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Olsen %K fpf %B Ecology and Society %V 22 %G eng %0 Journal Article %J Science Findings %D 2017 %T Forests, people, fire: Integrating the sciences to build capacity for an “All Lands” approach to forest restoration %A Oliver, M. %A Susan Charnley %A Spies, Thomas A %A Kline, Jeffery D. %A White, Eric M. %K fpf %B Science Findings %G eng %9 Portland, OR: US Department of Agriculture, Forest Service, Pacific Northwest Research Station %& 5 %0 Journal Article %J Ecology and Society %D 2017 %T Integrating social science into empirical models of coupled human and natural systems %A Kline, Jeffrey D. %A White, Eric M. %A Fischer, A Paige %A Steen-Adams, Michelle M. %A Susan Charnley %A Christine S. Olsen %A Spies, Thomas A %A Bailey, John D. %K CHANS %K coupled human and natural systems %K fpf %K integrated social and ecological models %K interdisciplinary research %K wildfire %B Ecology and Society %V 22 %G eng %U https://www.ecologyandsociety.org/vol22/iss3/art25/ %& 25 %R 10.5751/ES-09329-220325 %0 Journal Article %J Ecology and Society %D 2017 %T Spatiotemporal dynamics of simulated wildfire, forest management and forest succession in central Oregon, U.S.A. %A Ana Barros %A Alan A. Ager %A Michelle Day %A H. Preisler %A Spies, Thomas A %A Eric White %A Robert J. Pabst %A Keith A. Olsen %A Platt, Emily K. %A Bailey, John D. %A John Bolte %K fpf %B Ecology and Society %V 22 %G eng %U https://www.ecologyandsociety.org/vol22/iss1/art24/ %R 10.5751/ES-08917-220124 %0 Journal Article %J Ecology and Society %D 2017 %T Using an agent-based model to examine forest management outcomes in a fire-prone landscape in Oregon, USA %A Spies, Thomas %A Eric White %A Ager, Alan %A Kline, Jeffrey D. %A John Bolte %A Platt, Emily %A Keith Olsen %A Pabst, Robert %A Ana Barros %A Bailey, John %A others %K fpf %B Ecology and Society %I The Resilience Alliance %V 22 %G eng %U https://www.ecologyandsociety.org/vol22/iss1/art25/ %R 10.5751/ES-08841-220125 KW - adaptation %0 Journal Article %J Ecology and Society %D 2014 %T Examining fire-prone forest landscapes as coupled human and natural systems %A Spies, Thomas A %A White, Eric M. %A Kline, Jeffrey D. %A Fischer, A Paige %A Ager, Alan %A Bailey, John %A John Bolte %A Koch, Jennifer %A Platt, Emily %A Christine S. Olsen %A Jacobs, Derric %A Bruce Shindler %A Steen-Adams, Michelle M. %A Hammer, Roger %K agent-based model %K CHANS %K coupled human and natural systems %K fire policy %K fire-prone landscapes %K fpf %B Ecology and Society %V 19 %G eng %U https://www.ecologyandsociety.org/vol19/iss3/art9/ %& 9 %R 10.5751/ES-06584-190309 %0 Audiovisual Material %D 2013 %T Characterizing Firewise and fuel reduction activities among private landowners %A Kline, Jeffery D. %A Christine S. Olsen %A Eric White %A Alan A. Ager %I USDA Forest Service PNW Research Station %G eng %0 Audiovisual Material %D 2011 %T Considering the Suitability of Engaging Stakeholders in Forest-Based Socio-Ecological Systems Modeling %A Erika Gorczyca %A Jessica Leahy %A Kathleen P Bell %A Jeremy Wilson %K ISSRM %I International Symposium on Society and Resource Management %C Madison, WI %8 06/2011 %G eng %U http://fpf.forestry.oregonstate.edu/sites/default/files/documents/ISSRM_2011_Gorczyca_Final.pdf %0 Journal Article %J Ecology and Society %D 2010 %T Evaluating Today’s Landscape Multifunctionality and Providing an Alternative Future: A Normative Scenario Approach %A Rainer Waldhardt %A Martin Bach %A René Borresch %A Lutz Breuer %A Tim Diekötter %A Hans-Georg Frede %A Stefan Gäth %A Oliver Ginzler %A Thomas Gottschalk %A Stefan Julich %A Matthias Krumpholz %A Friedrich Kuhlmann %A Annette Otte %A Birgit Reger %A Wolfgang Reiher %A Kim Schmitz %A P. 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Fishers compete in two ways: in a scramble to find the lobsters first and by directly interfering in other fishers’ ability to compete, i.e., by cutting their traps. Both forms of competition lead fishers to interact frequently and to self-organize into relatively small groups. They learn to restrain their competitive behavior toward their neighbors but do not extend that same restraint to nonneighbors. Groups work within well defined boundaries, contact one another frequently, actively exchange information about the resource, and, most importantly, depend on continuing mutual restraint for their economic well-being. These self-organizing, competitive processes lay the foundation for successful collective action, i.e., mutual agreements that create the additional restraint required for conservation. (truncated from original) %B Proceedings of the National Academy of Sciences of the United States of America %I National Academy of Sciences %V 104 %P pp. 15212-15217 %G eng %U http://www.jstor.org/stable/25449115 %0 Journal Article %J Fire Management Today %D 2006 %T Changing Beliefs and Building Trust at the Wildland/Urban Interface %A Jeremy S. Fried %A Demetrios Gatziolis %A J. Keith Gilless %A Christine A. Vogt %A Greg Winter %B Fire Management Today %V 66 %P 51-54 %G eng %0 Magazine Article %D 2005 %T Pattern-oriented modeling of agent-based complex systems: lessons from ecology. %A Grimm,Volker %A Revilla,Eloy %A Berger,Uta %A Jeltsch,Florian %A Mooij, Wolf M. %A Railsback, Steven F. %A Thulke,Hans-Hermann %A Weiner,Jacob %A Wiegand,Thorsten %A DeAngelis, Donald L. %K Analysis %K Ecological research_Analysis %K Ecological research_Methods %K Methods %X

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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