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  Statistical analysis of tipping pathways in agent-based models

Helfmann, L., Heitzig, J., Koltai, P., Kurths, J., Schütte, C. (2021): Statistical analysis of tipping pathways in agent-based models. - European Physical Journal - Special Topics, 230, 16-17, 3249-3271.
https://doi.org/10.1140/epjs/s11734-021-00191-0

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???ViewItemFull_lblCiteItemAs???: https://publications.pik-potsdam.de/pubman/item/item_25716 ???ViewItemFull_lblCiteItemVersionAs???: https://publications.pik-potsdam.de/pubman/item/item_25716_2
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 ???ViewItemFull_lblCreators???:
Helfmann, Luzie1, ???ENUM_CREATORROLE_AUTHOR???           
Heitzig, Jobst1, ???ENUM_CREATORROLE_AUTHOR???           
Koltai, Péter2, ???ENUM_CREATORROLE_AUTHOR???
Kurths, Jürgen1, ???ENUM_CREATORROLE_AUTHOR???           
Schütte, Christof2, ???ENUM_CREATORROLE_AUTHOR???
???ViewItemFull_lblAffiliations???:
1Potsdam Institute for Climate Impact Research, Potsdam, ou_persistent13              
2External Organizations, ou_persistent22              

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 ???ViewItemFull_lblAbstract???: Agent-based models are a natural choice for modeling complex social systems. In such models simple stochastic interaction rules for a large population of individuals on the microscopic scale can lead to emergent dynamics on the macroscopic scale, for instance a sudden shift of majority opinion or behavior. Here we are introducing a methodology for studying noise-induced tipping between relevant subsets of the agent state space representing characteristic configurations. Due to a large number of interacting individuals, agent-based models are high-dimensional, though usually a lower-dimensional structure of the emerging collective behaviour exists. We therefore apply Diffusion Maps, a non-linear dimension reduction technique, to reveal the intrinsic low-dimensional structure. We characterize the tipping behaviour by means of Transition Path Theory, which helps gaining a statistical understanding of the tipping paths such as their distribution, flux and rate. By systematically studying two agent-based models that exhibit a multitude of tipping pathways and cascading effects, we illustrate the practicability of our approach.

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???ViewItemFull_lblLanguages???: eng - English
 ???ViewItemFull_lblDates???: 2021-06-012021-06-182021-10
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 ???ViewItemFull_lblIdentifiers???: ???ENUM_IDENTIFIERTYPE_DOI???: 10.1140/epjs/s11734-021-00191-0
???ENUM_IDENTIFIERTYPE_PIKDOMAIN???: RD4 - Complexity Science
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???ENUM_IDENTIFIERTYPE_ORGANISATIONALK???: RD4 - Complexity Science
???ENUM_IDENTIFIERTYPE_ORGANISATIONALK???: FutureLab - Game Theory & Networks of Interacting Agents
???ENUM_IDENTIFIERTYPE_RESEARCHTK???: Complex Networks
???ENUM_IDENTIFIERTYPE_RESEARCHTK???: Tipping Elements
???ENUM_IDENTIFIERTYPE_MODELMETHOD???: Agent-based Models
???ENUM_IDENTIFIERTYPE_MODELMETHOD???: Nonlinear Data Analysis
???ENUM_IDENTIFIERTYPE_OATYPE???: Hybrid Open Access
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???ViewItemFull_lblSourceTitle???: European Physical Journal - Special Topics
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???ViewItemFull_lblPages???: ???lbl_noEntry??? ???ViewItemFull_lblSourceVolumeIssue???: 230 (16-17) ???ViewItemFull_lblSourceSequenceNo???: ???lbl_noEntry??? ???ViewItemFull_lblSourceStartEndPage???: 3249 - 3271 ???ViewItemFull_lblSourceIdentifier???: ???ENUM_IDENTIFIERTYPE_CONE???: https://publications.pik-potsdam.de/cone/journals/resource/150617
???ENUM_IDENTIFIERTYPE_PUBLISHER???: Springer