Degrees of Separation, Social Learning, and the Evolution of Cooperation in a Small-World Network

Seltzer, Nicholas; Smirnov, Oleg

doi:10.18564/jasss.2851

Cited by 9 publications

(4 citation statements)

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“…choice of social learning mechanisms) (Nakahashi et al, 2012;Seltzer and Smirnov, 2015;Molleman et al, 2013). Such simulations usually implement a small range of alternative social learning mechanisms and analyze their impact on the behavior of the simulated agent community.…”

Section: Introductionmentioning

confidence: 99%

“…In general it is suggested that social learning is a key contributor to the evolution of cooperation among humans and possibly also among other animals (Boyd and Richerson, 2009;Rendell et al, 2010;Chudek et al, 2013) It has been shown that in simulated social networks imitation of socially distant individuals increase the level of cooperation within the agent community (Seltzer and Smirnov, 2015). Other agentbased simulation studies show that certain forms of social learning (e.g.…”

Section: Introductionmentioning

confidence: 99%

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Social Learning, Environmental Adversity and the Evolution of Cooperation

András¹

2016

Proceedings of the Artificial Life Conference 2016

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Social learning plays a key role in the evolution of cooperation in humans and other animals. It has also been shown both theoretically and experimentally that environmental adversity is also a key determinant of the evolution of cooperation among individuals. Here we investigate the impact of social learning on the evolution of cooperation in the context of a range of levels of environmental adversity. We used an agent-based simulated world of asexual individuals that communicate and play a probabilistic version of the Prisoner's Dilemma game. We considered simulated worlds either with or without random spreading of the offspring and two variants of social learning, either copying to some extent all communication rules or copying fully some of the communication rules of the best performing neighbor individual. The results show that in the case of spreading of the offspring, social learning increases the level of cooperation and reverses the association between this and the level of environmental adversity, i.e. low adversity with social learning implies higher level of cooperation. Copying fully some communication rules also increases the steady-state level of communication complexity in the simulated agent communities. The results suggest that the level of cooperation in communities of individuals may get boosted alternately by highly adverse environments and by layers of social learning in low adversity environments.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Social Learning, Environmental Adversity and the Evolution of Cooperation

András¹

2016

Proceedings of the Artificial Life Conference 2016

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“…Following this work, a wealth of studies provides additional evidence for the positive effect of various spatial lattices on cooperation in PDG [20][21][22][23][24]. Starting from the discovery of the small-world network [25,26] and scalefree network [27], many studies about the evolution of PDG on these two network styles have emerged [28][29][30][31][32]. Furthermore, other game models, such as the snowdrift game, public good game, and stag hunt game, have been introduced to characterize different game situations among players.…”

Section: Introductionmentioning

confidence: 99%

The Evolution of Price Competition Game on Complex Networks

Xie

Shi

2018

Complexity

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The well-known “Bertrand paradox” describes a price competition game in which two competing firms reach an outcome where both charge a price equal to the marginal cost. The fact that the Bertrand paradox often goes against empirical evidences has intrigued many researchers. In this work, we study the game from a new theoretical perspective—an evolutionary game on complex networks. Three classic network models, square lattice, WS small-world network, and BA scale-free network, are used to describe the competitive relations among the firms which are bounded rational. The analysis result shows that full price keeping is one of the evolutionary equilibriums in a well-mixed interaction situation. Detailed experiment results indicate that the price-keeping phenomenon emerges in a square lattice, small-world network and scale-free network much more frequently than in a complete network which represents the well-mixed interaction situation. While the square lattice has little advantage in achieving full price keeping, the small-world network and the scale-free network exhibit a stronger capability in full price keeping than the complete network. This means that a complex competitive relation is a crucial factor for maintaining the price in the real world. Moreover, competition scale, original price, degree of cutting price, and demand sensitivity to price show a significant influence on price evolution on a complex network. The payoff scheme, which describes how each firm’s payoff is calculated in each round game, only influences the price evolution on the scale-free network. These results provide new and important insights for understanding price competition in the real world.

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“…An alternative approach is to use agent-based models and simulations to explore the impact of certain features of individual behaviour of the environment on the processes of cooperation and social learning (Andras et al, 2003;Axelrod, 1997;Nakahashi et al, 2012). Many simulation experiments have been conducted to explore mechanisms of social learning (Nakahashi et al, 2012;Molleman et al, 2013), cooperation (Pepper, 2007), and the role of social learning in the emergence and maintenance of cooperation (Seltzer and Smirnov, 2015). For example, it has been shown that social learning among distant individuals increases the level of cooperation, while conformism may reduce the level of cooperation (Molleman et al, 2013;Burton-Chellew et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Social learning in repeated cooperation games in uncertain environments

András

2018

Cognitive Systems Research

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Cooperation and social learning are fundamental mechanisms that maintain social organisation among animals and humans. Social institutions can be conceptualised abstractly as cooperation games with social learning. In some cases potential cooperation partners may be easily identifiable, while in other cases this is difficult. Real world institutions always operate in uncertain environments. Here we use agent-based simulation to explore the interaction between social learning, cooperation and environmental uncertainty with and without easy to identify cooperation partners. Our agents use a communication language to indicate their cooperation intentions. We discuss the measurement of communication or language complexity metrics, which may be used as correlates of the level of cooperation. The results show that more uncertainty induces more cooperation and that social learning increases the level of cooperation. We show that the positive impact of social learning is bigger in low uncertainty environments than in high uncertainty environments and also in cases where identification of potential cooperation partners is harder. The results suggest that environmental uncertainty, social learning and easy identification of cooperation partners may play alternating roles in the promotion of cooperation in social institutions and the expansion and development of these institutions.

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Degrees of Separation, Social Learning, and the Evolution of Cooperation in a Small-World Network

Cited by 9 publications

References 0 publications

Social Learning, Environmental Adversity and the Evolution of Cooperation

Social Learning, Environmental Adversity and the Evolution of Cooperation

The Evolution of Price Competition Game on Complex Networks

Social learning in repeated cooperation games in uncertain environments

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