Evolution of global cooperation driven by risks

Du, Jinming; Wu, Bin; Wang, Long

doi:10.1103/physreve.85.056117

Cited by 28 publications

(18 citation statements)

References 39 publications

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“…Previous studies focus on how mutation, selection and population structure affect these properties of stochastic evolutionary games. However, relatively little work has been done on this issue in the context of multi-player games [26,39,40,[71][72][73]. Motivated by these, we have investigated how the multi-player interaction pattern influences those dynamical properties.…”

Section: Summary and Discussionmentioning

confidence: 99%

Dynamic Properties of Evolutionary Multi-player Games in Finite Populations

Traulsen

Gokhale

2013

Games

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William D. Hamilton famously stated that "human life is a many person game and not just a disjoined collection of two person games". However, most of the theoretical results in evolutionary game theory have been developed for two player games. In spite of a multitude of examples ranging from humans to bacteria, multi-player games have received less attention than pairwise games due to their inherent complexity. Such complexities arise from the fact that group interactions cannot always be considered as a sum of multiple pairwise interactions. Mathematically, multi-player games provide a natural way to introduce non-linear, polynomial fitness functions into evolutionary game theory, whereas pairwise games lead to linear fitness functions. Similarly, studying finite populations is a natural way of introducing intrinsic stochasticity into population dynamics. While these topics have been dealt with individually, few have addressed the combination of finite populations and multi-player games so far. We are investigating the dynamical properties of evolutionary multi-player games in finite populations. Properties of the fixation probability and fixation time, which are relevant for rare mutations, are addressed in well mixed populations. For more frequent mutations, the average abundance is investigated in well mixed as well as in structured populations. While the fixation properties are generalizations of the results from two player scenarios, addressing the average abundance in multi-player games gives rise to novel outcomes not possible in pairwise games.

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Section: Summary and Discussionmentioning

confidence: 99%

Dynamic Properties of Evolutionary Multi-player Games in Finite Populations

Traulsen

Gokhale

2013

Games

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“…Within the area of dynamics, two-player games [14][15][16] are frequently adopted to model typical pairwise interactions to understand the evolution of cooperation [17][18][19][20][21][22][23][24][25][26]. Considering the ubiquitously group interactions ranging from the natural world to human society, researchers recently generalized two-player games to their multiplayer versions [27][28][29][30][31][32][33][34][35][36][37], such as the N -person prisoner's dilemma [30,38], N-person snowdrift game [31,32], N -person stag hunt game [39], as well as the N -person ultimatum game [40]. In a typical collective action, an individual's payoff could be no longer the simple summation of many pairwise interactions [33,41], and instead it is replaced by the multiple interactive payoffs from multiplayer games, which depends on what strategies all other opponents hold in the same group.…”

Section: Introductionmentioning

confidence: 99%

Evolutionary dynamics of general group interactions in structured populations

Broom

et al. 2016

Phys. Rev. E

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The evolution of populations is influenced by many factors, and the simple classical models have been developed in a number of important ways. Both population structure and multiplayer interactions have been shown to significantly affect the evolution of important properties, such as the level of cooperation or of aggressive behavior. Here we combine these two key factors and develop the evolutionary dynamics of general group interactions in structured populations represented by regular graphs. The traditional linear and threshold public goods games are adopted as models to address the dynamics. We show that for linear group interactions, population structure can favor the evolution of cooperation compared to the well-mixed case, and we see that the more neighbors there are, the harder it is for cooperators to persist in structured populations. We further show that threshold group interactions could lead to the emergence of cooperation even in well-mixed populations. Here population structure sometimes inhibits cooperation for the threshold public goods game, where depending on the benefit to cost ratio, the outcomes are bistability or a monomorphic population of defectors or cooperators. Our results suggest, counterintuitively, that structured populations are not always beneficial for the evolution of cooperation for nonlinear group interactions.

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“…It has been widely investigated during the last decades. The emergence and persistence of cooperation among individuals, whatever human beings, other creatures, or agents in complex systems are all focused by researchers [1]- [6]. Agents achieve a common task through association with others or division of labor.…”

Section: Introductionmentioning

confidence: 99%

Evolutionary game dynamics of multi-agent cooperation driven by self-learning

Wang

2013

2013 9th Asian Control Conference (ASCC)

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Multi-agent cooperation problem is a fundamental issue in the coordination control field. Individuals achieve a common task through association with others or division of labor. Evolutionary game dynamics offers a basic framework to investigate how agents self-adaptively switch their strategies in accordance with various targets, and also the evolution of their behaviors. In this paper, we analytically study the strategy evolution in a multiple player game model driven by self-learning. Self-learning dynamics is of importance for agent strategy updating yet seldom analytically addressed before. It is based on self-evaluation, which applies to distributed control. We focus on the abundance of different strategies (behaviors of agents) and their oscillation (frequency of behavior switching). We arrive at the condition under which a strategy is more abundant over the other under weak selection limit. Such condition holds for any finite population size of ≥ 3, thus it fits for the systems with finite agents, which has notable advantage over that of pairwise comparison process. At certain states of evolutionary stable state, there exists "ping-pong effect" with stable frequency, which is not affected by aspirations. Our results indicate that self-learning dynamics of multi-player games has special characters. Compared with pairwise comparison dynamics and Moran process, it shows different effect on strategy evolution, such as promoting cooperation in collective risk games with large threshold.

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Evolution of global cooperation driven by risks

Cited by 28 publications

References 39 publications

Dynamic Properties of Evolutionary Multi-player Games in Finite Populations

Dynamic Properties of Evolutionary Multi-player Games in Finite Populations

Evolutionary dynamics of general group interactions in structured populations

Evolutionary game dynamics of multi-agent cooperation driven by self-learning

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