Social physics

Jusup, Marko; Holme, Petter; Kanazawa, Kiyoshi; Takayasu, Misako; Romić, Ivan; Wang, Zhen; Geček, Sunčana; Lipić, Tomislav; Podobnik, Boris; Wang, Lin; Luo, Wei; Klanjšček, Tin; Fan, Jingfang; Boccaletti, Stefano; Perc, Matjaž

doi:10.1016/j.physrep.2021.10.005

Cited by 361 publications

(155 citation statements)

References 882 publications

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“…Understanding the collective and intricate nature of political corruption and other organized crime demands more than simple statistics. In an analogy with complex systems 1 – 4 , where the whole is often more than just the sum of its parts, one may say that the success of criminal organizations depends not only on the individual skills of the criminals, but in fact much more so on their ability to cooperate and create robust organizational structures that are capable of protecting and hiding their illegal activities. The usage of complexity science has been advocated by different authors as an ideal framework to investigate economic crime, organized crime, and corruption 5 – 9 .…”

Section: Introductionmentioning

confidence: 99%

Universality of political corruption networks

Martins

Cunha

Hanley

et al. 2022

Sci Rep

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Corruption crimes demand highly coordinated actions among criminal agents to succeed. But research dedicated to corruption networks is still in its infancy and indeed little is known about the properties of these networks. Here we present a comprehensive investigation of corruption networks related to political scandals in Spain and Brazil over nearly three decades. We show that corruption networks of both countries share universal structural and dynamical properties, including similar degree distributions, clustering and assortativity coefficients, modular structure, and a growth process that is marked by the coalescence of network components due to a few recidivist criminals. We propose a simple model that not only reproduces these empirical properties but reveals also that corruption networks operate near a critical recidivism rate below which the network is entirely fragmented and above which it is overly connected. Our research thus indicates that actions focused on decreasing corruption recidivism may substantially mitigate this type of organized crime.

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Section: Introductionmentioning

confidence: 99%

Universality of political corruption networks

Martins

Cunha

Hanley

et al. 2022

Sci Rep

Self Cite

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“…Meanwhile, the development of network science provides a tool to simplify the complex systems picture. While retaining the ability to see how the system is stitched together into a whole, we can gain insight into the operation of the system by exploring information from networks [ 16 ]. Thus, despite the complexity of TF, the idea of complex systems engineering can be integrated into the research of TF to promote its development.…”

Section: Introductionmentioning

confidence: 99%

A Review of Technological Forecasting from the Perspective of Complex Systems

Feng

Wang

et al. 2022

Entropy

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Technology forecasting (TF) is an important way to address technological innovation in fast-changing market environments and enhance the competitiveness of organizations in dynamic and complex environments. However, few studies have investigated the complex process problem of how to select the most appropriate forecasts for organizational characteristics. This paper attempts to fill this research gap by reviewing the TF literature based on a complex systems perspective. We first identify four contexts (technology opportunity identification, technology assessment, technology trend and evolutionary analysis, and others) involved in the systems of TF to indicate the research boundary of the system. Secondly, the four types of agents (field of analysis, object of analysis, data source, and approach) are explored to reveal the basic elements of the systems. Finally, the visualization of the interaction between multiple agents in full context and specific contexts is realized in the form of a network. The interaction relationship network illustrates how the subjects coordinate and cooperate to realize the TF context. Accordingly, we illustrate suggest five trends for future research: (1) refinement of the context; (2) optimization and expansion of the analysis field; (3) extension of the analysis object; (4) convergence and diversification of the data source; and (5) combination and optimization of the approach.

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“…Thus, a stochastic multi-agent simulation (MAS) framework based on a certain appropriate algorithm, such as Gillespie algorithm [7] , plays an essential role in drawing a precise prediction. However, there have been several works in literature which allow an ODE framework to approximately quantify the basic stochastic features [8] , [9] , [10] , [11] . The MAS approach accounting underlying network effect, which can also capture various realistic scenarios inevitably brought by stochastic components, could be extended to more practical applications than simple epidemiological analysis.…”

Section: Introductionmentioning

confidence: 99%