FRANK SCHWEITZER, GEORGES ANDRES, GIONA CASIRAGHI, CHRISTOPH GOTE, RAMONA ROLLER, INGO SCHOLTES, GIACOMO VACCARIO, and CHRISTIAN ZINGG

Advances in Complex SystemsVol. 25, No. 08, 2250014

Resilience denotes the capacity of a system to withstand shocks and its ability to recover from them. We develop a framework to quantify the resilience of highly volatile, non-equilibrium social organizations, such as collectives or collaborating teams. It consists of four steps: (i) delimitation, i.e. narrowing down the target systems, (ii) conceptualization, i.e. identifying how to approach social organizations, (iii) formal representation using a combination of agent-based and network models, (iv) operationalization, i.e. specifying measures and demonstrating how they enter the calculation of resilience. Our framework quantifies two dimensions of resilience, the robustness of social organizations and their adaptivity, and combines them in a novel resilience measure. It allows monitoring resilience instantaneously using longitudinal data instead of an ex-post evaluation.

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Jordi Piñero, Ricard Solé, Artemy Kolchinsky

Many molecular systems operate by harvesting and storing energy from their environments. However, the maintenance of a nonequilibrium state necessary to support energy harvesting itself carries thermodynamic costs. We consider the optimal tradeoff between costs and benefits of energy harvesting in a nonequilibrium steady state, for a system that may be in contact with a fluctuating environment. We find a universal bound on this tradeoff, which leads to closed-form expressions for optimal power output and optimal steady-state distributions for three physically meaningful regimes. Our results are illustrated using a model of a unicyclic network, which is inspired by the logic of biomolecular cycles.

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Klaus Jaffe

Classical laws of thermodynamics apply only for reversible processes. Most processes however are irreversible and occur in open systems. That is the case of synergy that emerges from synchronized reciprocal positive feedback loops between a network of diverse actors. For this process to proceed, compatible information from different sources synchronically coordinates the actions of the actors resulting in a nonlinear increase in the useful work or potential energy the system can manage. In contrast noise is produced when incompatible information is mixed. This synergy produced from the coordination of different agents achieves non-linear gains in free energy and in information (negentropy). Free energy can be estimated by proxies such as individual autonomy of an organism, emancipation from the environment, productivity, efficiency, capacity for flexibility, self-regulation, and self-control of behavior; whereas entropy, or the lack of it, is revealed by the degree of synchronized division of ever more specialized labor, structural complexity, information, and dissipation of energy. Empirical examples that provide quantitative data for these phenomena are presented. Results show that increases in free energy density are concomitant with decreases in entropy density. This may be a rule for synergistic processes in irreversible thermodynamics, which is consilient with the first and second laws of classical thermodynamics. Under this light, biological evolution is the task of self reproducing irreversible synergistic system to discover empirically (through natural and sexual selection) types of order that increase their free energy.

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Oscar Fajardo-Fontiveros, Ignasi Reichardt, Harry R. De Los Ríos, Jordi Duch, Marta Sales-Pardo & Roger Guimerà 

Nature Communications volume 14, Article number: 1043

Given a finite and noisy dataset generated with a closed-form mathematical model, when is it possible to learn the true generating model from the data alone? This is the question we investigate here. We show that this model-learning problem displays a transition from a low-noise phase in which the true model can be learned, to a phase in which the observation noise is too high for the true model to be learned by any method. Both in the low-noise phase and in the high-noise phase, probabilistic model selection leads to optimal generalization to unseen data. This is in contrast to standard machine learning approaches, including artificial neural networks, which in this particular problem are limited, in the low-noise phase, by their ability to interpolate. In the transition region between the learnable and unlearnable phases, generalization is hard for all approaches including probabilistic model selection.

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Gerardo Iñiguez, Sara Heydari, János Kertész, Jari Saramäki
Tie strengths in social networks are heterogeneous, with strong and weak ties playing different roles at both the network and the individual level. Egocentric networks, networks of relationships around a focal individual, exhibit a small number of strong ties and a larger number of weaker ties, a pattern that is evident in electronic communication records, such as mobile phone calls. Mobile phone data has also revealed persistent individual differences within this pattern. However, the generality and the driving mechanisms of this tie strength heterogeneity remain unclear. Here, we study tie strengths in egocentric networks across multiple datasets containing records of interactions between millions of people over time periods ranging from months to years. Our findings reveal a remarkable universality in the distribution of tie strengths and their individual-level variation across different modes of communication, even in channels that may not reflect offline social relationships. With the help of an analytically tractable model of egocentric network evolution, we show that the observed universality can be attributed to the competition between cumulative advantage and random choice, two general mechanisms of tie reinforcement whose balance determines the amount of heterogeneity in tie strengths. Our results provide new insights into the driving mechanisms of tie strength heterogeneity in social networks and have implications for the understanding of social network structure and individual behavior.

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