Evolution of Cooperation in Social Dilemmas on Complex Networks

Standardized local assortativity in networks and systemic risk in financial markets

The study of assortativity allows us to understand the heterogeneity of networks and the implication of network resilience. While a global measure has been predominantly used to characterize this network feature, there has been little research to suggest a local coefficient to account for the presence of local (dis)assortative patterns in diversely mixed networks. We build on existing literature and extend the concept of assortativity with the proposal of a standardized scale-independent local coefficient to observe the assortative characteristics of each entity in networks that would otherwise be smoothed out with a global measure. This coefficient provides a lens through which the granular level of details can be observed, as well as capturing possible pattern (dis)formation in dynamic networks. We demonstrate how the standardized local assortative coefficient discovers the presence of (dis)assortative hubs in static networks on a granular level, and how it tracks systemic risk in dynamic financial networks.

Oxytocin and the Punitive Hub-Dynamic Spread of Cooperation in Human Social Networks

Human society operates on large-scale cooperation. However, individual differences in cooperativeness and incentives to free ride on others' cooperation make large-scale cooperation fragile and can lead to reduced social welfare. Thus, how individual cooperation spreads through human social networks remains puzzling from ecological, evolutionary, and societal perspectives. Here, we identify oxytocin and costly punishment as biobehavioral mechanisms that facilitate the propagation of cooperation in social networks. In three laboratory experiments (n = 870 human participants: 373 males, 497 females), individuals were embedded in heterogeneous networks and made repeated decisions with feedback in games of trust (n = 342), ultimatum bargaining (n = 324), and prisoner's dilemma with punishment (n = 204). In each heterogeneous network, individuals at central positions (hub nodes) were given intranasal oxytocin (or placebo). Giving oxytocin (vs matching placebo) to central individuals increased their trust and enforcement of cooperation norms. Oxytocin-enhanced norm enforcement, but not elevated trust, explained the spreading of cooperation throughout the social network. Moreover, grounded in evolutionary game theory, we simulated computer agents that interacted in heterogeneous networks with central nodes varying in terms of cooperation and punishment levels. Simulation results confirmed that central cooperators' willingness to punish noncooperation allowed the permeation of the network and enabled the evolution of network cooperation. These results identify an oxytocin-initiated proximate mechanism explaining how individual cooperation facilitates network-wide cooperation in human society and shed light on the widespread phenomenon of heterogeneous composition and enforcement systems at all levels of life.SIGNIFICANCE STATEMENT Human society operates on large-scale cooperation. Yet because cooperation is exploitable by free riding, how cooperation in social networks emerges remains puzzling from evolutionary and societal perspectives. Here we identify oxytocin and altruistic punishment as key factors facilitating the propagation of cooperation in human social networks. Individuals played repeated economic games in heterogeneous networks where individuals at central positions were given oxytocin or placebo. Oxytocin-enhanced cooperative norm enforcement, but not elevated trust, explained cooperation spreading throughout the social network. Evolutionary simulations confirmed that central cooperators' willingness to punish noncooperation allowed the permeation of the network and enabled the evolution of cooperation. These results identify an oxytocin-initiated proximate mechanism explaining how individual cooperation facilitates network-wide cooperation in human social networks.

Cognitive Profiling of Nodes in 6G through Multiplex Social Network and Evolutionary Collective Dynamics

Complex systems are fully described by the connectedness of their elements studying how these develop a collective behavior, interacting with each other following their inner features, and the structure and dynamics of the entire system. The forthcoming 6G will attempt to rewrite the communication networks’ perspective, focusing on a radical revolution in the way entities and technologies are conceived, integrated and used. This will lead to innovative approaches with the aim of providing new directions to deal with future network challenges posed by the upcoming 6G, thus the complex systems could become an enabling set of tools and methods to design a self-organized, resilient and cognitive network, suitable for many application fields, such as digital health or smart city living scenarios. Here, we propose a complex profiling approach of heterogeneous nodes belonging to the network with the goal of including the multiplex social network as a mathematical representation that enables us to consider multiple types of interactions, the collective dynamics of diffusion and competition, through social contagion and evolutionary game theory, and the mesoscale organization in communities to drive learning and cognition. Through a framework, we detail the step by step modeling approach and show and discuss our findings, applying it to a real dataset, by demonstrating how the proposed model allows us to detect deeply complex knowable roles of nodes.

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.

Evolution of Cooperation in Social Dilemmas with Assortative Interactions

Cooperation in social dilemmas plays a pivotal role in the formation of systems at all levels of complexity, from replicating molecules to multi-cellular organisms to human and animal societies. In spite of its ubiquity, the origin and stability of cooperation pose an evolutionary conundrum, since cooperation, though beneficial to others, is costly to the individual cooperator. Thus natural selection would be expected to favor selfish behavior in which individuals reap the benefits of cooperation without bearing the costs of cooperating themselves. Many proximate mechanisms have been proposed to account for the origin and maintenance of cooperation, including kin selection, direct reciprocity, indirect reciprocity, and evolution in structured populations. Despite the apparent diversity of these approaches they all share a unified underlying logic: namely, each mechanism results in assortative interactions in which individuals using the same strategy interact with a higher probability than they would at random. Here we study the evolution of cooperation in both discrete strategy and continuous strategy social dilemmas with assortative interactions. For the sake of tractability, assortativity is modeled by an individual interacting with another of the same type with probability r and interacting with a random individual in the population with probability 1−r, where r is a parameter that characterizes the degree of assortativity in the system. For discrete strategy social dilemmas we use both a generalization of replicator dynamics and individual-based simulations to elucidate the donation, snowdrift, and sculling games with assortative interactions, and determine the analogs of Hamilton’s rule, which govern the evolution of cooperation in these games. For continuous strategy social dilemmas we employ both a generalization of deterministic adaptive dynamics and individual-based simulations to study the donation, snowdrift, and tragedy of the commons games, and determine the effect of assortativity on the emergence and stability of cooperation.

Evolving cooperation in multichannel games

Humans routinely engage in many distinct interactions in parallel. Team members collaborate on several concurrent projects, and even whole nations interact with each other across a variety of issues, including trade, climate change and security. Yet the existing theory of direct reciprocity studies isolated repeated games. Such models cannot account for strategic attempts to use the vested interests in one game as a leverage to enforce cooperation in another. Here we introduce a general framework of multichannel games. Individuals interact with each other over multiple channels; each channel is a repeated game. Strategic choices in one channel can affect decisions in another. With analytical equilibrium calculations for the donation game and evolutionary simulations for several other games we show that such linkage facilitates cooperation. Our results suggest that previous studies tend to underestimate the human potential for reciprocity. When several interactions occur in parallel, people often learn to coordinate their behavior across games to maximize cooperation in each of them.

Coevolution of public goods game and networks based on survival of the fittest

We introduce a random strategy update rule for the evolutionary public goods game on networks based on survival of the fittest. A survival cost parameter is introduced to public goods game. Players whose payoffs are below the survival cost will be deleted from the network. The same number of new nodes are randomly connected to the network and randomly designated cooperation or defection. Numerical results show that cooperation can flourish if the multiplication factor of the public goods game is greater than the network degree. We present a simple analytical method to explain this result. The fraction of cooperators reaches the maximum for a suitable survival cost. Furthermore, the initial random network has evolved into a heterogeneous network which facilitates the emergence of the cooperation. Our work could be helpful to understand how natural selection favors cooperation. It suggests a new method to investigate the impact of the survival cost on the evolution of cooperation.

Evolution of cooperative imitators in social networks

Many evolutionary game models for network reciprocity are based on an imitation dynamics, yet how semirational imitators prevail has seldom been explained. Here we use a model to investigate the coevolutionary dynamics of cooperation and partnership adjustment in a polygenic population of semirational imitators and rational payoff maximizers. A rational individual chooses a strategy best responding to its neighbors when updating strategy and switches to a new partner who can bring it the maximal payoff from all candidates when adjusting the partnership. In contrast, a semirational individual imitates its neighbor's strategy directly and adjusts its partnership based upon a simple reputation rule. Individual-based simulations show that cooperation cannot evolve in a population of all best responders even if they can switch their partners to somebody who can reward them best in game playing. However, when imitators exist, a stable community that consists of cooperative imitators emerges. Further, we show that a birth-death selection mechanism can eliminate all best responders, cultivating a social regime of all cooperative imitators. Compared with parallel simulations that assume fixed networks, cooperative imitators are evolutionarily favored, provided they are able to adjust their partners.