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GeoInformatica

, Volume 23, Issue 2, pp 269–298 | Cite as

Using agent-based modelling to simulate social-ecological systems across scales

  • Melvin LippeEmail author
  • Mike Bithell
  • Nick Gotts
  • Davide Natalini
  • Peter Barbrook-Johnson
  • Carlo Giupponi
  • Mareen Hallier
  • Gert Jan Hofstede
  • Christophe Le Page
  • Robin B. Matthews
  • Maja Schlüter
  • Peter Smith
  • Andrea Teglio
  • Kevin Thellmann
Article

Abstract

Agent-based modelling (ABM) simulates Social-Ecological-Systems (SESs) based on the decision-making and actions of individual actors or actor groups, their interactions with each other, and with ecosystems. Many ABM studies have focused at the scale of villages, rural landscapes, towns or cities. When considering a geographical, spatially-explicit domain, current ABM architecture is generally not easily translatable to a regional or global context, nor does it acknowledge SESs interactions across scales sufficiently; the model extent is usually determined by pragmatic considerations, which may well cut across dynamical boundaries. With a few exceptions, the internal structure of governments is not included when representing them as agents. This is partly due to the lack of theory about how to represent such as actors, and because they are not static over the time-scales typical for social changes to have significant effects. Moreover, the relevant scale of analysis is often not known a priori, being dynamically determined, and may itself vary with time and circumstances. There is a need for ABM to cross the gap between micro-scale actors and larger-scale environmental, infrastructural and political systems in a way that allows realistic spatial and temporal phenomena to emerge; this is vital for models to be useful for policy analysis in an era when global crises can be triggered by small numbers of micro-level actors. We aim with this thought-piece to suggest conceptual avenues for implementing ABM to simulate SESs across scales, and for using big data from social surveys, remote sensing or other sources for this purpose.

Keywords

Agent-based modelling Social-ecological systems Cross-scale ABM SESs 

Notes

Acknowledgements

This paper originated from discussions during the Lorentz Center workshop ‘Cross-Scale Resilience in Socio-Ecological Simulations’ in Leiden 1–4 May 2017. The authors would like to thank in particular Géraldine Abrami, Bruce Edmonds, Eline de Jong, Gary Polhill and Nanda Wijermans for organising the workshop, and the Lorentz Center for hosting and providing financial support. Maja Schlüter acknowledges funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 682472 – MUSES). The input of Pete Smith contributes to the DEVIL project [NE/M021327/1]. Kevin Thellmann acknowledges funding from the Water-People-Agriculture Research Training Group funded by the Anton & Petra Ehrmann-Stiftung. Nick Gotts acknowledges help from the Centre for Policy Modelling, Manchester Metropolitan University Business School, where he is a visiting fellow. Melvin Lippe acknowledges funding form the German Federal Ministry of Food and Agriculture due to a decision by the German Bundestag through the LaForeT Policies project.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Melvin Lippe
    • 1
    Email author
  • Mike Bithell
    • 2
  • Nick Gotts
    • 3
  • Davide Natalini
    • 4
  • Peter Barbrook-Johnson
    • 5
  • Carlo Giupponi
    • 6
  • Mareen Hallier
    • 7
  • Gert Jan Hofstede
    • 8
  • Christophe Le Page
    • 9
  • Robin B. Matthews
    • 10
  • Maja Schlüter
    • 11
  • Peter Smith
    • 12
  • Andrea Teglio
    • 6
  • Kevin Thellmann
    • 13
  1. 1.Thünen Institute of International Forestry and Forest EconomicsHamburgGermany
  2. 2.Department of GeographyUniversity of CambridgeCambridgeUK
  3. 3.EdinburghUK
  4. 4.Global Sustainability InstituteAnglia Ruskin UniversityCambridgeUK
  5. 5.Centre for Research in Social SimulationUniversity of SurreySurreyUK
  6. 6.Department of EconomicsCa’ Foscari University of Venice and Venice International UniversityVeneziaItaly
  7. 7.Institute of MathematicsBrandenburg University of Technology Cottbus-SenftenbergCottbusGermany
  8. 8.Information Technology Group, Department of Social SciencesWageningen UniversityWageningenNetherlands
  9. 9.CIRAD, UPR GREENMontpellierFrance
  10. 10.James Hutton InstituteAberdeenUK
  11. 11.Stockholm Resilience CentreStockholm UniversityStockholmSweden
  12. 12.Institute of Biological & Environmental Sciences, School of Biological SciencesUniversity of AberdeenScotlandUK
  13. 13.Institute of Agricultural Sciences in the Tropics (Hans-Ruthenberg-Institute)University of HohenheimStuttgartGermany

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