Collective Behavior in Cascade and Schelling Model

Iwanaga, Saori; Namatame, Akira

doi:10.1016/j.procs.2013.10.045

Cited by 8 publications

(5 citation statements)

References 15 publications

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“…In threshold model, we found that collective behaviour depends on the initial behaviour of high degree agents (Iwanaga and Namatame, 2013). With comparing both models, we analyse the affection on collective behaviour in cascade model.…”

Section: Threshold Modelmentioning

confidence: 97%

“…Moreover, we show the effect of number of interaction on collective behaviour. Then, in threshold model, we found that collective behaviour depends on the initial behaviour of high degree agents in several social networks, such as random network, small world network or scale free network (Iwanaga and Namatame, 2013).…”

Section: Introductionmentioning

confidence: 97%

“…Montanari and Saberi (2010) shows that innovation spreads much more slowly on well-connected network structure dominated by long range links than in low dimensional ones dominated and Komatsu and Namatame (2012) obtains the optimal network for good cascade using genetic algorithm and they show the network have a sufficient number of vulnerable nodes and hub node of medium size. We (Iwanaga and Namatame, 2013) compared threshold model with cascade model and showed that collective behaviour is affected in the structure of the social network and threshold to make decision and the collective behaviour was stochastic. Moreover, collective behaviour is almost same as threshold model, though the decision is not interactive and simultaneously.…”

Section: Introductionmentioning

confidence: 99%

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Collective behaviour in cascade model depends on turn of the choice

Iwanaga

Namatame

2016

IJAL

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There are growing interests for studying collective behaviour including the dynamics of markets, the emergence of social norms and conventions, and collective phenomena in daily life such as traffic congestion. In cascade model, collective behaviour is affected in the structure of the social network and threshold to make decision, and the collective behaviour was stochastic, similar to threshold model in local interaction. In both model, although decisions of agents are deterministic, collective behaviour of population become stochastic. In previous work, we analysed origin of stochastic collective behaviour and we found that collective behaviour depends on the initial behaviour of high degree in threshold model in local interaction. In this paper, we analysed origin of stochastic collective behaviour in cascade model. We analysed why collective behaviour is stochastic in scale free network. Then, we show that turn of the choice effects on collective behaviour not the first choices.

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Section: Threshold Modelmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

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Collective behaviour in cascade model depends on turn of the choice

Iwanaga

Namatame

2016

IJAL

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“…Some studies have extended the model to two-or multidimensional spaces in simulations and analytic proofs (Dall'Asta et al, 2008;Immorlica et al, 2015;Barmpalias et al, 2016Barmpalias et al, , 2018. Studies more rooted in the model's home discipline of economics have included factors such as housing markets (Zhang, 2004) or used it in a game-theoretic study of collective action (Iwanaga and Namatame, 2013). Pancs et al (2007) show how even if all actors have a strict preference for integration, the segregation result still holds.…”

Section: Literature Reviewmentioning

confidence: 99%

Positive algorithmic bias cannot stop fragmentation in homophilic networks

Blex

Yasseri

2020

The Journal of Mathematical Sociology

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Fragmentation, echo chambers, and their amelioration in social networks have been a growing concern in the academic and non-academic world. This paper shows how, under the assumption of homophily, echo chambers and fragmentation are system-immanent phenomena of highly flexible social networks, even under ideal conditions for heterogeneity. We achieve this by finding an analytical, network-based solution to the Schelling model and by proving that weak ties do not hinder the process. Furthermore, we derive that no level of positive algorithmic bias in form of rewiring is capable of preventing fragmentation and its effect on reducing the fragmentation speed is negligible.

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Section: Literature Reviewmentioning

confidence: 99%

Positive algorithmic bias cannot stop fragmentation in homophilic networks

Blex,

Yasseri

2020

Preprint

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Fragmentation, echo chambers, and their amelioration in social networks have been a growing concern in the academic and nonacademic world. This paper shows how, under the assumption of homophily, echo chambers and fragmentation are system-immanent phenomena of highly flexible social networks, even under ideal conditions for heterogeneity. We achieve this by finding an analytical, network-based solution to the Schelling model and by proving that weak ties do not hinder the process. Furthermore, we derive the minimal algorithmic bias needed for the mitigation of such fragmentation. However, we show that the effect of such algorithmic bias on stopping fragmentation is limited.

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Collective Behavior in Cascade and Schelling Model

Cited by 8 publications

References 15 publications

Collective behaviour in cascade model depends on turn of the choice

Collective behaviour in cascade model depends on turn of the choice

Positive algorithmic bias cannot stop fragmentation in homophilic networks

Positive algorithmic bias cannot stop fragmentation in homophilic networks

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