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Agents that react to changing market situations.

Kwang Mong Sim1, Chung Yu Choi

  • 1Dept. of Inf. Eng., Chinese Univ. of Hong Kong, Shatin, China.

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|February 2, 2008
PubMed
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Market-driven agents adapt their concession rates to market dynamics using mathematical functions. This approach enables agents to achieve realistic trading outcomes in simulated large markets.

Area of Science:

  • Artificial Intelligence
  • Computational Economics
  • Agent-Based Modeling

Background:

  • Negotiation agents traditionally employ fixed strategies.
  • Market dynamics necessitate adaptive negotiation behaviors.
  • Existing models lack robust mechanisms for real-time market responsiveness.

Purpose of the Study:

  • To establish foundations for designing market-driven agent strategies.
  • To develop and evaluate a testbed for market-driven agents.
  • To analyze agent performance in large-scale market simulations.

Main Methods:

  • Agents utilize four mathematical functions: eagerness, remaining trading time, trading opportunity, and competition.
  • Strategies are dynamically adjusted based on market conditions and trading progress.

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  • Performance is assessed through simulations and theoretical analyses in large markets.

    • Main Results:

    • Market-driven agents effectively adjust concession rates based on market situations.
    • Agents demonstrate prudent and appropriate concession behaviors.
    • Simulated trading outcomes align with real-world trading intuitions.

    Conclusions:

    • Market-driven strategies provide a robust framework for adaptive negotiation.
    • The proposed approach enhances agent performance in dynamic trading environments.
    • This research offers insights into optimizing agent behavior in complex markets.