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Agent-Based Modeling of Economic Instability

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Predictive Econometrics and Big Data (TES 2018)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 753))

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Abstract

Networks increase interdependence, which creates challenges for managing economic risks. This is especially apparent in areas such as financial institutions and enterprise risk management, where the actions of a single agent (firm or bank) can impact all the other agents in interconnected networks. In this paper, we use agent-based modeling (ABM) in order to analyze how local defaults of supply chain participants propagate through the dynamic supply chain network and interbank networks and form avalanches of bankruptcy. We focus on the linkage dependence among agents at the micro-level and estimate the impact on the macro activities. Combining agent-based modeling with the network analysis can shed light on understanding the primary role of banks in lending to the wider real economy. Understanding the linkage dependency among firms and banks can help in the design of regulatory paradigms that rein in systemic risk while enhancing economic growth.

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Author information

Authors and Affiliations

  1. National Defense Academy, Yokosuka, Japan

    Akira Namatame

Authors
  1. Akira Namatame
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Correspondence to Akira Namatame .

Editor information

Editors and Affiliations

  1. Computer Science Department, University of Texas at El Paso, El Paso, Texas, USA

    Vladik Kreinovich

  2. International College, Chiang Mai University, Chiang Mai, Thailand

    Songsak Sriboonchitta

  3. International College, Chiang Mai University, Chiang Mai, Thailand

    Nopasit Chakpitak

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Namatame, A. (2018). Agent-Based Modeling of Economic Instability. In: Kreinovich, V., Sriboonchitta, S., Chakpitak, N. (eds) Predictive Econometrics and Big Data. TES 2018. Studies in Computational Intelligence, vol 753. Springer, Cham. https://doi.org/10.1007/978-3-319-70942-0_18

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  • DOI: https://doi.org/10.1007/978-3-319-70942-0_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-70941-3

  • Online ISBN: 978-3-319-70942-0

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