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ASPEN: A Microsimulation Model of the Economy

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  • Basu, N
  • Pryor, R
  • Quint, T

Abstract

In this report we present ASPEN, a new agent-based microeconomic simulation model of the U.S. economy being developed at Sandia National Laboratories (SNL). The model is notable because it allows a large number of individual economic agents to be modeled at a high level of detail and with a great degree of freedom. Some of the features of ASPEN are (a) a sophisticated message-passing system which allows individual pairs of agents to communicate with one another, (b) the use of genetic algorithms to simulate certain agents' learning, and (c) a detailed financial sector which includes a banking system and a bond market. Results from runs of the model are also presented. Citation Copyright 1998 by Kluwer Academic Publishers.

Suggested Citation

  • Basu, N & Pryor, R & Quint, T, 1998. "ASPEN: A Microsimulation Model of the Economy," Computational Economics, Springer;Society for Computational Economics, vol. 12(3), pages 223-241, December.
    • Handle: RePEc:kap:compec:v:12:y:1998:i:3:p:223-41
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    Cited by:

    1. Cincotti, Silvano & Raberto, Marco & Teglio, Andrea, 2010. "Credit money and macroeconomic instability in the agent-based model and simulator Eurace," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 4, pages 1-32.
    2. van de Ven, J., 2001. "Simulating Cohort Demographic Characteristics for Australia," Department of Economics - Working Papers Series 779, The University of Melbourne.
    3. van de Ven, J., 2001. "Simulating Cohort Earnings for Australia," Department of Economics - Working Papers Series 780, The University of Melbourne.
    4. Ashraf, Quamrul & Gershman, Boris & Howitt, Peter, 2017. "Banks, market organization, and macroeconomic performance: An agent-based computational analysis," Journal of Economic Behavior & Organization, Elsevier, vol. 135(C), pages 143-180.
    5. Ouyang, Min, 2014. "Review on modeling and simulation of interdependent critical infrastructure systems," Reliability Engineering and System Safety, Elsevier, vol. 121(C), pages 43-60.
    6. Ebrahim Bagheri & Ali A. Ghorbani, 2010. "UML-CI: A reference model for profiling critical infrastructure systems," Information Systems Frontiers, Springer, vol. 12(2), pages 115-139, April.
    7. Chopra, Shauhrat S. & Khanna, Vikas, 2015. "Interconnectedness and interdependencies of critical infrastructures in the US economy: Implications for resilience," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 436(C), pages 865-877.
    8. James Sprigg & Mark Ehlen, 2007. "Comparative dynamics in an overlapping-generations model: the effects of quasi-rational discrete choice on finding and maintaining Nash equilibrium," Computational Economics, Springer;Society for Computational Economics, vol. 29(1), pages 69-96, February.
    9. Marco Raberto & Andrea Teglio & Silvano Cincotti, 2008. "Integrating Real and Financial Markets in an Agent-Based Economic Model: An Application to Monetary Policy Design," Computational Economics, Springer;Society for Computational Economics, vol. 32(1), pages 147-162, September.
    10. Howitt, Peter, 2012. "What have central bankers learned from modern macroeconomic theory?," Journal of Macroeconomics, Elsevier, vol. 34(1), pages 11-22.
    11. Ouyang, Min & Dueñas-Osorio, Leonardo, 2011. "An approach to design interface topologies across interdependent urban infrastructure systems," Reliability Engineering and System Safety, Elsevier, vol. 96(11), pages 1462-1473.

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