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A Generic Framework for a Combined Agent-based Market and Production Model

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  • Bas Straatman
  • Danielle Marceau
  • Roger White

Abstract

Agent-based market models are in general based on a-priori defined supply and demand schemes. Likewise, production models assume that prices are known a-priori. In reality prices depend on variable demands and supplies, while demand and supply depend on variable prices, and these two processes are interconnected. This paper describes a model that for the first time simulates a combined agent-based double auction market and production model. The model is built around von Neumann technology matrices (von Neumann, Rev Econ Stud 13(1):1–9, 1946 ) which provide the links between products. Agents possess one or more technologies to produce products from other products. They trade in order to acquire the inputs and sell in order to generate revenue, and the price is determined by a process of negotiation between buyers and sellers. The algorithm of negotiation is based on Cliff’s Zero Intelligence Plus approach (Cliff, Minimal-intelligence agents for bargaining behaviors in market-based environments. Technical report, School of Cognitive and Computing Sciences, University of Sussex, 1997 ), but instead of a single commodity with fixed limit prices the agents change their limit prices for multiple products based on the simulated economic situation. The combination of production and market provides a simple but complete bottom-up model framework for microeconomics. As the results show, the model employs a price mechanism that results in an appropriate allocation of resources without a central command. Copyright Springer Science+Business Media New York 2013

Suggested Citation

  • Bas Straatman & Danielle Marceau & Roger White, 2013. "A Generic Framework for a Combined Agent-based Market and Production Model," Computational Economics, Springer;Society for Computational Economics, vol. 41(4), pages 425-445, April.
    • Handle: RePEc:kap:compec:v:41:y:2013:i:4:p:425-445
    DOI: 10.1007/s10614-012-9341-z
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    References listed on IDEAS

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    1. Carl Chiarella & Giulia Iori, 2002. "A simulation analysis of the microstructure of double auction markets," Quantitative Finance, Taylor & Francis Journals, vol. 2(5), pages 346-353.
    2. Gode, Dhananjay K & Sunder, Shyam, 1993. "Allocative Efficiency of Markets with Zero-Intelligence Traders: Market as a Partial Substitute for Individual Rationality," Journal of Political Economy, University of Chicago Press, vol. 101(1), pages 119-137, February.
    3. Vernon L. Smith, 1962. "An Experimental Study of Competitive Market Behavior," Journal of Political Economy, University of Chicago Press, vol. 70, pages 111-111.
    4. E Ray Canterbery, 2001. "A Brief History of Economics:Artful Approaches to the Dismal Science," World Scientific Books, World Scientific Publishing Co. Pte. Ltd., number 4079, February.
    5. LeBaron, Blake & Arthur, W. Brian & Palmer, Richard, 1999. "Time series properties of an artificial stock market," Journal of Economic Dynamics and Control, Elsevier, vol. 23(9-10), pages 1487-1516, September.
    6. E Ray Canterbery, 2010. "A BRIEF HISTORY OF ECONOMICS:Artful Approaches to the Dismal Science (2nd Edition)," World Scientific Books, World Scientific Publishing Co. Pte. Ltd., edition 2, number 7661, February.
    7. B. LeBaron, 2001. "A builder's guide to agent-based financial markets," Quantitative Finance, Taylor & Francis Journals, vol. 1(2), pages 254-261.
    8. Jason Potts, 2001. "Knowledge and markets," Journal of Evolutionary Economics, Springer, vol. 11(4), pages 413-431.
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    2. van Wijk, Mark T., 2014. "From global economic modelling to household level analyses of food security and sustainability: How big is the gap and can we bridge it?," Food Policy, Elsevier, vol. 49(P2), pages 378-388.
    3. Bernardo Alves Furtado & Isaque Daniel Rocha Eberhardt, 2016. "A Simple Agent-Based Spatial Model of the Economy: Tools for Policy," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 19(4), pages 1-12.

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