Evolutionary game theory and criticality

Mahmoodi, Korosh; Grigolini, Paolo

doi:10.1088/1751-8113/50/1/015101

Cited by 11 publications

(14 citation statements)

References 27 publications

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“…Using the terms adopted by (Rand, 2016) we may identify his intuitive decision making with the choice of imitation strength and his deliberative decision making as a direct adoption of the Prisoner's Dilemma game. The theoretical prediction made by Rand that “deliberation will undermine pure cooperation” seems to fit the observation (Mahmoodi and Grigolini, 2017) that the lack of criticality disrupts the Nowak and May network reciprocity (Nowak and May, 1992). …”

Section: Discussionmentioning

confidence: 60%

“…The critical value of the control parameter K is K C = 1 in the all-to-all coupling case and K C = 1.5 (M = 100) in the case of a regular two-dimensional lattice (Mahmoodi and Grigolini, 2017). …”

Section: Decision Making Modelmentioning

confidence: 99%

“…The additional social interaction of the individuals within this social group was found to disrupt network reciprocity (Vilone et al, 2012 , 2014 ). However, when this additional activity is based on individuals imitating the choices made by their nearest neighbors, it may favor the survival of cooperation (Mahmoodi and Grigolini, 2017 ). This survivability is a consequence of the imitation strength being sufficiently strong to generate criticality as in the Decision Making Model (DMM) (West et al, 2014 ).…”

Section: Introductionmentioning

confidence: 99%

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Self-organizing Complex Networks: individual versus global rules

2017

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We introduce a form of Self-Organized Criticality (SOC) inspired by the new generation of evolutionary game theory, which ranges from physiology to sociology. The single individuals are the nodes of a composite network, equivalent to two interacting subnetworks, one leading to strategy choices made by the individuals under the influence of the choices of their nearest neighbors and the other measuring the Prisoner's Dilemma Game payoffs of these choices. The interaction between the two networks is established by making the imitation strength K increase or decrease according to whether the last two payoffs increase or decrease upon increasing or decreasing K. Although each of these imitation strengths is selected selfishly, and independently of the others as well, the social system spontaneously evolves toward the state of cooperation. Criticality is signaled by temporal complexity, namely the occurrence of non-Poisson renewal events, the time intervals between two consecutive crucial events being given by an inverse power law index μ = 1.3 rather than by avalanches with an inverse power law distribution as in the original form of SOC. This new phenomenon is herein labeled self-organized temporal criticality (SOTC). We compare this bottom-up self-organization process to the adoption of a global choice rule based on assigning to all the units the same value K, with the time evolution of common K being determined by consciousness of the social benefit, a top-down process implying the action of a leader. In this case self-organization is impeded by large intensity fluctuations and the global social benefit turns out to be much weaker. We conclude that the SOTC model fits the requests of a manifesto recently proposed by a number of European social scientists.

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Section: Discussionmentioning

confidence: 60%

Section: Decision Making Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Self-organizing Complex Networks: individual versus global rules

2017

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“…Since the clusters of cooperators are richer than the clusters of defectors it is plausible that the most successful nearest neighbor is a cooperator. However, this attempt at mimicking the action of a collective intelligence failed because the social activity of the units, being subcritical, disrupts the beneficial effects of network reciprocity [44,45]. We note that SOTC modeling represents an attempt to amend the field of EGT by the limitations preventing, for instance, the concept of network reciprocity from yielding a satisfactory resolution of the altruism paradox.…”

Section: Discussionmentioning

confidence: 99%

Self‐Organized Temporal Criticality: Bottom‐Up Resilience versus Top‐Down Vulnerability

2018

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We propose a social model of spontaneous self-organization generating criticality and resilience, called Self-Organized Temporal Criticality (SOTC). The criticality-induced long-range correlation favors the societal benefit and can be interpreted as the social system becoming cognizant of the fact that altruism generates societal benefit. We show that when the spontaneous bottom-up emergence of altruism is replaced by a top-down process, mimicking the leadership of an elite, the crucial events favoring the system's resilience are turned into collapses, corresponding to the falls of the leading elites. We also show with numerical simulation that the top-down SOTC lacks the resilience of the bottom-up SOTC. We propose this theoretical model to contribute to the mathematical foundation of theoretical sociology illustrated in 1901 by Pareto to explain the rise and fall of elites.

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“…Therefore, in modelling the decision-making process of human groups one has to take into account the effect of social interactions, which promote consensus-seeking, but also the influence of the level of expertise/knowledge of individuals. Under this perspective a few models of decision-making can be found in the literature, attempting to capture the influence of the main drivers of the individual behavior in groups, and in particular, of self-interest and consensus-seeking [47][48][49][50][51]. Following this line of research, in this paper, we employ a model of decision making, already proposed by GC and IG in [51], where consensus-seeking is modelled using the Ising-Glauber dynamics [52,53], whereas the knowledge of each member in the group is modelled through an individual fitness landscape described in terms of a Kauffman N K model [54,55].…”

Section: Introductionmentioning

confidence: 99%

Criticality triggers the emergence of collective intelligence in groups

et al. 2017

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A spinlike model mimicking human behavior in groups is employed to investigate the dynamics of the decision-making process. Within the model, the temporal evolution of the state of systems is governed by a time-continuous Markov chain. The transition rates of the resulting master equation are defined in terms of the change of interaction energy between the neighboring agents (change of the level of conflict) and the change of a locally defined agent fitness. Three control parameters can be identified: (i) the social interaction strength βJ measured in units of social temperature, (ii) the level of confidence β^{'} that each individual has on his own expertise, and (iii) the level of knowledge p that identifies the expertise of each member. Based on these three parameters, the phase diagrams of the system show that a critical transition front exists where a sharp and concurrent change in fitness and consensus takes place. We show that at the critical front, the information leakage from the fitness landscape to the agents is maximized. This event triggers the emergence of the collective intelligence of the group, and in the end it leads to a dramatic improvement in the decision-making performance of the group. The effect of size M of the system is also investigated, showing that, depending on the value of the control parameters, increasing M may be either beneficial or detrimental.

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Evolutionary game theory and criticality

Cited by 11 publications

References 27 publications

Self-organizing Complex Networks: individual versus global rules

Self-organizing Complex Networks: individual versus global rules

Self‐Organized Temporal Criticality: Bottom‐Up Resilience versus Top‐Down Vulnerability

Criticality triggers the emergence of collective intelligence in groups

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