Leveraging statistical physics to improve understanding of cooperation in multiplex networks

Fu, Feng; Chen, Xingru

doi:10.1088/1367-2630/aa78c1

Cited by 31 publications

(21 citation statements)

References 6 publications

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“…While the bulk of research concerning evolutionary games on networks has been devoted to single and isolated networks, such as small-world [20][21][22][23][24][25][26], scale-free [27][28][29][30][31][32][33][34][35][36][37][38][39][40][41], coevolving [42][43][44][45][46][47][48][49][50], hierarchical [51,52] and community [53] networks, the focus has in recent years been shifting towards multilayer and interdependent networks [54][55][56][57][58][59][60][61][62][63][64][65][66][67][68]. This research has revealed interdependent network reciproci...…”

Section: Introductionmentioning

confidence: 99%

Assortativity provides a narrow margin for enhanced cooperation on multilayer networks

et al. 2019

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Research at the interface of statistical physics, evolutionary game theory, and network science has in the past two decades significantly improved our understanding of cooperation in structured populations. We know that networks with broad-scale degree distributions favor the emergence of robust cooperative clusters, and that temporal networks might preclude defectors to exploit cooperators, provided the later can sever their bad ties soon enough. In recent years, however, research has shifted from single and isolated networks to multilayer and interdependent networks. This has revealed new paths to cooperation, but also opened up new questions that remain to be answered. We here study how assortativity in connections between two different network layers affects public cooperation. The connections between the two layers determine to what extent payoffs in one network influence the payoffs in the other network. We show that assortative linking between the layersconnecting hubs of one network with the hubs in the other-does enhance cooperation under adverse conditions, but does so with a relatively modest margin in comparison to random matching or disassortative matching between the two layers. We also confirm previous results, showing that the bias in the payoffs in terms of contributions from different layers can help public cooperation to prevail, and in fact more so than the assortativity between layers. These results are robust to variations in the network structure and average degree, and they can be explained well by the distribution of strategies across the networks and by the suppression of individual success levels that is due to the payoff interdependence.

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Section: Introductionmentioning

confidence: 99%

Assortativity provides a narrow margin for enhanced cooperation on multilayer networks

et al. 2019

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“…Although the present example draws on spatial evolutionary games and should be of interest to contemporary statistical physics research on the subject [48][49][50][51][52][53][54][55][56][57][58][59][60][61][62][63], the concept of stability of subsystem solutions in agent-based models goes far beyond disciplinary boundaries. Whether agents are humans, firms, ants, or ecological entities, whenever more than two states compete in a structured population (represented by a lattice or a network), the stability of subsystem solutions is crucial for a correct and relevant analysis.…”

Section: Discussionmentioning

confidence: 99%

Stability of subsystem solutions in agent-based models

Perc

2017

Preprint

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The fact that relatively simple entities, such as particles or neurons, or even ants or bees or humans, give rise to fascinatingly complex behavior when interacting in large numbers is the hallmark of complex systems science. Agent-based models are frequently employed for modeling and obtaining a predictive understanding of complex systems. Since the sheer number of equations that describe the behavior of an entire agent-based model often makes it impossible to solve such models exactly, Monte Carlo simulation methods must be used for the analysis. However, unlike pairwise interactions among particles that typically govern solid-state physics systems, interactions among agents that describe systems in biology, sociology or the humanities often involve group interactions, and they also involve a larger number of possible states even for the most simplified description of reality. This begets the question: When can we be certain that an observed simulation outcome of an agent-based model is actually stable and valid in the large system-size limit? The latter is key for the correct determination of phase transitions between different stable solutions, and for the understanding of the underlying microscopic processes that led to these phase transitions. We show that a satisfactory answer can only be obtained by means of a complete stability analysis of subsystem solutions. A subsystem solution can be formed by any subset of all possible agent states. The winner between two subsystem solutions can be determined by the average moving direction of the invasion front that separates them, yet it is crucial that the competing subsystem solutions are characterized by a proper composition and spatiotemporal structure before the competition starts. We use the spatial public goods game with diverse tolerance as an example, but the approach has relevance for a wide variety of agent-based models.

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“…The evolution of cooperation in social dilemmas-situations where what is best for the society is at odds with what is best for an individual-is a vibrant example of this development. Many reviews [8][9][10][11] and research papers that reveal key mechanisms for socially preferable evolutionary outcomes have been published in recent years [12][13][14][15][16][17][18][19][20][21][22][23][24][25][26]. Since cooperative behaviour is central to the survival of many animal species, and since it is also at the heart of the remarkable evolutionary success story of humans [27,28], it is one of the great challenges of the 21st century that we succeed in understanding how best to sustain and promote cooperation [29].…”

Section: Introductionmentioning

confidence: 99%

Evolutionary dynamics of cooperation in neutral populations

Szolnoki

Perc

2018

New J. Phys.

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Cooperation is a difficult proposition in the face of Darwinian selection. Those that defect have an evolutionary advantage over cooperators who should therefore die out. However, spatial structure enables cooperators to survive through the formation of homogeneous clusters, which is the hallmark of network reciprocity. Here we go beyond this traditional setup and study the spatiotemporal dynamics of cooperation in a population of populations. We use the prisonerʼs dilemma game as the mathematical model and show that considering several populations simultaneously gives rise to fascinating spatiotemporal dynamics and pattern formation. Even the simplest assumption that strategies between different populations are payoff-neutral with one another results in the spontaneous emergence of cyclic dominance, where defectors of one population become prey of cooperators in the other population, and vice versa. Moreover, if social interactions within different populations are characterized by significantly different temptations to defect, we observe that defectors in the population with the largest temptation counterintuitively vanish the fastest, while cooperators that hang on eventually take over the whole available space. Our results reveal that considering the simultaneous presence of different populations significantly expands the complexity of evolutionary dynamics in structured populations, and it allows us to understand the stability of cooperation under adverse conditions that could never be bridged by network reciprocity alone.

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Leveraging statistical physics to improve understanding of cooperation in multiplex networks

Cited by 31 publications

References 6 publications

Assortativity provides a narrow margin for enhanced cooperation on multilayer networks

Assortativity provides a narrow margin for enhanced cooperation on multilayer networks

Stability of subsystem solutions in agent-based models

Evolutionary dynamics of cooperation in neutral populations

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