Creation, evolution, and dissolution of social groups

Flamino, James; Szymański, Bolesław K.; Bahulkar, Ashwin; Chan, Kevin; Ližardo, Omar

doi:10.1038/s41598-021-96805-7

Cited by 12 publications

(16 citation statements)

References 48 publications

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“…These models in this subsection do not permit agents to alter their original ideological position assigned. This dynamic on how agents swap places with other agents in the effort to maximize their utility, which is homogeneity in this case, has been investigated empirically in the work of [40].…”

Section: Movements Modelmentioning

confidence: 99%

A model simulation of political segmentation through an estimation of the entropy

Domenikos¹,

Mantzaris²

2022

J. Stat. Mech.

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The interconnectivity of a global communication system has allowed exchanges of ideas and expression producing a plethora of positive results. There have also been notable exceptions where clashes of ideas produce friction between people and even violence in the effort to dominate a political discourse. Various models have been proposed on how these ideas arise, grow and interact with each other. This paper explores how the Schelling model of segregation can be modified to represent ideological states and from this new formulation is proposed to assess the entropic trajectory of its simulation. The simple yet expressive model proposed is modified where simulations are monitored from the entropic measures of the system based upon ideological interactions and movements. A key result is that the entropy displays spikes due to randomized outside influences, and that the total entropy of the system decreases. This provides insight into how a system of politically engaged agents can be monitored for stability.

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Section: Movements Modelmentioning

confidence: 99%

A model simulation of political segmentation through an estimation of the entropy

Domenikos¹,

Mantzaris²

2022

J. Stat. Mech.

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“…This way hypergraphs allow to model interactions between groups of different sizes by means of inter-layer links. For the formation and the dissolution of groups, however, more refined dynamic models are needed [50,149].…”

Section: /54mentioning

confidence: 99%

Modeling social resilience: Questions, answers, open problems

Schweitzer¹,

Andres²,

Casiraghi³

et al. 2023

Preprint

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Resilience denotes the capacity of a system to withstand shocks and its ability to recover from them. We develop a framework to quantify the resilience of highly volatile, non-equilibrium social organizations, such as collectives or collaborating teams. It consists of four steps: (i) delimitation, i.e., narrowing down the target systems, (ii) conceptualization, i.e., identifying how to approach social organizations, (iii) formal representation using a combination of agent-based and network models, (iv) operationalization, i.e. specifying measures and demonstrating how they enter the calculation of resilience. Our framework quantifies two dimensions of resilience, the robustness of social organizations and their adaptivity, and combines them in a novel resilience measure. It allows monitoring resilience instantaneously using longitudinal data instead of an ex-post evaluation.

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“…This way hypergraphs allow to model 22/54 interactions between groups of different sizes by means of inter-layer links. For the formation and the dissolution of groups, however, more refined dynamic models are needed [50,149].…”

Section: Bipartite Networkmentioning

confidence: 99%

Modeling Social Resilience: Questions, Answers, Open Problems

Schweitzer

Andres

Casiraghi

et al. 2022

Advs. Complex Syst.

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Resilience denotes the capacity of a system to withstand shocks and its ability to recover from them. We develop a framework to quantify the resilience of highly volatile, nonequilibrium social organizations, such as collectives or collaborating teams. It consists of four steps: (i) delimitation, i.e., narrowing down the target systems, (ii) conceptualization, i.e., identifying how to approach social organizations, (iii) formal representation using a combination of agent-based and network models, (iv) operationalization, i.e. specifying measures and demonstrating how they enter the calculation of resilience. Our framework quantifies two dimensions of resilience, the robustness of social organizations and their adaptivity, and combines them in a novel resilience measure. It allows monitoring resilience instantaneously using longitudinal data instead of an ex-post evaluation.

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Creation, evolution, and dissolution of social groups

Cited by 12 publications

References 48 publications

A model simulation of political segmentation through an estimation of the entropy

A model simulation of political segmentation through an estimation of the entropy

Modeling social resilience: Questions, answers, open problems

Modeling Social Resilience: Questions, Answers, Open Problems

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