al.  Experimental Auction Markets and the Walrasian Hypothesis , 1965, Journal of Political Economy.

[10]  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.

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

[12]  Simon Parsons,et al.  A novel method for automatic strategy acquisition in N-player non-zero-sum games , 2006, AAMAS '06.

[13]  R. McAfee,et al.  A dominant strategy double auction , 1992 .

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

[15]  Nicholas R. Jennings,et al.  A Risk-Based Bidding Strategy for Continuous Double Auctions , 2004, ECAI.

[16]  Elizabeth Sklar,et al.  Reducing price fluctuation in continuous double auctions through pricing policy and shout improvement , 2006, AAMAS '06.

[17]  Vernon L. Smith.  Papers in Experimental Economics: An Experimental Study of Competitive Market Behavior , 1962 .

[18]  L. Samuelson.  Evolutionary Games and Equilibrium Selection , 1997 .

[19]  Javier Gil-Bazo,et al.  PRICE DYNAMICS, INFORMATIONAL EFFICIENCY, AND WEALTH DISTRIBUTION IN CONTINUOUS DOUBLE‐AUCTION MARKETS , 2007, Comput. Intell..

[20]  D. Cliff.  Minimal-Intelligence Agents for Bargaining Behaviors in Market-Based Environments , 1997 .

[21]  Daniel Friedman,et al.  Markups in double auction markets , 2007 .

[22]  R. Palmer,et al.  Characterizing effective trading strategies: Insights from a computerized double auction tournament , 1994 .

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

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