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Testing institutional arrangements via agent-based modeling: a U.S. electricity market example

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  • Li, Hongyan
  • Sun, Junjie
  • Tesfatsion, Leigh

Abstract

Many critical goods and services in modern-day economies are produced and distributed through complex institutional arrangements. Agent-based computational economics (ACE) modeling tools are capable of handling this degree of complexity. In concrete support of this claim, this study presents an ACE test bed designed to permit the exploratory study of restructured U.S. wholesale power markets with transmission grid congestion managed by locational marginal prices (LMPs). Illustrative findings are presented showing how spatial LMP cross-correlation patterns vary systematically in response to changes in the price responsiveness of wholesale power demand when wholesale power sellers have learning capabilities. These findings highlight several distinctive features of ACE modeling: namely, an emphasis on process rather than on equilibrium; an ability to capture complicated structural, institutional, and behavioral real-world aspects (micro-validation); and an ability to study the effects of changes in these aspects on spatial and temporal outcome distributions.

Suggested Citation

  • Li, Hongyan & Sun, Junjie & Tesfatsion, Leigh, 2010. "Testing institutional arrangements via agent-based modeling: a U.S. electricity market example," ISU General Staff Papers 201002020800001088, Iowa State University, Department of Economics.
    • Handle: RePEc:isu:genstf:201002020800001088
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    References listed on IDEAS

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    1. Tesfatsion, Leigh & Judd, Kenneth L., 2006. "Handbook of Computational Economics, Vol. 2: Agent-Based Computational Economics," Staff General Research Papers Archive 10368, Iowa State University, Department of Economics.
    2. Tesfatsion, Leigh, 2006. "Agent-Based Computational Economics: A Constructive Approach to Economic Theory," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 16, pages 831-880, Elsevier.
    3. Leigh Tesfatsion, 2002. "Agent-Based Computational Economics," Computational Economics 0203001, University Library of Munich, Germany, revised 15 Aug 2002.
    4. Leigh Tesfatsion & Kenneth L. Judd (ed.), 2006. "Handbook of Computational Economics," Handbook of Computational Economics, Elsevier, edition 1, volume 2, number 2.
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    Cited by:

    1. Paul L. Borrill & Leigh Tesfatsion, 2011. "Agent-based Modeling: The Right Mathematics for the Social Sciences?," Chapters, in: John B. Davis & D. Wade Hands (ed.), The Elgar Companion to Recent Economic Methodology, chapter 11, Edward Elgar Publishing.
    2. Juan Manuel Larrosa, 2016. "Agentes computacionales y análisis económico," Revista de Economía Institucional, Universidad Externado de Colombia - Facultad de Economía, vol. 18(34), pages 87-113, January-J.
    3. Bozhechkova Alexandra & Trunin Pavel & Sinelnikova-Muryleva Elena & Petrova Diana & Chentsov Alexander, 2018. "Building of monetary and currency markets models," Research Paper Series, Gaidar Institute for Economic Policy, issue 175P, pages 1-96.

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    More about this item

    JEL classification:

    • C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling
    • D4 - Microeconomics - - Market Structure, Pricing, and Design
    • D6 - Microeconomics - - Welfare Economics
    • L1 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance
    • L3 - Industrial Organization - - Nonprofit Organizations and Public Enterprise
    • L94 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Electric Utilities
    • Q4 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy

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