Who’s Talking First? Consensus or Lack Thereof in Coevolving Opinion Formation Models

Nardini, Cecilia; Kozma, Balázs; Barrat, Alain

doi:10.1103/physrevlett.100.158701

Cited by 173 publications

(161 citation statements)

References 25 publications

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“…Furthermore, in collective motion it cannot be neglected that an agent's decision to move in a certain direction will determine the agents with whom it will interact next. However, a similar feedback of individual decisions on future interaction partners was also studied in recent works on opinion formation [16][17][18][19][20][21][22][23]. The resulting models incorporate both an opinion formation process on the network and a dynamic update of the network topology and thus fall into the class of adaptive networks (ANs) [24,25].…”

Section: Introductionmentioning

confidence: 99%

Adaptive-network models of swarm dynamics

Huepe¹,

Zschaler

et al. 2011

New J. Phys.

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We propose a simple adaptive-network model describing recent swarming experiments. Exploiting an analogy with human decision making, we capture the dynamics of the model by a low-dimensional system of equations permitting analytical investigation. We find that the model reproduces several characteristic features of swarms, including spontaneous symmetry breaking, noise-and density-driven order-disorder transitions that can be of first or second order, and intermittency. Reproducing these experimental observations using a nonspatial model suggests that spatial geometry may have a lesser impact on collective motion than previously thought.

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Section: Introductionmentioning

confidence: 99%

Adaptive-network models of swarm dynamics

Huepe¹,

Zschaler

et al. 2011

New J. Phys.

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“…Despite the theoretical success of the CND in studying network structure, most realistic case studies of network systems have to consider both topology and node activities simultaneously. Examples are neural networks (Daido and Nakanishi 2004), power grids (Blaabjerg et al 2006), epidemic dynamics (Pastor-Satorras and Vespignani 2001), cascading effects in disaster spreading (Helbing 2013), individual fitness (Caldarelli et al 2002), social norms and collaborative expectations (Peyton Young 1998), co-evolutionary dynamics (Nardini et al 2008;Aoki and Aoyagi 2012), and data mining (Hric et al 2016;Peel et al 2017). However, in these studies the definitions of node activities and the methods to analyze them are highly problem-specific and have a dynamic nature.…”

Section: Introductionmentioning

confidence: 99%

Towards Quantitatively Understanding the Complexity of Social-Ecological Systems—From Connection to Consilience

Shi

Wang

et al. 2017

Int J Disaster Risk Sci

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The complexity of social-ecological systems (SES) is rooted in the outcomes of node activities connected by network topology. Thus far, in network dynamics research, the connectivity degree (CND), indicating how many nodes are connected to a given node, has been the dominant concept. However, connectivity focuses only on network topology, neglecting the crucial relation to node activities, and thereby leaving system outcomes largely unexplained. Inspired by the phenomenon of ''consensus of wills and coordination of activities'' often observed in disaster risk management, we propose a new concept of network characteristic, the consilience degree (CSD), aiming to measure the way in which network topology and node activities together contribute to system outcomes. The CSD captures the fact that nodes may assume different states that make their activities more or less compatible. Connecting two nodes with in/compatible states will lead to outcomes that are un/desirable from the perspective of the SES in question. We mathematically prove that the CSD is a generalized CND, and the CND is a special case of CSD. As a general, fundamental concept, the CSD can facilitate the development of a new framework of network properties, models, and theories that allows us to understand patterns of network behavior that cannot be explained in terms of connectivity alone. We further demonstrate that a co-evolutionary mechanism can naturally improve the CSD. Given the generality of co-evolution in SES, we argue that the CSD is an inherent attribute rather than an artificial concept, which underpins the fundamental importance of the CSD to the study of SES.

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“…", about fairness and about opportunities, versus corruption and crony economics [1][2][3][4][5][6][7] (see footnote a). Clearly, there are other economical and sociological functions and indicators that are valuable in describing nations [36][37][38][39][40][41][42][43][44][45][46][47][48][49][50][51][52][53] and freedom 8 and democracy 6 are in°uencing lives much more. Yet, the income distribution, and the distributions derived from that, the \wealth distribution" and the \control on the°ow of wealth distribution", are clearly among the basic most in-°u ential quantities and should be researched on.…”

Section: Introductionmentioning

confidence: 99%

“…1 For example the Gini 9,26-28 of that distribution tells about the distortion in the income distribution, therefore, suggests to research on whether the opportunities are based on abilities and e®orts rather than on \crony economics" and other biases (e.g., corruption). Gini is a number, 0 Gini 1, where the Gini formula is [26][27][28][29][30][31][32][33][34][35][36] Gini ¼ 1 hci…”

Section: Introductionmentioning

confidence: 99%

The IncomeGini of Fairness

Flomenbom¹

2017

Rep. Adv. Phys. Sci.

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We are in favor of wealth accumulation in a free and democratic nation. Yet, the question is whether the mechanism of wealth accumulation is the same to all the people or are there di®erent mechanisms to di®erent socioeconomic-groups, therefore unfair biases in the nation. Here, we would like to develop ways to quantify and¯ght there unfair distortions. Utilizing methods from socioecono-physics, we¯nd that in a democratic and a free nation, a fair society is such that the income distribution's Gini is in the range, 25% and 33%, 34%. (Gini is the departure of the distribution from the \all are equal" distribution). The value, Gini % 40%, is indeed the transition from the intermediate stage nation to the corrupt nation, where Gini 40% requires that the income distribution is smooth in the top percentiles (rather than a jump like formed). Therefore, the e®ect of the 0.1% excessive wealth is pivotal to the problem; such nations have Gini values beyond 40%. The wealth distribution's properties further strengthen these¯ndings.We develop a dynamical model that is emphasizing the di®erence in the environment of the two camps: the top and the rest, yet¯nd that interactions create a smoother income density.We therefore think that the income distribution's Gini % 40% is the alarm stage, yet the Gini should better follow the middle range of the fair society stage (25% through 33%) rather than the upper bound in that stage, where ways to realize that are elaborated on.

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Who’s Talking First? Consensus or Lack Thereof in Coevolving Opinion Formation Models

Cited by 173 publications

References 25 publications

Adaptive-network models of swarm dynamics

Adaptive-network models of swarm dynamics

Towards Quantitatively Understanding the Complexity of Social-Ecological Systems—From Connection to Consilience

The IncomeGini of Fairness

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