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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Rafael Prieto-Curiel, Jorge E. Patino, and Brilé Anderson

120 (9) e2214254120

The emptiness, elongation, and sprawl of a city have lasting implications for cities’ future energy needs. This paper creates a publicly available set of urban form indicators and estimates intercity distances. It uses footprint data of millions of buildings in Africa as well as the boundaries of urban agglomerations, street network data, and terrain metrics to detect different extension patterns in almost six thousand cities. These methods estimate the increasingly longer commutes in urban areas and the energy needed to move millions of people. Designing compact, dense, and better-connected urban forms will help cities be more sustainable and liveable.

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Wolfgang Tschacher and Hermann Haken

Entropy 2023, 25(2), 341

A common assumption of psychological theories of humor is that experienced funniness results from an incongruity between stimuli provided by a verbal joke or visual pun, followed by a sudden, surprising resolution of incongruity. In the perspective of complexity science, this characteristic incongruity-resolution sequence is modeled by a phase transition, where an initial attractor-like script, suggested by the initial joke information, is suddenly destructed, and in the course of resolution replaced by a less probable novel script. The transition from the initial to the enforced final script was modeled as a succession of two attractors with different minimum potentials, during which free energy becomes available to the joke recipient. Hypotheses derived from the model were tested in an empirical study where participants rated the funniness of visual puns. It was found, consistent with the model, that the extent of incongruity and the abruptness of resolution were associated with reported funniness, and with social factors, such as disparagement (Schadenfreude) added to humor responses. The model suggests explanations as to why bistable puns and phase transitions in conventional problem solving, albeit also based on phase transitions, are generally less funny. We proposed that findings from the model can be transferred to decision processes and mental change dynamics in psychotherapy.

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