The Explicit Representation of Context in Agent-Based Models of Complex Adaptive Spatial Systems.
The dynamic behavior of complex adaptive spatial systems is driven by interactions that occur among system components. The proper representation of individual-individual and individual-environment interaction in agent-based simulations of such systems is, therefore, of paramount importance to model...
| Publicado en: | Annals of the Association of American Geographers Vol. 100; no. 5; pp. 1128 - 1156 |
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| Autores principales: | , |
| Formato: | Artículo |
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Taylor & Francis Ltd
December 2010
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=511541660&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 511541660 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00045608 AAG jtl: Annals of the Association of American Geographers issn: 00045608 maglogo: N pubinfo: dt: December 2010 vid: 100 iid: 5 pid: 377 pub: Taylor & Francis Ltd artinfo: ui: 511541660 10.1080/00045608.2010.517739 ppf: 1128 ppct: 28 formats: tig: atl: The Explicit Representation of Context in Agent-Based Models of Complex Adaptive Spatial Systems. aug: au: Tang, Wenwu Bennett, David A. su: Geographic information systems Computer simulation Intelligent agents sug: subj: Geographic information systems Computer simulation Intelligent agents ab: The dynamic behavior of complex adaptive spatial systems is driven by interactions that occur among system components. The proper representation of individual-individual and individual-environment interaction in agent-based simulations of such systems is, therefore, of paramount importance to model design. In this article we develop and implement a context-driven, agent-based modeling approach that supports the explicit representation of situation-dependent information for decision making within dynamic spatial environments. This context-driven approach is characterized by the identification of primitive contextual elements, the organization of such elements into context patterns, and contextualized learning. We synthesize two existing frameworks for the representation of spatiotemporal context and extend them to include higher level constructs and feedback mechanisms needed for learning and adaptation. Contextualized information is organized, analyzed, and used by agents to enhance their problem-solving capabilities. Dynamically changing information relevant to the decision-making processes of agents is captured in this approach and used to drive spatiotemporal learning in agents. We examine the utility of this context-driven approach via an agent-based model of elk movement. Elk in this model are represented as contextually aware intelligent agents that learn optimal movement patterns and adapt to heterogeneous landscape dynamics. The internal and external stimuli received by elk during migration are incorporated into the representation of agent context. The context-driven approach, as experimental results indicate, provides solid support for the representation of individual-centric interactions within complex adaptive spatial systems. Reprinted by permission of the publisher. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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