Quantifying the Role of Homophily in Human Cooperation Using Multiplex Evolutionary Game Theory

Beyond collective intelligence: Collective adaptation

We develop a conceptual framework for studying collective adaptation in complex socio-cognitive systems, driven by dynamic interactions of social integration strategies, social environments and problem structures. Going beyond searching for 'intelligent' collectives, we integrate research from different disciplines and outline modelling approaches that can be used to begin answering questions such as why collectives sometimes fail to reach seemingly obvious solutions, how they change their strategies and network structures in response to different problems and how we can anticipate and perhaps change future harmful societal trajectories. We discuss the importance of considering path dependence, lack of optimization and collective myopia to understand the sometimes counterintuitive outcomes of collective adaptation. We call for a transdisciplinary, quantitative and societally useful social science that can help us to understand our rapidly changing and ever more complex societies, avoid collective disasters and reach the full potential of our ability to organize in adaptive collectives.

Multiple social network influences can generate unexpected environmental outcomes

Understanding the function of social networks can make a critical contribution to achieving desirable environmental outcomes. Social-ecological systems are complex, adaptive systems in which environmental decision makers adapt to a changing social and ecological context. However, it remains unclear how multiple social influences interact with environmental feedbacks to generate environmental outcomes. Based on national-scale survey data and a social-ecological agent-based model in the context of voluntary private land conservation, our results suggest that social influences can operate synergistically or antagonistically, thereby enabling behaviors to spread by two or more mechanisms that amplify each other's effects. Furthermore, information through social networks may indirectly affect and respond to isolated individuals through environmental change. The interplay of social influences can, therefore, explain the success or failure of conservation outcomes emerging from collective behavior. To understand the capacity of social influence to generate environmental outcomes, social networks must not be seen as 'closed systems'; rather, the outcomes of environmental interventions depend on feedbacks between the environment and different components of the social system.

A novel approach for the design of context-aware services for social inclusion and education

BACKGROUND: The new generation networks (5G and beyond) will allow us to collect and process real-time information about a user and his context. Analyzing the adolescents’ behaviour and taking into account relations between their psychological frailty and socio-cultural context, it is possible to highlight situations of vulnerability. OBJECTIVE: It is crucial to shed light on how the nature of social relationships and the similarity among individuals play a role in the collective dynamics. METHODS: To understand these dynamics, Evolutionary Game Theory and the analysis of social networks, modeled as multiplex networks, are useful. RESULTS: Thanks to a simulative approach we evaluate the emergence and maintenance of cooperation within a class, assessing the role of social network structure and of the homophily on the dynamics. CONCLUSION: Exploiting these tools it is possible to design innovative ICT context-aware services based on collective cooperation and aimed at improving social inclusion, education and support for frail people.

The Dynamical Interplay of Collective Attention, Awareness and Epidemics Spreading in the Multiplex Social Networks During COVID-19

Leveraging social and communication technologies, we can digitally observe that the collective attention typically exhibits a heterogeneous structure. It shows that people's interests are organized in clusters around different topics, but the rising of an extraordinary emergency event, as the coronavirus disease epidemics, channels the people's attention into a more homogenized structure, shifting it as triggered by a non-random collective process. The connectedness of networked individuals, on multiple social levels, impacts on the attention, representing a tuning element of different behavioural outcomes, changing the awareness diffusion enough to produce effects on epidemics spreading. We propose a mathematical framework to model the interplay between the collective attention and the co-evolving processes of awareness diffusion, modelled as a social contagion phenomenon, and epidemic spreading on weighted multiplex networks. Our proposed modeling approach structures a systematically understanding as a social network marker of interdependent collective dynamics through the introduction of the multiplex dimension of both networked individuals and topics, quantifying the role of human-related factors, as homophily, network properties, and heterogeneity. We introduce a data-driven approach by integrating different types of data, digitally traced as user-generated data from Twitter and Google Trends, in response to an extraordinary emergency event as coronavirus disease. Our findings demonstrate how the proposed model allows us to quantify the reaction of the collective attention, proving that it can represent a social predictive marker of the awareness dynamics, unveiling the impact on epidemic spreading, for a timely crisis response planning. Simulations results shed light on the coherence between the data-driven approach and the proposed analytical model.

Collectivism-individualism: Strategic behavior in tacit coordination games

The effect of culture on strategic interaction has been widely explored. However, the effect of the cultural background on focal point selection in tacit coordination games has not yet been examined. To accomplish this goal, in this study we have focused on the individual level of analysis. That is, we constructed a strategic profile to model the behavior of each individual player and then used unsupervised learning methods on the individual data points. We have chosen to examine two groups of participants, Israelis (ICB) and Chinese (CCB), each belonging to a different cultural background representing individualist and collectivist societies, respectively. Clustering the individual strategic profiles has allowed us to gain further insights regarding the differences between the behavioral strategies of each cultural group. The results of this study demonstrate that the cultural background has a profound effect on the strategic profile and on the ability to succeed in tacit coordination games. Moreover, the current study emphasizes the importance of relying on the individual level of analysis and not only on the group level of analysis. The implications of these results and potential future studies are discussed.

The dynamics of men's cooperation and social status in a small-scale society

We propose that networks of cooperation and allocation of social status co-emerge in human groups. We substantiate this hypothesis with one of the first longitudinal studies of cooperation in a preindustrial society, spanning 8 years. Using longitudinal social network analysis of cooperation among men, we find large effects of kinship, reciprocity and transitivity in the nomination of cooperation partners over time. Independent of these effects, we show that (i) higher-status individuals gain more cooperation partners, and (ii) individuals gain status by cooperating with individuals of higher status than themselves. We posit that human hierarchies are more egalitarian relative to other primates species, owing in part to greater interdependence between cooperation and status hierarchy.

Social dynamics modeling of chrono-nutrition

Gut microbiota and human relationships are strictly connected to each other. What we eat reflects our body-mind connection and synchronizes with people around us. However, how this impacts on gut microbiota and, conversely, how gut bacteria influence our dietary behaviors has not been explored yet. To quantify the complex dynamics of this interplay between gut and human behaviors we explore the "gut-human behavior axis" and its evolutionary dynamics in a real-world scenario represented by the social multiplex network. We consider a dual type of similarity, homophily and gut similarity, other than psychological and unconscious biases. We analyze the dynamics of social and gut microbial communities, quantifying the impact of human behaviors on diets and gut microbial composition and, backwards, through a control mechanism. Meal timing mechanisms and "chrono-nutrition" play a crucial role in feeding behaviors, along with the quality and quantity of food intake. Considering a population of shift workers, we explore the dynamic interplay between their eating behaviors and gut microbiota, modeling the social dynamics of chrono-nutrition in a multiplex network. Our findings allow us to quantify the relation between human behaviors and gut microbiota through the methodological introduction of gut metabolic modeling and statistical estimators, able to capture their dynamic interplay. Moreover, we find that the timing of gut microbial communities is slower than social interactions and shift-working, and the impact of shift-working on the dynamics of chrono-nutrition is a fluctuation of strategies with a major propensity for defection (e.g. high-fat meals). A deeper understanding of the relation between gut microbiota and the dietary behavioral patterns, by embedding also the related social aspects, allows improving the overall knowledge about metabolic models and their implications for human health, opening the possibility to design promising social therapeutic dietary interventions.

Quantifying the propagation of distress and mental disorders in social networks

Heterogeneity of human beings leads to think and react differently to social phenomena. Awareness and homophily drive people to weigh interactions in social multiplex networks, influencing a potential contagion effect. To quantify the impact of heterogeneity on spreading dynamics, we propose a model of coevolution of social contagion and awareness, through the introduction of statistical estimators, in a weighted multiplex network. Multiplexity of networked individuals may trigger propagation enough to produce effects among vulnerable subjects experiencing distress, mental disorder, which represent some of the strongest predictors of suicidal behaviours. The exposure to suicide is emotionally harmful, since talking about it may give support or inadvertently promote it. To disclose the complex effect of the overlapping awareness on suicidal ideation spreading among disordered people, we also introduce a data-driven approach by integrating different types of data. Our modelling approach unveils the relationship between distress and mental disorders propagation and suicidal ideation spreading, shedding light on the role of awareness in a social network for suicide prevention. The proposed model is able to quantify the impact of overlapping awareness on suicidal ideation spreading and our findings demonstrate that it plays a dual role on contagion, either reinforcing or delaying the contagion outbreak.

Opinion formation in multiplex networks with general initial distributions

We study opinion dynamics over multiplex networks where agents interact with bounded confidence. Namely, two neighbouring individuals exchange opinions and compromise if their opinions do not differ by more than a given threshold. In literature, agents are generally assumed to have a homogeneous confidence bound. Here, we study analytically and numerically opinion evolution over structured networks characterised by multiple layers with respective confidence thresholds and general initial opinion distributions. Through rigorous probability analysis, we show analytically the critical thresholds at which a phase transition takes place in the long-term consensus behaviour, over multiplex networks with some regularity conditions. Our results reveal the quantitative relation between the critical threshold and initial distribution. Further, our numerical simulations illustrate the consensus behaviour of the agents in network topologies including lattices and, small-world and scale-free networks, as well as for structure-dependent convergence parameters accommodating node heterogeneity. We find that the critical thresholds for consensus tend to agree with the predicted upper bounds in Theorems 4 and 5 in this paper. Finally, our results indicate that multiplexity hinders consensus formation when the initial opinion configuration is within a bounded range and, provide insight into information diffusion and social dynamics in multiplex systems modeled by networks.

Rumor Detection over Varying Time Windows

This study determines the major difference between rumors and non-rumors and explores rumor classification performance levels over varying time windows-from the first three days to nearly two months. A comprehensive set of user, structural, linguistic, and temporal features was examined and their relative strength was compared from near-complete date of Twitter. Our contribution is at providing deep insight into the cumulative spreading patterns of rumors over time as well as at tracking the precise changes in predictive powers across rumor features. Statistical analysis finds that structural and temporal features distinguish rumors from non-rumors over a long-term window, yet they are not available during the initial propagation phase. In contrast, user and linguistic features are readily available and act as a good indicator during the initial propagation phase. Based on these findings, we suggest a new rumor classification algorithm that achieves competitive accuracy over both short and long time windows. These findings provide new insights for explaining rumor mechanism theories and for identifying features of early rumor detection.

The Impact of Heterogeneity and Awareness in Modeling Epidemic Spreading on Multiplex Networks

In the real world, dynamic processes involving human beings are not disjoint. To capture the real complexity of such dynamics, we propose a novel model of the coevolution of epidemic and awareness spreading processes on a multiplex network, also introducing a preventive isolation strategy. Our aim is to evaluate and quantify the joint impact of heterogeneity and awareness, under different socioeconomic conditions. Considering, as case study, an emerging public health threat, Zika virus, we introduce a data-driven analysis by exploiting multiple sources and different types of data, ranging from Big Five personality traits to Google Trends, related to different world countries where there is an ongoing epidemic outbreak. Our findings demonstrate how the proposed model allows delaying the epidemic outbreak and increasing the resilience of nodes, especially under critical economic conditions. Simulation results, using data-driven approach on Zika virus, which has a growing scientific research interest, are coherent with the proposed analytic model.

Instrumenting Beliefs in Threshold Public Goods

Understanding the causal impact of beliefs on contributions in Threshold Public Goods (TPGs) is particularly important since the social optimum can be supported as a Nash Equilibrium and best-response contributions are a function of beliefs. Unfortunately, investigations of the impact of beliefs on behavior are plagued with endogeneity concerns. We create a set of instruments by cleanly and exogenously manipulating beliefs without deception. Tests indicate that the instruments are valid and relevant. Perhaps surprisingly, we fail to find evidence that beliefs are endogenous in either the one-shot or repeated-decision settings. TPG allocations are determined by a base contribution and beliefs in a one shot-setting. In the repeated-decision environment, once we instrument for first-round allocations, we find that second-round allocations are driven equally by beliefs and history. Moreover, we find that failing to instrument prior decisions overstates their importance.