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  Macroscopic approximation methods for the analysis of adaptive networked agent-based models: Example of a two-sector investment model

Kolb, J. J., Müller-Hansen, F., Kurths, J., Heitzig, J. (2020): Macroscopic approximation methods for the analysis of adaptive networked agent-based models: Example of a two-sector investment model. - Physical Review E, 102, 4, 042311.
https://doi.org/10.1103/PhysRevE.102.042311

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Kolb, Jakob Johannes1, Author              
Müller-Hansen, Finn1, Author              
Kurths, Jürgen1, Author              
Heitzig, Jobst1, Author              
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1Potsdam Institute for Climate Impact Research, ou_persistent13              

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 Abstract: In this paper, we propose a statistical aggregation method for agent-based models with heterogeneous agents that interact both locally on a complex adaptive network and globally on a market. The method combines three approaches from statistical physics: (a) moment closure, (b) pair approximation of adaptive network processes, and (c) thermodynamic limit of the resulting stochastic process. As an example of use, we develop a stochastic agent-based model with heterogeneous households that invest in either a fossil-fuel- or renewables-based sector while allocating labor on a competitive market. Using the adaptive voter model, the model describes agents as social learners that interact on a dynamic network. We apply the approximation methods to derive a set of ordinary differential equations that approximate the macrodynamics of the model. A comparison of the reduced analytical model with numerical simulations shows that the approximation fits well for a wide range of parameters. The method makes it possible to use analytical tools to better understand the dynamical properties of models with heterogeneous agents on adaptive networks. We showcase this with a bifurcation analysis that identifies parameter ranges with multistabilities. The method can thus help to explain emergent phenomena from network interactions and make them mathematically traceable.

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 Dates: 2020-10-282020-10-28
 Publication Status: Finally published
 Pages: -
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 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1103/PhysRevE.102.042311
MDB-ID: No data to archive
PIKDOMAIN: RD4 - Complexity Science
Research topic keyword: Complex Networks
Model / method: copan:CORE
Research topic keyword: Nonlinear Dynamics
Research topic keyword: Economics
Organisational keyword: RD4 - Complexity Science
Organisational keyword: FutureLab - Game Theory & Networks of Interacting Agents
Working Group: Network- and machine-learning-based prediction of extreme events
 Degree: -

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Title: Physical Review E
Source Genre: Journal, SCI, Scopus, p3
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Pages: - Volume / Issue: 102 (4) Sequence Number: 042311 Start / End Page: - Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/150218
Publisher: American Physical Society (APS)