Opinion Formation Models

Helbing, Dirk

doi:10.1007/978-3-642-11546-2_10

Cited by 7 publications

(7 citation statements)

References 25 publications

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“…The parameters compatible with our experiment suggest that the system operates in a regime of quasiperiodicity close to a cascade of transitions leading to chaos [1,25]. Anecdotal evidence supports the existence of such fluctuations in human activities [26]. An interesting model prediction that we have not observed experimentally here, relates to region 4 in our model.…”

supporting

confidence: 68%

Foraging at the Edge of Chaos: Internal Clock versus External Forcing

et al. 2013

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Activity rhythms in animal groups arise both from external changes in the environment, as well as from internal group dynamics. These cycles are reminiscent of physical and chemical systems with quasiperiodic and even chaotic behavior resulting from ''autocatalytic'' mechanisms. We use nonlinear differential equations to model how the coupling between the self-excitatory interactions of individuals and external forcing can produce four different types of activity rhythms: quasiperiodic, chaotic, phase locked, and displaying over or under shooting. At the transition between quasiperiodic and chaotic regimes, activity cycles are asymmetrical, with rapid activity increases and slower decreases and a phase shift between external forcing and activity. We find similar activity patterns in ant colonies in response to varying temperature during the day. Thus foraging ants operate in a region of quasiperiodicity close to a cascade of transitions leading to chaos. The model suggests that a wide range of temporal structures and irregularities seen in the activity of animal and human groups might be accounted for by the coupling between collectively generated internal clocks and external forcings.

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supporting

confidence: 68%

Foraging at the Edge of Chaos: Internal Clock versus External Forcing

et al. 2013

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“…Highly complex models are the state-of-the-art for weather and climate prediction [11, 23, 24], whereas simple models allow us to assess processes and feedbacks, thereby improving our intuition of climate system dynamics [25–29]. Likewise, the behavioural sciences have benefited from a variety of modelling approaches, that address the diverse set of social processes that take place on the individual and societal level [30]. Here, we use minimal models for both social and climate dynamics.…”

Section: Methodsmentioning

confidence: 99%

Charting pathways to climate change mitigation in a coupled socio-climate model

2019

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Geophysical models of climate change are becoming increasingly sophisticated, yet less effort is devoted to modelling the human systems causing climate change and how the two systems are coupled. Here, we develop a simple socio-climate model by coupling an Earth system model to a social dynamics model. We treat social processes endogenously—emerging from rules governing how individuals learn socially and how social norms develop—as well as being influenced by climate change and mitigation costs. Our goal is to gain qualitative insights into scenarios of potential socio-climate dynamics and to illustrate how such models can generate new research questions. We find that the social learning rate is strongly influential, to the point that variation of its value within empirically plausible ranges changes the peak global temperature anomaly by more than 1°C. Conversely, social norms reinforce majority behaviour and therefore may not provide help when we most need it because they suppress the early spread of mitigative behaviour. Finally, exploring the model’s parameter space for mitigation cost and social learning suggests optimal intervention pathways for climate change mitigation. We find that prioritising an increase in social learning as a first step, followed by a reduction in mitigation costs provides the most efficient route to a reduced peak temperature anomaly. We conclude that socio-climate models should be included in the ensemble of models used to project climate change.

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“…Robots are man-made computerized machines which perform tasks on human commands or by themselves and seek to make work easier for human beings. The research of robot modeling provides better understanding of the interaction of robots and its environment [ 10 ]. These models can help us learn the main factors of the behaviors of the system and also help us in improving the understanding of the properties of robot collective behaviors that emerged.…”

Section: Introductionmentioning

confidence: 99%

A Mathematical Model to Study the Effectiveness of Some of the Strategies Adopted in Curtailing the Spread of COVID-19

Baba

Esmaili

2020

Computational and Mathematical Methods in Medicine

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In this paper, we developed a model that suggests the use of robots in identifying COVID-19-positive patients and which studied the effectiveness of the government policy of prohibiting migration of individuals into their countries especially from those countries that were known to have COVID-19 epidemic. Two compartmental models consisting of two equations each were constructed. The models studied the use of robots for the identification of COVID-19-positive patients. The effect of migration ban strategy was also studied. Four biologically meaningful equilibrium points were found. Their local stability analysis was also carried out. Numerical simulations were carried out, and the most effective strategy to curtail the spread of the disease was shown.

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Opinion Formation Models

Cited by 7 publications

References 25 publications

Foraging at the Edge of Chaos: Internal Clock versus External Forcing

Foraging at the Edge of Chaos: Internal Clock versus External Forcing

Charting pathways to climate change mitigation in a coupled socio-climate model

A Mathematical Model to Study the Effectiveness of Some of the Strategies Adopted in Curtailing the Spread of COVID-19

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