Luca Gallo, Lucas Lacasa, Vito Latora & Federico Battiston 
Nature Communications volume 15, Article number: 4754 (2024)

Many real-world complex systems are characterized by interactions in groups that change in time. Current temporal network approaches, however, are unable to describe group dynamics, as they are based on pairwise interactions only. Here, we use time-varying hypergraphs to describe such systems, and we introduce a framework based on higher-order correlations to characterize their temporal organization. The analysis of human interaction data reveals the existence of coherent and interdependent mesoscopic structures, thus capturing aggregation, fragmentation and nucleation processes in social systems. We introduce a model of temporal hypergraphs with non-Markovian group interactions, which reveals complex memory as a fundamental mechanism underlying the emerging pattern in the data.

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Klaus Jaffe

Measuring complexity in multidimensional systems with high degrees of freedom and a variety of types of information, remains an important challenge. Complexity of a system is related to the number and variety of components, the number and type of interactions among them, the degree of redundancy, and the degrees of freedom of the system. Examples show that different disciplines of science converge in complexity measures for low and high dimensional problems. For low dimensional systems, such as coded strings of symbols (text, computer code, DNA, RNA, proteins, music), Shannon’s Information Entropy (expected amount of information in an event drawn from a given distribution) and Kolmogorov‘s Algorithmic Complexity (the length of the shortest algorithm that produces the object as output), are used for quantitative measurements of complexity. For systems with more dimensions (ecosystems, brains, social groupings), network science provides better tools for that purpose. For complex highly multidimensional systems, none of the former methods are useful. Useful Information Φ, as proposed by Infodynamics, can be related to complexity. It can be quantified by measuring the thermodynamic Free Energy F and/or useful Work it produces. Complexity measured as Total Information I, can then be defined as the information of the system, that includes Φ, useless information or Noise N, and Redundant Information R. Measuring one or more of these variables allows quantifying and classifying complexity.

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Zhengyi Fang; Rebecca Ahrnsbrak; Andy Rekito

JAMA Data Brief

New data from the Medical Expenditure Panel Survey (MEPS) Household Component support prior findings that about 7% of US adults have had post–COVID-19 condition, also known as long COVID. The household survey of the US civilian noninstitutionalized population, sponsored by the Agency for Healthcare Research and Quality, found that an estimated 6.9% of adults—17.8 million—had ever had long COVID as of early 2023.

This nationally representative survey included a sample of 17 418 adults aged 18 years or older, which corresponds to 259 million adults. A total of 8275 adults reported having had COVID-19, of which 1202 adults reported having had long COVID symptoms.

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Javier Sánchez-Cañizares

Adaptive Behavior

Many theoretical studies defend the existence of ongoing phase transitions in the brain dynamics that could explain its enormous plasticity to cope with the environment. However, tackling the ever-changing landscapes of brain dynamics seems a hopeless task with complex models. This paper uses a simple Haken-Kelso-Bunz (HKB) model to illustrate how phase transitions that change the number of attractors in the landscape for the relative phase between two neural assemblies can occur, helping to explain a qualitative agreement with empirical decision-making measures. Additionally, the paper discusses the possibility of interpreting this agreement with the aid of Irruption Theory (IT). Being the effect of symmetry breakings and the emergence of non-linearities in the fundamental equations, the order parameter governing phase transitions may not have a complete microscopic determination. Hence, many requirements of IT, particularly the Participation Criterion, could be fulfilled by the HKB model and its extensions. Briefly stated, triggering phase transitions in the brain activity could thus be conceived of as a consequence of actual motivations or free will participating in decision-making processes.

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