Evolutionary mixed games in structured populations: Cooperation and the benefits of heterogeneity

Amaral, Marco Antônio Franco do; Wardil, Lucas; Perc, Matjaž; Silva, J. Kamphorst Leal da

doi:10.1103/physreve.93.042304

Cited by 101 publications

(62 citation statements)

References 93 publications

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“…Thus, each time we are faced with the choice of either cooperating or defecting, we should also take into account what we risk to loose as individuals, and what we are striving for. In the realm of these considerations, evolutionary multigames [69,[285][286][287] appear as a promising mathematical foundation for properly taking into account such upgrades to existing models. Alternatives to imitation dynamics, such as myopic best response microscopic dynamics [204,205,207] and its variants [215], also merit more research in the realm of the public goods game, in particular as they are more akin to an innovative analysis of a particular situation and thus useful to model human behavior.…”

Section: Future Research and Outlookmentioning

confidence: 99%

Statistical physics of human cooperation

et al. 2017

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Extensive cooperation among unrelated individuals is unique to humans, who often sacrifice personal benefits for the common good and work together to achieve what they are unable to execute alone. The evolutionary success of our species is indeed due, to a large degree, to our unparalleled other-regarding abilities. Yet, a comprehensive understanding of human cooperation remains a formidable challenge. Recent research in social science indicates that it is important to focus on the collective behavior that emerges as the result of the interactions among individuals, groups, and even societies. Non-equilibrium statistical physics, in particular Monte Carlo methods and the theory of collective behavior of interacting particles near phase transition points, has proven to be very valuable for understanding counterintuitive evolutionary outcomes. By studying models of human cooperation as classical spin models, a physicist can draw on familiar settings from statistical physics. However, unlike pairwise interactions among particles that typically govern solid-state physics systems, interactions among humans often involve group interactions, and they also involve a larger number of possible states even for the most simplified description of reality. The complexity of solutions therefore often surpasses that observed in physical systems. Here we review experimental and theoretical research that advances our understanding of human cooperation, focusing on spatial pattern formation, on the spatiotemporal dynamics of observed solutions, and on self-organization that may either promote or hinder socially favorable states.Comment: 48 two-column pages, 35 figures; Review accepted for publication in Physics Report

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Section: Future Research and Outlookmentioning

confidence: 99%

Statistical physics of human cooperation

et al. 2017

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“…continuous and reversible, in complex networks' structure and dynamics, the synchronizing process rather reminds first-order (discontinuous and irreversible) transitions [19]. For evolutionary games in multiplex network, interactions between layers influence evolution of cooperation [20][21][22]. Researchers found spreading dynamics on multiplex network also behaves distinctively from the single-layer network [23][24][25][26][27][28][29][30][31][32][33][34][35].…”

Section: Introductionmentioning

confidence: 99%

Heterogeneous behavioral adoption in multiplex networks

et al. 2018

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Heterogeneity is found widely in populations, e.g. different individuals have diverse personalities and a different willingness to accept novel ideas or behaviors. Whereas population heterogeneity is rarely considered in studying the social contagions on complex networks, especially on multiplex networks. To explore the effect of population heterogeneity on the dynamics of social contagions, a novel model based on double-layer multiplex networks is proposed, in which information diffuses synchronously on the two layers, and each layer is assigned with different adoption thresholds. Meanwhile, populations are classified into the activists and conservatives according to their willingness to adopt new behaviors. To qualitatively understand the effect of population heterogeneity on social contagions, a generalized edge-based compartmental theory is proposed. Through rigorous theoretical analysis and extensive simulations, we find the activists in the two layers promote the adoption of behavior. More interestingly, the crossover phenomena in phase transition are found in the growth of the final adopted size when increasing the information transmission rate. When the proportion of activists is relatively large, the phase transition exhibits continuous pattern. Reducing the proportion of activists induces a hybrid transition, in which the final adoption size first grows continuously at the first threshold, and then increases discontinuously at the second threshold. In addition, we find that the network heterogeneity will change the crossover phenomena in phase transition.

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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

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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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Evolutionary mixed games in structured populations: Cooperation and the benefits of heterogeneity

Cited by 101 publications

References 93 publications

Statistical physics of human cooperation

Statistical physics of human cooperation

Heterogeneous behavioral adoption in multiplex networks

Stability of subsystem solutions in agent-based models

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