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

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.

[1]  Steven F. Railsback,et al.  Individual-based modeling and ecology , 2005 .

[2]  宁北芳,et al.  疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A , 2005 .

[3]  François Bousquet,et al.  A Multi-Agent Model for Describing Transhumance in North Cameroon: Comparison of Different Rationality to Develop a Routine , 2001 .

[4]  G. Huse,et al.  Modelling changes in migration pattern of herring: collective behaviour and numerical domination , 2002 .

[5]  David R. Anderson,et al.  Model selection and multimodel inference : a practical information-theoretic approach , 2003 .

[6]  S. Peck.  Simulation as experiment: a philosophical reassessment for biological modeling. , 2004, Trends in ecology & evolution.

[7]  Andreas Huth,et al.  THE SIMULATION OF FISH SCHOOLS IN COMPARISON WITH EXPERIMENTAL DATA , 1994 .

[8]  H. Remmert,et al.  The Mosaic-Cycle Concept of Ecosystems , 1991, Ecological Studies.

[9]  Karin Frank,et al.  Pattern-oriented modelling in population ecology , 1996 .

[10]  James D. Watson,et al.  The Double Helix: A Personal Account of the Discovery of the Structure of DNA , 1968 .

[11]  J. Platt.  Strong Inference: Certain systematic methods of scientific thinking may produce much more rapid progress than others. , 1964, Science.

[12]  Philip W. Hedrick,et al.  Genetics, Demography and Viability of Fragmented Populations: Genetic population structure in desert bighorn sheep: implications for conservation in Arizona , 2000 .

[13]  A. Karma,et al.  Evolution of nanoporosity in dealloying , 2001, Nature.

[14]  Andrew G. Young,et al.  Being around to Pick up the Pieces@@@Genetics, Demography and Viability of Fragmented Populations , 2001 .

[15]  Volker Grimm,et al.  Using pattern-oriented modeling for revealing hidden information: a key for reconciling ecological theory and application , 2003 .

[16]  S. Auyang.  Foundations of Complex-System Theories: In Economics, Evolutionary Biology, and Statistical Physics , 1998 .

[17]  Friedrich Georgi.  Forstwissenschaftliches Centralblatt , 1991, Forstwissenschaftliches Centralblatt vereinigt mit Tharandter forstliches Jahrbuch.

[18]  Joshua M. Epstein,et al.  Growing artificial societies , 1996 .

[19]  Craig Loehle,et al.  A guide to increased creativity in research — inspiration or perspiration? , 1990 .

[20]  Steven C Bankes,et al.  Agent-based modeling: A revolution? , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[21]  Donald L. DeAngelis,et al.  UNCERTAINTY IN SPATIALLY EXPLICIT ANIMAL DISPERSAL MODELS , 2003 .

[22]  Steven F. Railsback,et al.  ANALYSIS OF HABITAT‐SELECTION RULES USING ANINDIVIDUAL‐BASED MODEL , 2002 .

[23]  Bernard P. Zeigler,et al.  Theory of modeling and simulation , 1976 .

[24]  C. Tomlin,et al.  Mathematical Modeling of Planar Cell Polarity to Understand Domineering Nonautonomy , 2005, Science.

[25]  David R. Anderson,et al.  Model selection and inference : a practical information-theoretic approach , 2000 .

[26]  Blake LeBaron,et al.  Empirical regularities from interacting long- and short-memory investors in an agent-based stock market , 2001, IEEE Trans. Evol. Comput..

[27]  John Alroy,et al.  A Multispecies Overkill Simulation of the End-Pleistocene Megafaunal Mass Extinction , 2001, Science.

[28]  S. Korpel,et al.  Die Urwälder der Westkarpaten , 1995 .

[29]  Robert L. Axtell,et al.  Population growth and collapse in a multiagent model of the Kayenta Anasazi in Long House Valley , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[30]  Scott Moss,et al.  Policy analysis from first principles , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[31]  Aravind Srinivasan,et al.  Modelling disease outbreaks in realistic urban social networks , 2004, Nature.

[32]  Paul W. Box,et al.  An individual-based model of canid populations: modelling territoriality and social structure , 2003 .

[33]  S. Goldhor.  Ecology , 1964, The Yale Journal of Biology and Medicine.

[34]  Susanne Winter,et al.  Totholz im Buchen-Urwald: Generische Vorhersagen des Simulationsmodells BEFORECWD zur Menge, räumlichen Verteilung und Verfügbarkeit , 2003, Forstwissenschaftliches Centralblatt vereinigt mit Tharandter forstliches Jahrbuch.

[35]  David L Smith,et al.  A priori prediction of disease invasion dynamics in a novel environment , 2004, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[36]  Christian Wissel,et al.  Modelling the mosaic cycle of a Middle European beech forest , 1992 .

[37]  Joshua M. Epstein,et al.  Growing Artificial Societies: Social Science from the Bottom Up , 1996 .

[38]  J. Timothy Wootton,et al.  Local interactions predict large-scale pattern in empirically derived cellular automata , 2001, Nature.

[39]  Christian Wissel,et al.  Was charakterisiert Buchenurwälder? Untersuchungen der Altersstruktur des Kronendachs und der räumlichen Verteilung der Baumriesen in einem Modellwald mit Hilfe des Simulationsmodells BEFORE , 2001, Forstwissenschaftliches Centralblatt vereinigt mit Tharandter forstliches Jahrbuch.

[40]  C. Wissel,et al.  Pattern formation triggered by rare events: lessons from the spread of rabies , 1997, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[41]  D. Dennett.  Darwin's Dangerous Idea: Evolution and the Meanings of Life , 1995 .

[42]  Thorsten Wiegand,et al.  Dealing with Uncertainty in Spatially Explicit Population Models , 2004, Biodiversity & Conservation.

[43]  Joshua M. Epstein,et al.  Growing Artificial Societies: Social Science from the Bottom Up , 1996 .

[44]  Miguel Delibes,et al.  Effects of Matrix Heterogeneity on Animal Dispersal: From Individual Behavior to Metapopulation‐Level Parameters , 2004, The American Naturalist.

[45]  Frank Schweitzer,et al.  Brownian Agents and Active Particles: Collective Dynamics in the Natural and Social Sciences , 2003 .

[46]  Andrew G. Young,et al.  Genetics, Demography and Viability of Fragmented Populations: Introductory concepts , 2000 .

[47]  Florian Jeltsch,et al.  From pattern to practice: a scaling-down strategy for spatially explicit modelling illustrated by the spread and control of rabies , 1999 .

[48]  K. Tamura,et al.  Metabolic engineering of plant alkaloid biosynthesis. Proc Natl Acad Sci U S A , 2001 .

[49]  Robert Lempert,et al.  Agent-based modeling as organizational and public policy simulators , 2002, Proceedings of the National Academy of Sciences of the United States of America.