Heider and coevolutionary balance: From discrete to continuous phase transition

Kargaran, A.; Jafari, G. R.

doi:10.1103/physreve.103.052302

Cited by 8 publications

(5 citation statements)

References 47 publications

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“…Following Heider’s intuition ( 18 – 41 ), current approaches toward social balance often account for the effect of triangles on social network formation in one way or another. For example, the models in refs.…”

mentioning

confidence: 99%

Empirical social triad statistics can be explained with dyadic homophylic interactions

Pham

Korbel

Hanel

et al. 2022

Proc. Natl. Acad. Sci. U.S.A.

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The remarkable robustness of many social systems has been associated with a peculiar triangular structure in the underlying social networks. Triples of people that have three positive relations (e.g., friendship) between each other are strongly overrepresented. Triples with two negative relations (e.g., enmity) and one positive relation are also overrepresented, and triples with one or three negative relations are drastically suppressed. For almost a century, the mechanism behind these very specific (“balanced”) triad statistics remained elusive. Here, we propose a simple realistic adaptive network model, where agents tend to minimize social tension that arises from dyadic interactions. Both opinions of agents and their signed links (positive or negative relations) are updated in the dynamics. The key aspect of the model resides in the fact that agents only need information about their local neighbors in the network and do not require (often unrealistic) higher-order network information for their relation and opinion updates. We demonstrate the quality of the model on detailed temporal relation data of a society of thousands of players of a massive multiplayer online game where we can observe triangle formation directly. It not only successfully predicts the distribution of triangle types but also explains empirical group size distributions, which are essential for social cohesion. We discuss the details of the phase diagrams behind the model and their parameter dependence, and we comment on to what extent the results might apply universally in societies.

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mentioning

confidence: 99%

Empirical social triad statistics can be explained with dyadic homophylic interactions

Pham

Korbel

Hanel

et al. 2022

Proc. Natl. Acad. Sci. U.S.A.

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“…To identify positive/negative pair-connection, we can adopt the energy function approach for signed social networks [42][43][44][45][46]. In this approach, the unbiased binary variable (±) represents the node and link's state.…”

Section: Consumers Strategy Dynamicsmentioning

confidence: 99%

Modelling co-evolution of resource feedback and social network dynamics in human-environmental systems

Saeedian,

Tu,

Menegazzo

et al. 2024

New J. Phys.

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Games with environmental feedback have become a crucial area of study across various scientific domains, modelling the dynamic interplay between human decisions and environmental changes, and highlighting the consequences of our choices on natural resources and biodiversity. In this work, we propose a co-evolutionary model for human-environment systems that incorporates the effects of knowledge feedback and social interaction on the sustainability of common pool resources. The model represents consumers as agents who adjust their resource extraction based on the resource's state. These agents are connected through social networks, where links symbolize either affinity or aversion among them. The interplay between social dynamics and resource dynamics is explored, with the system's evolution analyzed across various network topologies and initial conditions.We find that knowledge feedback can independently sustain common pool resources. However, the impact of social interactions on sustainability is dual-faceted: it can either support or impede sustainability, influenced by the network's connectivity and heterogeneity. A notable finding is the identification of a critical network mean degree, beyond which a depletion/repletion transition parallels an absorbing/active state transition in social dynamics, i.e., individual agents and their connections are/are not prone to being frozen in their social states.Furthermore, the study examines the evolution of the social network, revealing the emergence of two polarized groups where agents within each community have the same affinity. Finally, we observe an inverse relationship between system complexity and sustainability. Comparative analyses using Monte-Carlo simulations and rate equations are employed, along with analytical arguments, to reinforce the study's findings. The model successfully captures key aspects of the human-environment system, offering valuable insights to understand how both the spread of information and social dynamics may impact the sustainability of common pool resources.

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“…In this model, called the coevolutionary balance model, we can put a sum over all such units in the network to obtain the social tension, which is described mathematically as the Hamiltonian of the system [18,23]. Despite the Heider balance model, this model represents a continuous phase transition in the energy of the system [24,25].…”

Section: Introductionmentioning

confidence: 99%

“…In later studies comparisons have been made between these two structural and coevolutionary balance models in order to investigate the role of each balance view [25][26][27][28][29]. The investigations showed the coevolutionary term, considering node-link-node interactions, supports local benefits for people in the network as its concern is reducing the tension in each person's neighboring.…”

Section: Introductionmentioning

confidence: 99%

Second- to first-order phase transition: Coevolutionary versus structural balance

Noudehi

Kargaran²,

Azimi-Tafreshi³

et al. 2022

Phys. Rev. E

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Heider and coevolutionary balance: From discrete to continuous phase transition

Cited by 8 publications

References 47 publications

Empirical social triad statistics can be explained with dyadic homophylic interactions

Empirical social triad statistics can be explained with dyadic homophylic interactions

Modelling co-evolution of resource feedback and social network dynamics in human-environmental systems

Second- to first-order phase transition: Coevolutionary versus structural balance

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