Statistical analysis of tipping pathways in agent-based models

Helfmann, Luzie; Heitzig, Jobst; Koltai, Péter; Kurths, Jürgen; Schütte, Christof

doi:10.1140/epjs/s11734-021-00191-0

Cited by 9 publications

(5 citation statements)

References 71 publications

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“…The general framework that we have used to combine committor probabilities to compute rates and other steady-state statistics of rare transitions is transition path theory (TPT) (Vanden-Eijnden, 2014). TPT has been applied to molecular dynamics (Antoszewski et al, 2021;Meng et al, 2016;Noé et al, 2009;Strahan et al, 2021), atmospheric and oceanic sciences (Finkel et al, 2020(Finkel et al, , 2022Miron et al, 2021Miron et al, , 2022, and social sciences (Helfmann et al, 2021). Though TPT is typically formulated in a time-homogeneous setting, here we have built in explicit time-dependence to deal with the seasonal cycle, similarly to Helfmann et al (2020).…”

Section: Estimate An Empirical Probability Distribution Over Clusters...mentioning

confidence: 99%

Revealing the Statistics of Extreme Events Hidden in Short Weather Forecast Data

et al. 2023

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The atmosphere's extreme, irregular behavior is, in some ways, more important to characterize than its typical climatology. A society optimized for average historical weather patterns is highly exposed to damage from extreme heat and cold, flooding, and other natural hazards. Moreover, extremes may respond more sensitively than mean behavior to climate change, an argument supported by elementary statistics (

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Section: Estimate An Empirical Probability Distribution Over Clusters...mentioning

confidence: 99%

Revealing the Statistics of Extreme Events Hidden in Short Weather Forecast Data

et al. 2023

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“…Differentiating nodes by their community is frequently used in the literature, e.g. [27,41,22]. The well-known stochastic block model, which we discuss in more detail in section 4.4, generates random graphs that exhibit this clustered structure.…”

Section: Classes and Collective Variablesmentioning

confidence: 99%

Large population limits of Markov processes on random networks

Marvin¹,

Heitzig²,

Koltai³

et al. 2022

Preprint

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We consider time-continuous Markovian discrete-state dynamics on random networks of interacting agents and study the large population limit. The dynamics are projected onto low-dimensional collective variables given by the shares of each discrete state in the system, or in certain subsystems, and general conditions for the convergence of the collective variable dynamics to a mean-field ordinary differential equation are proved. We discuss the convergence to this mean-field limit for a continuous-time noisy version of the so-called "voter model" on Erdős-Rényi random graphs, on the stochastic block model, as well as on random regular graphs. Moreover, a heterogeneous population of agents is studied. For each of these types of interaction networks, we specify the convergence conditions in dependency on the corresponding model parameters.

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“…1.2 Models of opinion dynamics can give insight into associated processes of polarization versus consensus formation (Deffuant et al 2002), and transitions between these regimes (Helfmann et al 2021;Castellano et al 2000).…”

Section: Introductionmentioning

confidence: 99%

An Agent-Based Model of Cultural Change for a Low-Carbon Transition

Torren-Peraire,

Savin,

van den Bergh

2024

JASSS

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Meeting climate goals requires radical changes in the consumption behaviour of individuals. This necessitates an understanding of how the diffusion of low-carbon behaviour will occur. The speed and interdependency of these changes in behavioural choices may be modulated by individuals' culture. We develop an agent-based model to study how behavioural decarbonisation interacts with longer-term cultural change, composed of individuals with multiple behaviours that evolve due to imperfect social learning in a social network. Using the definition of culture as socially transmitted information, we represent individuals' environmental identity as an aggregation of attitudes towards multiple relevant behaviours. The strength of interaction between individuals is determined by the similarity in their environmental identity, leading to inter-behavioural dependency and spillovers in green attitudes. Our results show that the initial distribution of agent attitudes towards behaviours and asymmetries in social learning, such as confirmation bias, are the main drivers of model dynamics, helping to generate awareness of what roadblocks may appear to deep decarbonisation. To assess the impact of culture beyond a purely diffusive regime, we introduce green influencers as a minority of individuals who broadcast a green attitude. The greatest emissions reduction is achieved with the inclusion of culture, relative to a behavioural independence case, and with low confirmation bias. However, green influencers fail to achieve deep behavioural decarbonisation through solely voluntary action. We identify areas for further research regarding how culture, through inter-behavioural dependence, may be leveraged for climate policy.

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Statistical analysis of tipping pathways in agent-based models

Cited by 9 publications

References 71 publications

Revealing the Statistics of Extreme Events Hidden in Short Weather Forecast Data

Revealing the Statistics of Extreme Events Hidden in Short Weather Forecast Data

Large population limits of Markov processes on random networks

An Agent-Based Model of Cultural Change for a Low-Carbon Transition

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