Spillover modes in multiplex games: double-edged effects on cooperation, and their coevolution

In recent years, there has been growing interest in studying games on multiplex networks that account for interactions across linked social contexts. However, little is known about how potential cross-context interference, or spillover, of individual behavioural strategy impact overall cooperation. We consider three plausible spillover modes, quantifying and comparing their effects on the evolution of cooperation. In our model, social interactions take place on two network layers: one represents repeated interactions with close neighbours in a lattice, the other represents one-shot interactions with random individuals across the same population. Spillover can occur during the social learning process with accidental cross-layer strategy transfer, or during social interactions with errors in implementation due to contextual interference. Our analytical results, using extended pair approximation, are in good agreement with extensive simulations. We find double-edged effects of spillover on cooperation: increasing the intensity of spillover can promote cooperation provided cooperation is favoured in one layer, but too much spillover is detrimental. We also discover a bistability phenomenon of cooperation: spillover hinders or promotes cooperation depending on initial frequencies of cooperation in each layer. Furthermore, comparing strategy combinations that emerge in each spillover mode provides a good indication of their co-evolutionary dynamics with cooperation. Our results make testable predictions that inspire future research, and sheds light on human cooperation across social domains and their interference with one another

Topological enslavement in evolutionary games on correlated multiplex networks

Governments and enterprises strongly rely on incentives to generate favorable outcomes from social and strategic interactions between individuals. The incentives are usually modeled by payoffs in evolutionary games, such as the prisoner's dilemma or the harmony game, with imitation dynamics. Adjusting the incentives by changing the payoff parameters can favor cooperation, as found in the harmony game, over defection, which prevails in the prisoner's dilemma. Here, we show that this is not always the case if individuals engage in strategic interactions in multiple domains. In particular, we investigate evolutionary games on multiplex networks where individuals obtain an aggregate payoff. We explicitly control the strength of degree correlations between nodes in the different layers of the multiplex. We find that if the multiplex is composed of many layers and degree correlations are strong, the topology of the system enslaves the dynamics and the final outcome, cooperation or defection, becomes independent of the payoff parameters. The fate of the system is then determined by the initial conditions

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.This work was partially supported by the following Research Grant: Italian Ministry of University and Research - MIUR “Programma Operativo Nazionale Ricerca e Competitività 2007–2013” within the project “PON-03PE-00132-1” - Servify

Evolutionary games on multilayer networks: coordination and equilibrium selection

We study mechanisms of synchronisation, coordination, and equilibrium selection in two-player coordination games on multilayer networks. We apply the approach from evolutionary game theory with three possible update rules: the replicator dynamics (RD), the best response (BR), and the unconditional imitation (UI). Players interact on a two-layer random regular network. The population on each layer plays a different game, with layer I preferring the opposite strategy to layer II. We measure the difference between the two games played on the layers by a difference in payoffs $\Delta S$ while the inter-connectedness is measured by a node overlap parameter $q$. We discover a critical value $q_c(\Delta S)$ below which layers do not synchronise. For $q>q_c$ in general both layers coordinate on the same strategy. Surprisingly, there is a symmetry breaking in the selection of equilibrium -- for RD and UI there is a phase where only the payoff-dominant equilibrium is selected. Our work is an example of previously observed differences between the update rules on a single network. However, we took a novel approach with the game being played on two inter-connected layers. As we show, the multilayer structure enhances the abundance of the Pareto-optimal equilibrium in coordination games with imitative update rules

Study the Effects of Multilevel Selection in Multi-Population Cultural Algorithm

This is a study on the effects of multilevel selection (MLS) theory in optimizing numerical functions. Based on this theory, a new architecture for Multi-Population Cultural Algorithm is proposed which incorporates a new multilevel selection framework (ML-MPCA). The approach used in this paper is based on biological group selection theory that states natural selection acts collectively on all the members of a given group. The effects of cooperation are studied using n-player prisoner’s dilemma. In this game, N individuals are randomly divided into m groups and individuals independently choose to be either cooperator or defector. A two-level selection process is introduced namely within group selection and between group selection. Individuals interact with the other members of the group in an evolutionary game that determines their fitness. The principal idea behind incorporating this multilevel selection model is to avoid premature convergence and to escape from local optima and for better exploration of the search space. We test our algorithm using the CEC 2015 expensive benchmark functions to evaluate its performance. These problems are a set of 15 functions which includes varied function categories. We show that our proposed algorithm improves solution accuracy and consistency. For 10 dimensional problems, the proposed method has 8 out 15 better results and for 30-dimensional problems we have 11 out of 15 better results when compared to the existing algorithms. The proposed model can be extended to more than two levels of selection and can also include migration

Evolution of cooperation in synergistically evolving dynamic interdependent networks: Fundamental advantages of coordinated network evolution

© 2019 The Author(s). Published by IOP Publishing Ltd on behalf of the Institute of Physics and Deutsche Physikalische Gesellschaft. Real networks are not only multi-layered yet also dynamic. The role of coordinated network evolution regarding dynamic multi-layer networks where both network and strategy evolution simultaneously show diverse interdependence by layers remains poorly addressed. Here, we propose a general and simple coevolution framework to analyze how coordination of different dynamical processes affects strategy propagation in synergistically evolving interdependent networks. The strategic feedback constitutes the main driving force of network evolution yet the inherent cross-layer self-optimization functions as its compensation. We show that these two ingredients often catalyze a better performance of network evolution in propagating cooperation. Coordinated network evolution may be a double-edged sword to cooperation and the network-Adapting rate plays a crucial role in flipping its double-sided effect. It often economizes the cost and time consumption for driving the system to the full cooperation phase. Importantly, strongly coupled slow-Tuned networks can outperform weakly coupled fast-regulated networks in solving social dilemmas, highlighting the fundamental advantages of coordinated network evolution and the importance of synergistic effect of dynamical processes in upholding human cooperation in multiplex networks

The structure and dynamics of multilayer networks

In the past years, network theory has successfully characterized the interaction among the constituents of a variety of complex systems, ranging from biological to technological, and social systems. However, up until recently, attention was almost exclusively given to networks in which all components were treated on equivalent footing, while neglecting all the extra information about the temporal- or context-related properties of the interactions under study. Only in the last years, taking advantage of the enhanced resolution in real data sets, network scientists have directed their interest to the multiplex character of real-world systems, and explicitly considered the time-varying and multilayer nature of networks. We offer here a comprehensive review on both structural and dynamical organization of graphs made of diverse relationships (layers) between its constituents, and cover several relevant issues, from a full redefinition of the basic structural measures, to understanding how the multilayer nature of the network affects processes and dynamics.Comment: In Press, Accepted Manuscript, Physics Reports 201

Networks of reliable reputations and cooperation: a review

Reputation has been shown to provide an informal solution to the problem of cooperation in human societies. After reviewing models that connect reputations and cooperation, we address how reputation results from information exchange embedded in a social network that changes endogenously itself. Theoretical studies highlight that network topologies have different effects on the extent of cooperation, since they can foster or hinder the flow of reputational information. Subsequently, we review models and empirical studies that intend to grasp the coevolution of reputations, cooperation and social networks. We identify open questions in the literature concerning how networks affect the accuracy of reputations, the honesty of shared information and the spread of reputational information. Certain network topologies may facilitate biased beliefs and intergroup competition or in-group identity formation that could lead to high cooperation within but conflicts between different subgroups of a network. Our review covers theoretical, experimental and field studies across various disciplines that target these questions and could explain how the dynamics of interactions and reputations help or prevent the establishment and sustainability of cooperation in small- and large-scale societies