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Toward Reverse Engineering to Economic Analysis: An Overview of Tools and Methodology

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  • Marisa Faggini

    (University of Salerno)

  • Bruna Bruno

    (University of Salerno)

  • Anna Parziale

    (University of Salerno)

Abstract

Following the reverse engineering (RE) approach to analyse an economic complex system is to infer how its underlying mechanism works. The main factors that condition the difficulty of RE are the number of variable components in the system and, most importantly, the interdependence of components on one another and nonlinear dynamics. All those aspects characterize the economic complex systems within which economic agents make their choices. Economic complex systems are adopted in RE science, and they could be used to understand, predict and model the dynamics of the complex systems that enable to define and to control the economic environment. With the RE approach, economic data could be used to peek into the internal workings of the economic complex system, providing information about its underling nonlinear dynamics. The idea of this paper arises from the aim to deepen the comprehension of this approach and to highlight the potential implementation of tools and methodologies based on it to treat economic complex systems. An overview of the literature about the RE is presented, by focusing on the definition and on the state of the art of the research, and then we consider two potential tools that could translate the methodological issues of RE by evidencing advantages and disadvantages for economic analysis: the recurrence analysis and the agent-based model (ABM).

Suggested Citation

  • Marisa Faggini & Bruna Bruno & Anna Parziale, 2022. "Toward Reverse Engineering to Economic Analysis: An Overview of Tools and Methodology," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 13(2), pages 1414-1432, June.
    • Handle: RePEc:spr:jknowl:v:13:y:2022:i:2:d:10.1007_s13132-021-00770-5
    DOI: 10.1007/s13132-021-00770-5
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    References listed on IDEAS

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    Cited by:

    1. Johann Lussange & Stefano Vrizzi & Stefano Palminteri & Boris Gutkin, 2024. "Modelling crypto markets by multi-agent reinforcement learning," Papers 2402.10803, arXiv.org.

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