Levels of emergence in individual based models: Coping with scarcity of data and pattern redundancy

Abstract Pattern Oriented Modelling (POM) is a well-known framework for designing, parameterizing, and analyzing individual based models (IBM). It assesses individual or agent based complex systems by applying inverse modelling on patterns observed in the real system of interest. However, scarcity of field data is an underlying problem in many environmental modelling projects and the relationship between the amount of available data and the domain of applicability of IBMs has hardly been considered. We argue that choosing the appropriate decision process, and the traits that represent it, when modelling individuals is fundamental for the model to be able to generalize when few data are available, and therefore not representative of the intended domain of applicability. A pattern being the expression of a given comportment, we define the notion of level of emergence, and propose to order comportments according to these levels. This approach may aid in selecting the appropriate trait for generalization when designing an IBM. We also show how levels of emergence can be used to assess the redundancy in patterns. A pattern is redundant with respect to other patterns when it does not bring new information about the process which generates it. Since POM makes use of patterns to calibrate the model, one should ensure that the considered patterns are independent, because redundancy might lead to non-optimal results. Our recommendations thus provide a way of avoiding some of the potential pitfalls of the POM framework and contribute to the on-going development of a standard approach for individual-based modelling.

[1]  M. Boyce,et al.  WOLVES INFLUENCE ELK MOVEMENTS: BEHAVIOR SHAPES A TROPHIC CASCADE IN YELLOWSTONE NATIONAL PARK , 2005 .

[2]  John H. Holland,et al.  Cognitive systems based on adaptive algorithms , 1977, SGAR.

[3]  O. François,et al.  Approximate Bayesian Computation (ABC) in practice. , 2010, Trends in ecology & evolution.

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

[5]  G. Huse.  Individual‐based Modeling and Ecology , 2008 .

[6]  C. Clark,et al.  Towards a Unifield Foraging Theory , 1986 .

[7]  Daniel Fortin,et al.  Adaptive models for large herbivore movements in heterogeneous landscapes , 2005, Landscape Ecology.

[8]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[9]  Steven F. Railsback,et al.  Concepts from complex adaptive systems as a framework for individual-based modelling , 2001 .

[10]  Atle Mysterud,et al.  FUNCTIONAL RESPONSES IN HABITAT USE: AVAILABILITY INFLUENCES RELATIVE USE IN TRADE-OFF SITUATIONS , 1998 .

[11]  Juan M. Morales,et al.  EXTRACTING MORE OUT OF RELOCATION DATA: BUILDING MOVEMENT MODELS AS MIXTURES OF RANDOM WALKS , 2004 .

[12]  M. Boyce,et al.  Elk winter foraging at fine scale in Yellowstone National Park , 2005, Oecologia.

[13]  Damaris Zurell,et al.  The virtual ecologist approach: simulating data and observers , 2010 .

[14]  George L. W. Perry,et al.  Dispersal of organisms in a patchy stream environment under different settlement scenarios , 2000 .

[15]  Thomas G. Dietterich.  Overfitting and undercomputing in machine learning , 1995, CSUR.

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

[17]  Jack P. C. Kleijnen,et al.  EUROPEAN JOURNAL OF OPERATIONAL , 1992 .

[18]  J. A. Schaefer,et al.  Caribou movement as a correlated random walk , 2000, Oecologia.

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

[20]  Steven F. Railsback,et al.  GETTING “RESULTS”: THE PATTERN‐ORIENTED APPROACH TO ANALYZING NATURAL SYSTEMS WITH INDIVIDUAL‐BASED MODELS , 2001 .

[21]  Edward J. Rykiel,et al.  Testing ecological models: the meaning of validation , 1996 .

[22]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .

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

[24]  Volker Grimm,et al.  Patterns for parameters in simulation models , 2007 .