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Pattern-Oriented Modeling of Agent-Based Complex Systems: Lessons from Ecology

Volker GrimmInstitut für Biochemie und Biologie, Universität Potsdam, Maulbeerallee 2, 14469 Potsdam, GermanyEloy RevillaNetherlands Institute of Ecology, Centre for Limnology, Rijksstraatweg 6, 3631 AC Nieuwersluis, NetherlandsUta BergerDepartment of Applied Biology, Estación Biológica de Doñana, Spanish Council for Scientific Research CSIC, Ave. Maria Luisa s/n, E-41013 Seville, SpainFlorian JeltschInstitut für Biochemie und Biologie, Universität Potsdam, Maulbeerallee 2, 14469 Potsdam, GermanyWolf M. MooijInstitut für Biochemie und Biologie, Universität Potsdam, Maulbeerallee 2, 14469 Potsdam, GermanySteven F. RailsbackNetherlands Institute of Ecology, Centre for Limnology, Rijksstraatweg 6, 3631 AC Nieuwersluis, NetherlandsHans‐Hermann ThulkeZentrum für Marine Tropenökologie, Fahrenheitstrasse 6, 28359 Bremen, GermanyJacob WeinerZentrum für Marine Tropenökologie, Fahrenheitstrasse 6, 28359 Bremen, GermanyThorsten WiegandDepartment of Applied Biology, Estación Biológica de Doñana, Spanish Council for Scientific Research CSIC, Ave. Maria Luisa s/n, E-41013 Seville, SpainDonald L. DeAngelisInstitut für Biochemie und Biologie, Universität Potsdam, Maulbeerallee 2, 14469 Potsdam, Germany

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Agent-based complex systems are dynamic networks of many interacting agents; examples include ecosystems, financial markets, and cities. The search for general principles underlying the internal organization of such systems often uses bottom-up simulation models such as cellular automata and agent-based models. No general framework for designing, testing, and analyzing bottom-up models has yet been established, but recent advances in ecological modeling have come together in a general strategy we call pattern-oriented modeling. This strategy provides a unifying framework for decoding the internal organization of agent-based complex systems and may lead toward unifying algorithmic theories of the relation between adaptive behavior and system complexity.

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DOI: 10.1126/science.1116681

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