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Spatial Interactions in Agent-Based Modeling

In: Complexity and Geographical Economics

Author

Listed:
  • Marcel Ausloos

    (Rés. Beauvallon
    eHumanities Group, Royal Netherlands Academy of Arts and Sciences)

  • Herbert Dawid

    (Bielefeld University)

  • Ugo Merlone

    (Università di Torino)

Abstract

Agent Based Modeling (ABM) has become a widespread approach to model complex interactions. In this chapter after briefly summarizing some features of ABM the different approaches in modeling spatial interactions are discussed.It is stressed that agents can interact either indirectly through a shared environment and/or directly with each other. In such an approach, higher-order variables such as commodity prices, population dynamics or even institutions, are not exogenously specified but instead are seen as the results of interactions. It is highlighted in the chapter that the understanding of patterns emerging from such spatial interaction between agents is a key problem as much as their description through analytical or simulation means.The chapter reviews different approaches for modeling agents’ behavior, taking into account either explicit spatial (lattice based) structures or networks. Some emphasis is placed on recent ABM as applied to the description of the dynamics of the geographical distribution of economic activities—out of equilibrium. The Eurace@Unibi Model, an agent-based macroeconomic model with spatial structure, is used to illustrate the potential of such an approach for spatial policy analysis.

Suggested Citation

  • Marcel Ausloos & Herbert Dawid & Ugo Merlone, 2015. "Spatial Interactions in Agent-Based Modeling," Dynamic Modeling and Econometrics in Economics and Finance, in: Pasquale Commendatore & Saime Kayam & Ingrid Kubin (ed.), Complexity and Geographical Economics, edition 127, pages 353-377, Springer.
    • Handle: RePEc:spr:dymchp:978-3-319-12805-4_14
    DOI: 10.1007/978-3-319-12805-4_14
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    Cited by:

    1. Gontis, V. & Havlin, S. & Kononovicius, A. & Podobnik, B. & Stanley, H.E., 2016. "Stochastic model of financial markets reproducing scaling and memory in volatility return intervals," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 1091-1102.
    2. Zhangqi Zhong & Lingyun He, 2022. "Macro-Regional Economic Structural Change Driven by Micro-founded Technological Innovation Diffusion: An Agent-Based Computational Economic Modeling Approach," Computational Economics, Springer;Society for Computational Economics, vol. 59(2), pages 471-525, February.
    3. Simon Cramer & Torsten Trimborn, 2019. "Stylized Facts and Agent-Based Modeling," Papers 1912.02684, arXiv.org.
    4. Vygintas Gontis & Shlomo Havlin & Aleksejus Kononovicius & Boris Podobnik & H. Eugene Stanley, 2015. "Stochastic model of financial markets reproducing scaling and memory in volatility return intervals," Papers 1507.05203, arXiv.org, revised Oct 2016.
    5. Zhangqi, Zhong & Zhuli, Chen & Lingyun, He, 2022. "Technological innovation, industrial structural change and carbon emission transferring via trade-------An agent-based modeling approach," Technovation, Elsevier, vol. 110(C).
    6. Mirjam Schindler & Geoffrey Caruso, 2020. "Emerging urban form – Emerging pollution: Modelling endogenous health and environmental effects of traffic on residential choice," Environment and Planning B, , vol. 47(3), pages 437-456, March.

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