Abstract
The range of application of methodologies of complexity science, interdisciplinary by nature, has spread even more broadly across disciplines after the dawn of this century. Specifically, applications to public policy and corporate strategies have proliferated in tandem. This paper reviews the most used complex systems methodologies with an emphasis on public policy. We briefly present examples, pros, and cons of agent‐based modeling, network models, dynamical systems, data mining, and evolutionary game theory. Further, we illustrate some specific experiences of large applied projects in macroeconomics, urban systems, and infrastructure planning. We argue that agent‐based modeling has established itself as a strong tool within scientific realm. However, adoption by policy‐makers is still scarce. Considering the huge amount of exemplary, successful applications of complexity science across the most varied disciplines, we believe policy is ready to become an actual field of detailed and useful applications.
Highlights
Speaking, complex systems are those in which the sum of the parts is insufficient to describe the macroscopic properties of systems’ behavior and evolution [1,2,3]
Social actions, carried out by millions of individuals interacting in a multitude of way and through traditional or digital means, and economic processes, where highly heterogeneous economic actors are interconnected by transactions, ownership relations, competition, and mutualism, are two paradigmatic kinds of complex systems
The authors suggest [136] that the core reasons of rising unemployment are lower innovation rates, workers’ skills deterioration, and reduced firms entry dynamics. This is relevant because it goes against typical policy recommendations based on Dynamic Stochastic General Equilibrium (DSGE) applications hitherto supported by international institutions such as OECD and the IMF, but which is under discussion [138]
Summary
Complex systems are those in which the sum of the parts is insufficient to describe the macroscopic properties of systems’ behavior and evolution [1,2,3]. As an attempt to design the operation of such interactions, presupposes some level of comprehension of the mechanisms, processes, and likely trajectories while maintaining a strict knowledge of the inherent incompleteness of modeling complex systems [8,9,10,11,12]. This view that policies are complex enables the application of complex systems’ methodologies onto the analysis of public (and private) policy-making.
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