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Multi-Issue Negotiation Processes by Evolutionary Simulation, Validation and Social Extensions

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  • Enrico Gerding
  • David van Bragt
  • Han La Poutré

Abstract

We describe a system for bilateral negotiations in which artificial agents aregenerated by an evolutionary algorithm (EA). The negotiations are governed bya finite-horizon version of the alternating-offers protocol. Several issuesare negotiated simulataneously. We first analyse and validate the outcomes ofthe evolutionary system, using the game-theoretic subgame-perfect equilibriumas a benchmark. We then present two extensions of the negotiation model. Inthe first extension agents take into account the fairness of the obtainedpayoff. We find that when the fairness norm is consistently applied during thenegotiation, agents reach symmetric outcomes which are robust and ratherinsensitive to the actual fairness settings. In the second extension we modela competitive market situation where agents have multiple bargainingopportunities before reaching the final agreement. Symmetric outcomes are nowalso obtained, even when the number of bargaining opportunities is small. Wefurthermore study the influence of search or negotiation costs in this game. Copyright Kluwer Academic Publishers 2003

Suggested Citation

  • Enrico Gerding & David van Bragt & Han La Poutré, 2003. "Multi-Issue Negotiation Processes by Evolutionary Simulation, Validation and Social Extensions," Computational Economics, Springer;Society for Computational Economics, vol. 22(1), pages 39-63, August.
    • Handle: RePEc:kap:compec:v:22:y:2003:i:1:p:39-63
    DOI: 10.1023/A:1024592607487
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    Cited by:

    1. Ludo Waltman & Nees Eck & Rommert Dekker & Uzay Kaymak, 2011. "Economic modeling using evolutionary algorithms: the effect of a binary encoding of strategies," Journal of Evolutionary Economics, Springer, vol. 21(5), pages 737-756, December.
    2. D.D.B. Bragt, van & J. A. La Poutr & E. H. Gerding, 2000. "Equilibrium Selection In Evolutionary Bargaining Models," Computing in Economics and Finance 2000 323, Society for Computational Economics.
    3. Herbert Dawid & Joern Dermietzel, 2006. "How Robust is the Equal Split Norm? Responsive Strategies, Selection Mechanisms and the Need for Economic Interpretation of Simulation Parameters," Computational Economics, Springer;Society for Computational Economics, vol. 28(4), pages 371-397, November.
    4. D.D.B. van Bragt and J.A. La Poutre, 2001. "Evolving Automata Negotiate with a Variety of Opponents," Computing in Economics and Finance 2001 118, Society for Computational Economics.
    5. D.D.B. van Bragt & J.A. La Poutré, 2003. "Why Agents for Automated Negotiations Should Be Adaptive," Netnomics, Springer, vol. 5(2), pages 101-118, November.

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