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ACE Models of Endogenous Interactions

In: Handbook of Computational Economics

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  • Vriend, Nicolaas J.

Abstract

Various approaches used in Agent-based Computational Economics (ACE) to model endogenously determined interactions between agents are discussed. This concerns models in which agents not only (learn how to) play some (market or other) game, but also (learn to) decide with whom to do that (or not).

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This chapter was published in:

  • Leigh Tesfatsion & Kenneth L. Judd (ed.), 2006. "Handbook of Computational Economics," Handbook of Computational Economics, Elsevier, edition 1, volume 2, number 2, 00.
    This item is provided by Elsevier in its series Handbook of Computational Economics with number v:2-21.

    Handle: RePEc:eee:hecchp:v:2-21

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    References

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    Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
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    1. Romans Pancs & Nicolaas J. Vriend, 2003. "Schelling's Spatial Proximity Model of Segregation Revisited," Working Papers 487, Queen Mary, University of London, School of Economics and Finance.
    2. Kirman, Alan P. & Vriend, Nicolaas J., 2001. "Evolving market structure: An ACE model of price dispersion and loyalty," Journal of Economic Dynamics and Control, Elsevier, vol. 25(3-4), pages 459-502, March.
    3. Joshua M. Epstein & Robert L. Axtell, 1996. "Growing Artificial Societies: Social Science from the Bottom Up," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262550253, December.
    4. Oechssler, Jorg, 1997. "Decentralization and the coordination problem," Journal of Economic Behavior & Organization, Elsevier, vol. 32(1), pages 119-135, January.
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    Cited by:
    1. João Bernardino & Tanya Araújo, 2010. "On Positional Consumption and Technological Innovation- an Agent-based Approach," Working Papers Department of Economics 2010/04, ISEG - School of Economics and Management, Department of Economics, University of Lisbon.
    2. Haydée Lugo & Raúl Jiménez, 2006. "Incentives to Cooperate in Network Formation," Computational Economics, Society for Computational Economics, vol. 28(1), pages 15-27, August.
    3. Dan Ladley & Seth Bullock, 2008. "The Strategic Exploitation of Limited Information and Opportunity in Networked Markets," Computational Economics, Society for Computational Economics, vol. 32(3), pages 295-315, October.
    4. Chen, Shu-Heng, 2012. "Varieties of agents in agent-based computational economics: A historical and an interdisciplinary perspective," Journal of Economic Dynamics and Control, Elsevier, vol. 36(1), pages 1-25.
    5. Bargigli, Leonardo & Tedeschi, Gabriele, 2014. "Interaction in agent-based economics: A survey on the network approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 399(C), pages 1-15.
    6. Narine Udumyan & Juliette Rouchier & Dominique Ami, 2014. "Integration of Path-Dependency in a Simple Learning Model: The Case of Marine Resources," Computational Economics, Society for Computational Economics, vol. 43(2), pages 199-231, February.
    7. Chad Seagren, 2011. "Examining social processes with agent-based models," The Review of Austrian Economics, Springer, vol. 24(1), pages 1-17, March.
    8. Karolina Safarzyńska & Jeroen Bergh, 2010. "Evolutionary models in economics: a survey of methods and building blocks," Journal of Evolutionary Economics, Springer, vol. 20(3), pages 329-373, June.
    9. Chang, Myong-Hun & Harrington, Joseph Jr., 2006. "Agent-Based Models of Organizations," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 26, pages 1273-1337 Elsevier.
    10. Jack Robles, 2008. "Evolution, bargaining, and time preferences," Economic Theory, Springer, vol. 35(1), pages 19-36, April.

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