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Using agent-based modelling to simulate social-ecological systems across scales
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Number of Authors: 142019 (English)In: Geoinformatica, ISSN 1384-6175, E-ISSN 1573-7624, Vol. 23, no 2, p. 269-298Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
2019. Vol. 23, no 2, p. 269-298
Keywords [en]
Agent-based modelling, Social-ecological systems, Cross-scale, ABM, SESs
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:su:diva-170172DOI: 10.1007/s10707-018-00337-8ISI: 000468331900006OAI: oai:DiVA.org:su-170172DiVA, id: diva2:1329280
Available from: 2019-06-24 Created: 2019-06-24 Last updated: 2019-06-24Bibliographically approved

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