Comparison of empirical methods for building agent-based models in land use science

The use of agent-based models (ABMs) for investigating land-use science questions has been increasing dramatically over the last decade. Modelers have moved from ‘proofs of existence’ toy models to case-specific, multi-scaled, multi-actor, and data-intensive models of land-use and land-cover change. An international workshop, titled ‘Multi-Agent Modeling and Collaborative Planning—Method2Method Workshop’, was held in Bonn in 2005 in order to bring together researchers using different data collection approaches to informing agent-based models. Participants identified a typology of five approaches to empirically inform ABMs for land use science: sample surveys, participant observation, field and laboratory experiments, companion modeling, and GIS and remotely sensed data. This paper reviews these five approaches to informing ABMs, provides a corresponding case study describing the model usage of these approaches, the types of data each approach produces, the types of questions those data can answer, and an evaluation of the strengths and weaknesses of those data for use in an ABM.

[1]  K. Shadan,et al.  Available online: , 2012 .

[2]  O. Bagasra,et al.  Proceedings of the National Academy of Sciences , 1914, Science.

[3]  Proceedings of the National Academy , 1915, Botanical Gazette.

[4]  H. Couclelis A Theoretical Framework for Alternative Models of Spatial Decision and Behavior , 1986 .

[5]  Cheng Hsiao,et al.  Analysis of Panel Data , 1987 .

[6]  A. Strauss,et al.  Basics of Qualitative Research , 1992 .

[7]  A. Strauss,et al.  Basics of qualitative research: Grounded theory procedures and techniques. , 1992 .

[8]  W. Groot Environmental Science Theory: Concepts and Methods in a One-World, Problem-Oriented Paradigm , 1992 .

[9]  A. Case Neighborhood influence and technological change , 1992 .

[10]  Pamela Jordan Basics of qualitative research: Grounded theory procedures and techniques , 1994 .

[11]  W. Arthur Inductive Reasoning and Bounded Rationality , 1994 .

[12]  S. Funtowicz,et al.  Environmental science theory: Concepts and methods in a one-World, problem-oriented paradigm: Wouter T. de Groot. Elsevier, Amsterdam, 1992, 584 pp. ISBN 0-444-88993-0, Dfl. 360.00 , 1994 .

[13]  John R. Koza,et al.  Hidden Order: How Adaptation Builds Complexity. , 1995, Artificial Life.

[14]  R. Chambers Whose Reality Counts?: Putting the First Last , 1997 .

[15]  E. Ostrom A Behavioral Approach to the Rational Choice Theory of Collective Action: Presidential Address, American Political Science Association, 1997 , 1998, American Political Science Review.

[16]  M. Grosh,et al.  Designing Household Survey Questionnaires for Developing Countries : Lessons from Ten Years of LSMS Experience , 1999 .

[17]  P. Stern,et al.  People and pixels : linking remote sensing and social science , 1999 .

[18]  Mark Persoff UK , 1999, EC Tax Review.

[19]  Peter Deadman,et al.  Modelling individual behaviour and group performance in an intelligent agent-based simulation of the tragedy of the commons , 1999 .

[20]  A A Schuessler,et al.  Ecological inference. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[21]  Michael X Cohen,et al.  Harnessing Complexity: Organizational Implications of a Scientific Frontier , 2000 .

[22]  M. Grosh,et al.  Designing Household Survey Questionnaires for Developing Countries: Lessons from 15 Years of the Living Standards Measurement Study, Volume 3 , 2000 .

[23]  M. Janssen,et al.  Multi-Agent Systems for the Simulation of Land-Use and Land-Cover Change: A Review , 2003 .

[24]  E. Lambin,et al.  Land-Cover-Change Trajectories in Southern Cameroon , 2000 .

[25]  J. Cárdenas,et al.  Local environmental control and institutional crowding-out. , 2000 .

[26]  Carl Henning Reschke Evolutionary Perspectives on Simulations of Social Systems , 2001, J. Artif. Soc. Soc. Simul..

[27]  Thomas Berger,et al.  Agent-based spatial models applied to agriculture: A simulation tool , 2001 .

[28]  E. Irwin,et al.  Interacting agents, spatial externalities and the evolution of residential land use patterns , 2002 .

[29]  Timothy Evans,et al.  A Review and Assessment of Land-Use Change Models Dynamics of Space, Time, and Human Choice , 2002 .

[30]  M. Janssen,et al.  Using artificial agents to understand laboratory experiments of common-pool resources with real agents , 2002 .

[31]  François Bousquet,et al.  Collective creation of artificial worlds can help govern concrete natural resource management problems: a northern Thailand experience , 2002 .

[32]  Colin Camerer Behavioral Game Theory: Experiments in Strategic Interaction , 2003 .

[33]  M. Janssen,et al.  Multi-Agent Systems for the Simulation of Land-Use and Land-Cover Change: A Review , 2003 .

[34]  V.E.Adler,et al.  Q4 , 2003, nlin/0309030.

[35]  K. Seto,et al.  Modeling the Drivers of Urban Land Use Change in the Pearl River Delta, China: Integrating Remote Sensing with Socioeconomic Data , 2003, Land Economics.

[36]  U. Purintrapiban,et al.  SYNERGIES BETWEEN MULTI-AGENT SYSTEMS AND ROLE-PLAYING GAMES IN COMPANION MODELING FOR INTEGRATED NATURAL RESOURCE MANAGEMENT IN SOUTHEAST ASIA , 2004 .

[37]  K. Happe Agricultural policies and farm structures: agent-based modelling and application to EU-policy reform , 2004 .

[38]  Hugh Kelley,et al.  Multi-scale analysis of a household level agent-based model of landcover change. , 2004, Journal of environmental management.

[39]  E. Ostrom,et al.  WHAT DO PEOPLE BRING INTO THE GAME: EXPERIMENTS IN THE FIELD ABOUT COOPERATION IN THE COMMONS , 2004 .

[40]  Colin Camerer,et al.  Foundations of Human Sociality - Economic Experiments and Ethnographic: Evidence From Fifteen Small-Scale Societies , 2004 .

[41]  Eduardo S Brondízio,et al.  Colonist Household Decisionmaking and Land-Use Change in the Amazon Rainforest: An Agent-Based Simulation , 2004 .

[42]  John Duffy,et al.  Agent-Based Models and Human Subject Experiments , 2004 .

[43]  Marco G A Huigen,et al.  First principles of the MameLuke multi-actor modelling framework for land use change, illustrated with a Philippine case study. , 2004, Journal of environmental management.

[44]  Thomas Berger,et al.  Empirical Parameterization of Multi-Agent Models in Applied Development Research , 2005 .

[45]  Mark Rounsevell,et al.  Exploring a spatio‐dynamic neighbourhood‐based model of residential behaviour in the Brussels periurban area , 2005, Int. J. Geogr. Inf. Sci..

[46]  François Bousquet,et al.  SYNERGIES BETWEEN MULTI-AGENT SYSTEMS AND ROLE-PLAYING GAMES IN COMPANION MODELING FOR INTEGRATED NATURAL RESOURCE MANAGEMENT IN SOUTHEAST ASIA , 2005 .

[47]  Daniel Castillo,et al.  Simulation of common pool resource field experiments: a behavioral model of collective action , 2005 .

[48]  G. Deffuant,et al.  An Individual‐Based Model of Innovation Diffusion Mixing Social Value and Individual Benefit1 , 2005, American Journal of Sociology.

[49]  Uta Berger,et al.  Pattern-Oriented Modeling of Agent-Based Complex Systems: Lessons from Ecology , 2005, Science.

[50]  François Bousquet,et al.  Companion modelling to support collective land management in the highlands of Northern Thailand , 2005 .

[51]  T. Downing,et al.  Multi-agent modelling of climate outlooks and food security on a community garden scheme in Limpopo, South Africa , 2005, Philosophical Transactions of the Royal Society B: Biological Sciences.

[52]  Thomas Berger,et al.  Research, part of a Special Feature on Empirical based agent-based modeling Creating Agents and Landscapes for Multiagent Systems from Random Samples , 2006 .

[53]  Robert L. Goldstone,et al.  Group path formation , 2006, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[54]  Wenjie Sun,et al.  Spatially explicit experiments for the exploration of land‐use decision‐making dynamics , 2006, Int. J. Geogr. Inf. Sci..

[55]  Daniel G. Brown,et al.  Effects of Heterogeneity in Residential Preferences on an Agent-Based Model of Urban Sprawl , 2006 .

[56]  Thomas Berger,et al.  Land use decisions in developing countries and their representation in multi-agent systems , 2006 .

[57]  R. Pontius,et al.  Modeling Land-Use and Land-Cover Change , 2006 .

[58]  E. Ostrom,et al.  Empirically Based, Agent-based models , 2006 .

[59]  K. Overmars,et al.  Research, part of a Special Feature on Empirical based agent-based modeling Multiactor Modeling of Settling Decisions and Behavior in the San Mariano Watershed, the Philippines: a First Application with the MameLuke Framework , 2006 .

[60]  M. Janssen,et al.  Learning, Signaling, and Social Preferences in Public-Good Games , 2006 .

[61]  D. Parker,et al.  The geography of market failure: Edge-effect externalities and the location and production patterns of organic farming , 2007 .

[62]  Daniel A. Levinthal,et al.  Exploration and Exploitation in Organizational Learning , 2007 .

[63]  Scott E. Page,et al.  Agent-Based Models , 2014, Encyclopedia of GIS.

[64]  Daniel G. Brown,et al.  Illustrating a new conceptual design pattern for agent-based models of land use via five case studies—the MR POTATOHEAD framework , 2008 .