Elisa C. Baek, Ryan Hyon, Karina López, Mason A. Porter & Carolyn Parkinson 

Nature Communications volume 16, Article number: 5864 (2025)

It has been proposed that information sharing, which is a ubiquitous and consequential behavior, plays a critical role in cultivating and maintaining a sense of shared reality. Across three studies, we test this theory by investigating whether or not people are especially likely to share information that they believe will be interpreted similarly by others in their social circles. Using neuroimaging data collected while people who live in the same residential community viewed brief film clips, we find that more similar neural responses across participants is associated with a greater likelihood to share content. We then test this relationship using two behavioral studies and find (1) that people are particularly likely to share content that they believe others in their social circles will interpret similarly and (2) that perceived similarity with others leads to increased sharing likelihood. In concert, our findings support the idea that people are driven to share information to create and reinforce shared understanding, which is critical to social connection.

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Ulrik Brandes, Hadi Sotudeh, Doğan Parlak, Paolo Laffranchi & Mert Erkul
npj Complexity volume 2, Article number: 25 (2025)

We propose shape graphs as instantaneous representations of spatial arrangements in association football (soccer). Shape graphs are a novel type of subgraph of Delaunay triangulations inspired by related applications in fingerprinting and facial expression detection. They provide a foundational data structure that supports various downstream tasks in a manner that is flexible, explainable, and efficient. While previous approaches aggregate spatial positions over periods of time to stabilize the underlying signal, we instead interpret each frame individually for increased explainability at the highest possible temporal resolution. As an example use case we introduce position plots, a novel visualization capturing the characteristic fluidity of relative positioning during a game while retaining the possibility to add match context.

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Rebecca Carstens, Raj Deshpande, Pau Esteve, Nicolò Fidelibus, Sara Linde Neven, Ramona Ottow, Lokamruth K. R., Paula Rodríguez-Sánchez, Luca Santagata, Javier M. Buldú, Brennan Klein, Maddalena Torricelli

Elite football is believed to have evolved in recent years, but systematic evidence for the pace and form of that change is sparse. Drawing on event-level records for 13,067 matches in ten top-tier men’s and women’s leagues in England, Spain, Germany, Italy, and the United States (2020-2025), we quantify match dynamics with two views: conventional performance statistics and pitch-passing networks that track ball movement among a grid of pitch (field) regions. Between 2020 and 2025, average passing volume, pass accuracy, and the percent of passes made under pressure all rose. In general, the largest year-on-year changes occurred in women’s competitions. Network measures offer alternative but complementary perspectives on the changing gameplay in recent years, normalized outreach in the pitch passing networks decreased, while the average shortest path lengths increased, indicating a wider ball circulation. Together, these indicators point to a sustained intensification of collective play across contemporary professional football.

Read the full article at: arxiv.org

Carlos Mendoza Montano

Front. Complex Syst., 31 July 2025

Traditional evolutionary theory explains adaptation and diversification through random mutation and natural selection. While effective in accounting for trait variation and fitness optimization, this framework provides limited insight into the physical principles underlying the spontaneous emergence of complex, ordered systems. A complementary theory is proposed: that evolution is fundamentally driven by the reduction of informational entropy. Grounded in non-equilibrium thermodynamics, systems theory, and information theory, this perspective posits that living systems emerge as self-organizing structures that reduce internal uncertainty by extracting and compressing meaningful information from environmental noise. These systems increase in complexity by dissipating energy and exporting entropy, while constructing coherent, predictive internal architectures, fully in accordance with the second law of thermodynamics. Informational entropy reduction is conceptualized as operating in synergy with Darwinian mechanisms. It generates the structural and informational complexity upon which natural selection acts, whereas mutation and selection refine and stabilize those configurations that most effectively manage energy and information. This framework extends previous thermodynamic models by identifying informational coherence, not energy efficiency, as the primary evolutionary driver. Recently formalized metrics, Information Entropy Gradient (IEG), Entropy Reduction Rate (ERR), Compression Efficiency (CE), Normalized Information Compression Ratio (NICR), and Structural Entropy Reduction (SER), provide testable tools to evaluate entropy-reducing dynamics across biological and artificial systems. Empirical support is drawn from diverse domains, including autocatalytic networks in prebiotic chemistry, genome streamlining in microbial evolution, predictive coding in neural systems, and ecosystem-level energy-information coupling. Together, these examples demonstrate that informational entropy reduction is a pervasive, measurable feature of evolving systems. While this article presents a theoretical perspective rather than empirical results, it offers a unifying explanation for major evolutionary transitions, the emergence of cognition and consciousness, the rise of artificial intelligence, and the potential universality of life. By embedding evolution within general physical laws that couple energy dissipation to informational compression, this framework provides a generative foundation for interdisciplinary research on the origin and trajectory of complexity.

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