Abstract
AbstractFollowing 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).
Highlights
Since many years, the development, analysis of and experimentation with models are tools of science and applied systems in economics
To model means to develop a model as a representative of a system, and it has sense only if the produced model and its dynamics sufficiently correspond to the original system
In “The Reverse Engineering Approach to Discover the Dynamics of Economic Complex Systems”, we propose an example of models for complex system dynamics by developing a method of reverse engineering (RE) to regain a class of complex system; in “Reverse Engineering Framework for Controlling Chaos: the Recurrence Analysis”, we stress the inadequacy of the top-down and bottom-up approach and we suggest a new approach, chaotic itinerancy (Kaneko & Tsuda, 1994), for integrating them
Summary
The development, analysis of and experimentation with models are tools of science and applied systems in economics. To model means to develop a model as a representative of a system, and it has sense only if the produced model and its dynamics sufficiently correspond to the original system. A micro model on the other hand represents different components, entities or units separately. The reductionist approach of linear systems has been challenged by the discovery of complex system theories focused on “interaction among parts of the systems and not so much on the characteristics of the parts themselves”. In this framework, the behaviour of the single parts does not explain the behaviour of the whole
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
