Carlos M. Garrido, Francisco C. Santos, Elias Fernández Domingos, Ana M. Nunes & Jorge M. Pacheco 
Scientific Reports volume 15, Article number: 3865 (2025)

The sustainable governance of Global Risky Commons (GRC)—global commons in the presence of a sizable risk of overall failure—is ubiquitous and requires a global solution. A prominent example is the mitigation of the adverse effects of global warming. In this context, the Collective Risk Dilemma (CRD) provides a convenient baseline model which captures many important features associated with GRC type problems by formulating them as problems of cooperation. Here we make use of the CRD to develop, for the first time, a bottom-up institutional governance framework of GRC. We find that the endogenous creation of local institutions that require a minimum consensus amongst group members—who, in turn, decide the nature of the institution (reward/punishment) via an electoral process—leads to higher overall cooperation than previously proposed designs, especially at low risk, proving that carrots and sticks implemented through local voting processes are more powerful than other designs. The stochastic evolutionary game theoretical model framework developed here further allows us to directly compare our results with those stemming from previous models of institutional governance. The model and the methods employed here are relevant and general enough to be applied to a variety of contemporary interdisciplinary problems.

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Oriol Cabanas-Tirapu, Lluís Danús, Esteban Moro, Marta Sales-Pardo & Roger Guimerà 

Nature Communications volume 16, Article number: 1336 (2025)

Modeling human mobility is critical to address questions in urban planning, sustainability, public health, and economic development. However, our understanding and ability to model flows between urban areas are still incomplete. At one end of the modeling spectrum we have gravity models, which are easy to interpret but provide modestly accurate predictions of flows. At the other end, we have machine learning models, with tens of features and thousands of parameters, which predict mobility more accurately than gravity models but do not provide clear insights on human behavior. Here, we show that simple machine-learned, closed-form models of mobility can predict mobility flows as accurately as complex machine learning models, and extrapolate better. Moreover, these models are simple and gravity-like, and can be interpreted similarly to standard gravity models. These models work for different datasets and at different scales, suggesting that they may capture the fundamental universal features of human mobility.

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INBAL ARNON, SIMON KIRBY, JENNY A. ALLEN, CLAIRE GARRIGUE, EMMA L. CARROLL, AND ELLEN C. GARLAND
SCIENCE 6 Feb 2025 Vol 387, Issue 6734 pp. 649-653

Humpback whale song is a culturally transmitted behavior. Human language, which is also culturally transmitted, has statistically coherent parts whose frequency distribution follows a power law. These properties facilitate learning and may therefore arise because of their contribution to the faithful transmission of language over multiple cultural generations. If so, we would expect to find them in other culturally transmitted systems. In this study, we applied methods based on infant speech segmentation to 8 years of humpback recordings, uncovering in whale song the same statistical structure that is a hallmark of human language. This commonality, in two evolutionarily distant species, points to the role of learning and cultural transmission in the emergence of properties thought to be unique to human language.

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Abbas Shoja-Daliklidash, Morteza Nattagh-Najafi, Nasser Sepehri-Javan

In this paper, we address a longstanding challenge in self-organized criticality (SOC) systems: establishing a connection between sandpiles and complex networks. Our approach employs a similarity-based transfer function characterized by two parameters, =(r1,r2). Here, r1 quantifies the similarity of local activities, while r2 governs the filtration process used to convert a weighted network into a binary one. We reveal that the degree centrality distribution of the resulting network follows a generalized Gamma distribution (GGD), which transitions to a power-law distribution under specific conditions. The GGD exponents, estimated numerically, exhibit a dependency on . Notably, while both decreasing r1 and r2 lead to denser networks, r2 plays a more significant role in influencing network density. Furthermore, the Shannon entropy is observed to decrease linearly with increasing r2, whereas its variation with r1 is more gradual. An analytical expression for the Shannon entropy is proposed. To characterize the network structure, we investigate the clustering coefficient (cc), eigenvalue centrality (e), closeness centrality (c), and betweenness centrality (b). The distributions of cc, e, and c exhibit peaked profiles, while b displays a power-law distribution over a finite interval of k. Additionally, we explore correlations between the exponents and identify a specific parameter regime of  and k where the e−k, c−k, and b−k correlations become negative.

Read the full article at: arxiv.org