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From agent-based modeling to actor-based reactive systems in the analysis of financial networks

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  • Silvia Crafa

    (Dipartimento di Matematica - Università di Padova)

Abstract

We present a new framework for the analysis of financial networks, called Actor-based Reactive Systems (ARS), that pushes further the Agent-Based approach (ABM) by resorting to ideas coming from the study of distributed systems in computer science. Two distinctive features, namely a fundamentally different management of time and a fully decentralized control logic, have a profound impact in terms of expressiveness of analysis, flexibility of modeling, and efficiency of experimentation. To illustrate the feasibility of the framework, we develop a realistic case study by analyzing the systemic risk of a model of the European banking network with a nontrivial contagion procedure, that combines an initial asset shock with the negative feedback loop triggered by asset fire sales. We show that, compared to ABMs, ARSs bring about finer-grained analyses, with a greater degree of heterogeneity and adaptivity of economic agents. Moreover, the very low computational cost and the detailed account of the system’s execution support the design and the development of very flexible stress tests to rapidly experiment with many hypothetical scenarios in a test-oriented style.

Suggested Citation

  • Silvia Crafa, 2021. "From agent-based modeling to actor-based reactive systems in the analysis of financial networks," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 16(3), pages 649-673, July.
    • Handle: RePEc:spr:jeicoo:v:16:y:2021:i:3:d:10.1007_s11403-021-00323-8
    DOI: 10.1007/s11403-021-00323-8
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    Cited by:

    1. Ivan Jericevich & Patrick Chang & Tim Gebbie, 2021. "Simulation and estimation of an agent-based market-model with a matching engine," Papers 2108.07806, arXiv.org, revised Aug 2021.
    2. Dicks, Matthew & Paskaramoorthy, Andrew & Gebbie, Tim, 2024. "A simple learning agent interacting with an agent-based market model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 633(C).

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