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Evolutionary Models of Bargaining: Comparing Agent-Based Computational and Analytical Approaches to Understanding Convention Evolution

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  • Carpenter, Jeffrey P

Abstract

This paper compares two methodologies that have been used to understand the evolution of bargaining conventions. The first is the analytical approach that employs a standard learning dynamic and computes equilibria numerically. The second approach simulates an environment with a finite population of interacting agents. We compare these two approaches within the context of three variations on a common model. In one variation agents randomly experiment with different demands. A second variation posits assortative interactions, and the third allows for sophistication in agent strategies. The simulation results suggest that the agent-based approach performs well in selecting equilibria in most instances, but exact predicted population distributions often vary from those calculated numerically. Copyright 2002 by Kluwer Academic Publishers

Suggested Citation

  • Carpenter, Jeffrey P, 2002. "Evolutionary Models of Bargaining: Comparing Agent-Based Computational and Analytical Approaches to Understanding Convention Evolution," Computational Economics, Springer;Society for Computational Economics, vol. 19(1), pages 25-49, February.
    • Handle: RePEc:kap:compec:v:19:y:2002:i:1:p:25-49
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    Cited by:

    1. Navarro-Barrientos, Jesús Emeterio & Cantero-Álvarez, Rubén & Matias Rodrigues, João F. & Schweitzer, Frank, 2008. "Investments in random environments," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(8), pages 2035-2046.
    2. Parna Parsapour-Moghaddam & Armaghan Abed-Elmdoust & Reza Kerachian, 2015. "A Heuristic Evolutionary Game Theoretic Methodology for Conjunctive Use of Surface and Groundwater Resources," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(11), pages 3905-3918, September.
    3. Kwang Mong Sim, 2023. "An Incentive-Compatible and Computationally Efficient Fog Bargaining Mechanism," Computational Economics, Springer;Society for Computational Economics, vol. 62(4), pages 1883-1918, December.
    4. Steven Kimbrough & Frederic Murphy, 2009. "Learning to Collude Tacitly on Production Levels by Oligopolistic Agents," Computational Economics, Springer;Society for Computational Economics, vol. 33(1), pages 47-78, February.
    5. Panagiotis Papaioannnou & Lucia Russo & George Papaioannou & Constantinos Siettos, 2013. "Can social microblogging be used to forecast intraday exchange rates?," Papers 1310.5306, arXiv.org.
    6. Dospinescu, Andrei Silviu, 2011. "Analyzing the Dynamics of Relative Prices on a Market with Speculative and Non-Speculative Agents Based on the Evolutionary Model," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(1), pages 72-87, March.
    7. Aggio, Gustavo de Oliveira, 2011. "Emergência de convenções sociais - Uma análise a partir da simulação de interações descentralizadas caracterizadas pela disposição a imitação de comportamento," Revista Brasileira de Economia - RBE, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil), vol. 65(1), March.
    8. 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.
    9. Harting, Philipp & Radi, Davide, 2020. "Residential segregation: The role of inequality and housing subsidies," Journal of Economic Behavior & Organization, Elsevier, vol. 178(C), pages 801-819.
    10. Panagiotis Papaioannou & Thomas Dionysopoulos & Dietmar Janetzko & Constantinos Siettos, 2016. "S&P500 Forecasting and Trading using Convolution Analysis of Major Asset Classes," Papers 1612.04370, arXiv.org.
    11. Dawid, Herbert, 2007. "Evolutionary game dynamics and the analysis of agent-based imitation models: The long run, the medium run and the importance of global analysis," Journal of Economic Dynamics and Control, Elsevier, vol. 31(6), pages 2108-2133, June.
    12. Panagiotis Papaioannou & Lucia Russo & George Papaioannou & Constantinos Siettos, 2013. "Can social microblogging be used to forecast intraday exchange rates?," Netnomics, Springer, vol. 14(1), pages 47-68, November.

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