Pluralistic Modeling of Complex Systems

TL;DR

This paper advocates for a pluralistic modeling approach for complex systems, emphasizing the integration of diverse models to better understand and predict socio-economic and natural phenomena amidst inherent uncertainties.

Contribution

It introduces a paradigm shift towards a pluralistic or possibilistic modeling framework that combines multiple, potentially inconsistent, approaches to better capture complex system behaviors.

Findings

Highlights limitations of single-model approaches in complex systems

Proposes integrating diverse models for more robust understanding

Identifies promising interdisciplinary collaboration areas

Abstract

The modeling of complex systems such as ecological or socio-economic systems can be very challenging. Although various modeling approaches exist, they are generally not compatible and mutually consistent, and empirical data often do not allow one to decide what model is the right one, the best one, or most appropriate one. Moreover, as the recent financial and economic crisis shows, relying on a single, idealized model can be very costly. This contribution tries to shed new light on problems that arise when complex systems are modeled. While the arguments can be transferred to many different systems, the related scientific challenges are illustrated for social, economic, and traffic systems. The contribution discusses issues that are sometimes overlooked and tries to overcome some frequent misunderstandings and controversies of the past. At the same time, it is highlighted how some long-standing scientific puzzles may be solved by considering non-linear models of heterogeneous agents with spatio-temporal interactions. As a result of the analysis, it is concluded that a paradigm shift towards a pluralistic or possibilistic modeling approach, which integrates multiple world views, is overdue. In this connection, it is argued that it can be useful to combine many different approaches to obtain a good picture of reality, even though they may be inconsistent. Finally, it is identified what would be profitable areas of collaboration between the socio-economic, natural, and engineering sciences.