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

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Part of the Lecture Notes in Computer Science book series (LNAI,volume 4865)

Abstract

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.

Keywords

  • Nash Equilibrium
  • Trading Strategy
  • Dominant Strategy
  • Payoff Matrix
  • Replicator Dynamic

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Cai, K., Niu, J., Parsons, S. (2008). Using Evolutionary Game-Theory to Analyse the Performance of Trading Strategies in a Continuous Double Auction Market. In: Tuyls, K., Nowe, A., Guessoum, Z., Kudenko, D. (eds) Adaptive Agents and Multi-Agent Systems III. Adaptation and Multi-Agent Learning. AAMAS ALAMAS ALAMAS 2005 2007 2006. Lecture Notes in Computer Science(), vol 4865. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77949-0_4

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  • DOI: https://doi.org/10.1007/978-3-540-77949-0_4

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