Computational and Statistical Physics Approaches for Complex Systems and Social Phenomena III

Dear Colleagues,

Understanding social phenomena such politics, economics, social conflicts is challenging not least because these disciplines and domains share several properties of complex systems. They entail numerous interindependent agents interacting through dynamic processes characterized by uncertainty and unpredictability of outcomes, as well as difficulty linking causes to effects. Complex systems are nonlinear, and exhibit self-organization, adaptation, feedback loops, spontaneous order, and emergence. As a consequence, they give rise to “wicked problems”—unanticipated negative consequences of decisions—whose outcomes are difficult to analyze or predict with traditional, discipline-based methodologies. The 2021 Nobel Prize in Physics awarded to Profs. Giorgio Parisi, Suykuro Manabe, and Klaus Hasselmann for their “groundbreaking contributions to our understanding of complex systems” attests to the current focus and promise of methods for studying complexity.

The study of complex social systems requires interdisciplinary approaches, borrowing tools from statistical physics, information theory, and nonlinear dynamics, and applying them to sociology and economics. Statistical physics, in particular, can help overcome some of the obstacles to the study of complex social systems. Statistical physics computational methods have been used in disciplines other than physics, such as biology and neuroscience. By taking advantage of the extremely rapid increase in computational capacity, the applicability of these models has expanded to other social fields. These models have been used in such diverse fields as labor relations, communication through social media, politics, and social polarization. For instance, there have been efforts to apply statistical physics methods to understand and solve current problems such as the social polarization of societies.

For example, trend-based predictions are common in social sciences, and relying on the “all else being equal” linear methods cannot to effectively contend with complex systems. Their results are predictably wrong, except over very short time spans. Decisions based on such predictions are not particularly robust: any deviation of reality from assumptions can seriously undermine decisions based on them. Statistical physics models have been used instead to anticipate possible scenarios for the future. Anticipatory scenarios, increasingly applied in the fields of politics, business, and social and urban planning have enabled decision makers to develop robust strategies for responding to emerging problems, rather than make rigid plans bound to fail.

This Special Issue focuses on investigations of complex social systems using statistical physics simulation methods. These approaches are rooted in analogies between the behaviors of physics particles and the behaviors of human agents interacting in social spaces. We note that such analogies, seldom perfect, challenge the interpretation of physical parameters in terms of human behavior. This challenge is in need of further research.

We invite contributions to this Special Issue from researchers who study complex systems through the use of statistical physics computational methods, such as Monte Carlo simulations. The contributions may be original papers or reviews.

Prof. Dr. Hung T. Diep
Prof. Dr. Miron Kaufman
Prof. Dr. Sanda Kaufman
Guest Editors

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• social phenomena
• complex systems
• Monte Carlo simulations
• statistical physics

• Computational and Statistical Physics Approaches for Complex Systems and Social Phenomena in Entropy (6 articles)