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Abstract: Inter-city interactions are critical for the transmission of infectious diseases, yet their effects on the scaling of disease cases remain largely underexplored. Here, we use the commuting network as a proxy for inter-city interactions, integrating it with a general scaling framework to describe the incidence of seven infectious diseases across Brazilian cities as a function of population size and the number of commuters. Our models significantly outperform traditional urban scaling approaches, revealing that the relationship between disease cases and a combination of population and commuters varies across diseases and is influenced by both factors. Although most cities exhibit a less-than-proportional increase in disease cases with changes in population and commuters, more-than-proportional responses are also observed across all diseases. Notably, in some small and isolated cities, proportional rises in population and commuters correlate with a reduction in disease cases. These findings suggest that such towns may experience improved health outcomes and socioeconomic conditions as they grow and become more connected. However, as growth and connectivity continue, these gains diminish, eventually giving way to challenges typical of larger urban areas - such as socioeconomic inequality and overcrowding - that facilitate the spread of infectious diseases. Our study underscores the interconnected roles of population size and commuter dynamics in disease incidence while highlighting that changes in population size exert a greater influence on disease cases than variations in the number of commuters.
Keywords: complex networks, statistical physics, interactions between cities, disease scaling, social physics
Published in DKUM: 09.01.2025; Views: 0; Downloads: 2
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Abstract: Research on smoking behaviour has primarily focused on adolescents, with less attention given to middle-aged and older adults in rural settings. This study examines the influence of personal networks and sociodemographic factors on smoking behaviour in a rural Romanian community. We analysed data from 76 participants, collected through face-to-face interviews, including smoking status (non-smokers, current and former smokers), social ties and demographic details. Multilevel regression models were used to predict smoking status. The results indicate that social networks are essential in shaping smoking habits. Current smokers were more likely to have smoking family members, reinforcing smoking within familial networks, while non-smokers were typically embedded in non-smoking environments. Gender and age patterns show that women were less likely to smoke, and older adults were more likely to have quit smoking. These findings suggest that targeted interventions should focus not only on individuals but also on their social networks. In rural areas, family-based approaches may be particularly effective due to the strong influence of familial ties. Additionally, encouraging connections with non-smokers and former smokers could help disrupt smoking clusters, supporting smoking cessation efforts.
Keywords: network science, human behaviour, data science, smoking, social physics
Published in DKUM: 03.12.2024; Views: 0; Downloads: 0
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Abstract: Understanding human behaviour in decision problems and strategicinteractions has wide-ranging applications in economics, psychology andartificial intelligence. Game theory offers a robust foundation for this under-standing, based on the idea that individuals aim to maximize a utilityfunction. However, the exact factors influencing strategy choices remainelusive. While traditional models try to explain human behaviour as a func-tion of the outcomes of available actions, recent experimental researchreveals that linguistic content significantly impacts decision-making, thusprompting a paradigm shift from outcome-based to language-based utilityfunctions. This shift is more urgent than ever, given the advancement ofgenerative AI, which has the potential to support humans in making criticaldecisions through language-based interactions. We propose sentiment analy-sis as a fundamental tool for this shift and take an initial step by analysing61 experimental instructions from the dictator game, an economic gamecapturing the balance between self-interest and the interest of others,which is at the core of many social interactions. Our meta-analysis showsthat sentiment analysis can explain human behaviour beyond economicoutcomes. We discuss future research directions. We hope this worksets the stage for a novel game-theoretical approach that emphasizes theimportance of language in human decisions.
Keywords: game theory, artificial intelligence, social preferences, language-based preferences, social physics, moral behavior, trust
Published in DKUM: 27.11.2024; Views: 0; Downloads: 6
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Abstract: Evolutionary games provide the theoretical backbone for many aspects of our social life: from cooperation to crime, from climate inaction to imperfect vaccination and epidemic spreading, from antibiotics overuse to biodiversity preservation. An important, and so far overlooked, aspect of reality is the diverse strategic identities of individuals. While applying the same strategy to all interaction partners may be an acceptable assumption for simpler forms of life, this fails to account for the behavior of more complex living beings. For instance, we humans act differently around different people. Here we show that allowing individuals to adopt different strategies with different partners yields a very rich evolutionary dynamics, including time-dependent coexistence of cooperation and defection, systemwide shifts in the dominant strategy, and maturation in individual choices. Our results are robust to variations in network type and size, and strategy updating rules. Accounting for diverse strategic identities thus has far-reaching implications in the mathematical modeling of social games.
Keywords: cooperation, evolutionary game theory, social physics, collective dynamics, complex system
Published in DKUM: 20.11.2024; Views: 0; Downloads: 3
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Abstract: Collaboration patterns offer important insights into how scientific breakthroughs and innovations emerge in small and large research groups. However, links in traditional networks account only for pairwise interactions, thus making the framework best suited for the description of two-person collaborations, but not for collaborations in larger groups. We therefore study higher-order scientific collaboration networks where a single link can connect more than two individuals, which is a natural description of collaborations entailing three or more people. We also consider different layers of these networks depending on the total number of collaborators, from one upwards. By doing so, we obtain novel microscopic insights into the representativeness of researchers within different teams and their links with others. In particular, we can follow the maturation process of the main topological features of collaboration networks, as we consider the sequence of graphs obtained by progressively merging collaborations from smaller to bigger sizes starting from the single-author ones. We also perform the same analysis by using publications instead of researchers as network nodes, obtaining qualitatively the same insights and thus confirming their robustness. We use data from the arXiv to obtain results specific to the fields of physics, mathematics, and computer science, as well as to the entire coverage of research fields in the database.
Keywords: network, collaboration, social physics, complex system
Published in DKUM: 18.10.2024; Views: 0; Downloads: 1
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