Further towards a taxonomy of agent-based simulation models in environmental management

Agent-based simulation (ABS) is being increasingly used in environmental management. However, the efficient and effective use of ABS for environmental modelling is hindered by the fact that there is no fixed and clear definition of what an ABS is or even what an agent should be. Terminology has proliferated and definitions of agency have been drawn from an application area (Distributed Artificial Intelligence) which is not wholly relevant to the task of environmental simulation. This situation leaves modellers with little practical support for clearly identifying ABS techniques and how to implement them.This paper is intended to provide an overview of agent-based simulation in environmental modelling so that modellers can link their requirements to the current state of the art in the techniques that are currently used to satisfy them. Terminology is clarified and then simplified to two key existing terms, agent-based modelling and multi-agent simulation, which represent subtly different approaches to ABS, reflected in their respective artificial life (A-life) and distributed artificial intelligence roots. A representative set of case studies are reviewed, from which a classification scheme is developed as a stepping-stone to developing a taxonomy. The taxonomy can then be used by modellers to match ABS techniques to their requirements.

[1]  Nicholas Mark Gotts,et al.  What every agent-based modeller should know about floating point arithmetic , 2006, Environ. Model. Softw..

[2]  F. Bousqueta,et al.  Multi-agent simulations and ecosystem management : a review , 2004 .

[3]  Roger Strand,et al.  Can agent-based models assist decisions on large-scale practical problems? A philosophical analysis , 2000, Complex..

[4]  F. Guerrin,et al.  Biomas : un modèle multi-agents pour aider à la gestion négociée d'effluents d'élevage , 1999 .

[5]  Jim Doran.  Intervening to Achieve Co-operative Ecosystem Management: Towards an Agent Based Model , 2001, J. Artif. Soc. Soc. Simul..

[6]  Jacques Ferber,et al.  Multi-agent systems - an introduction to distributed artificial intelligence , 1999 .

[7]  Edmund Chattoe-Brown.  Just How (Un)realistic Are Evolutionary Algorithms As Representations of Social Processes , 1998 .

[8]  François Bousquet,et al.  SHADOC: a multi‐agent model to tackle viability of irrigated systems , 2000, Ann. Oper. Res..

[9]  G. Nigel Gilbert,et al.  Simulation for the social scientist , 1999 .

[10]  G. Booth,et al.  BacSim, a simulator for individual-based modelling of bacterial colony growth. , 1998, Microbiology.

[11]  E. Bonabeau,et al.  Why do we need artificial life , 1994 .

[12]  Bruce Edmonds,et al.  The Use of Models - Making MABS More Informative , 2000, MABS.

[13]  Peter Deadman,et al.  Agent-based simulations of household decision making and land use change near Altamira , 2001 .

[14]  Arthur C. Graesser,et al.  Is it an Agent, or Just a Program?: A Taxonomy for Autonomous Agents , 1996, ATAL.

[15]  C. Vlek,et al.  Consumats in a commons dilemma Testing the behavioural rules of simulated consumers Centre for Environmental and Traffic Psychology , 2022 .

[16]  C. Langton.  Artificial Life as a Tool for Biological Inquiry , 1997 .

[17]  Leigh Tesfatsion,et al.  Agent-Based Computational Economics: Growing Economies From the Bottom Up , 2002, Artificial Life.

[18]  J. Lansing,et al.  Emergent Properties of Balinese Water Temple Networks: Coadaptation on a Rugged Fitness Landscape , 1993 .

[19]  C. H. Dagli,et al.  Emergence and artificial life , 2003, IEMC '03 Proceedings. Managing Technologically Driven Organizations: The Human Side of Innovation and Change.

[20]  O P Judson,et al.  The rise of the individual-based model in ecology. , 1994, Trends in ecology & evolution.

[21]  Guy Theraulaz,et al.  Why Do We Need Artificial Life? , 1994, Artificial Life.

[22]  Nicholas R. Jennings,et al.  Intelligent agents: theory and practice , 1995, The Knowledge Engineering Review.

[23]  Randy Gimblett,et al.  An Intelligent Agent Based Model for Simulating and Evaluating River Trip Scenarios Along the Colorado River in Grand Canyon National Park , 2002 .

[24]  Marco A. Janssen,et al.  An adaptive agent model for analysing co-evolution of management and policies in a complex rangeland system , 2000 .

[25]  Timothy A. Kohler,et al.  Dynamics in human and primate societies: agent-based modeling of social and spatial processes , 2000 .

[26]  Nicholas Mark Gotts,et al.  IMITATIVE VERSUS NONIMITATIVE STRATEGIES IN A LAND-USE SIMULATION , 2001, Cybern. Syst..

[27]  V. Grimm.  Ten years of individual-based modelling in ecology: what have we learned and what could we learn in the future? , 1999 .

[28]  G. Booth.  Gecko: A continuous 2d world for ecological modeling , 1997 .

[29]  Olivier Barreteau,et al.  How bad Isn't the agent-based model CATCHSCAPE? , 2002 .

[30]  Claudia Pahl-Wostl,et al.  Understanding Climate Policy Using Participatory Agent-Based Social Simulation , 2000, MABS.

[31]  Jim Doran,et al.  From computer simulation to artificial societies , 1997 .

[32]  A. N. R. Law,et al.  A system for monitoring land cover , 2003 .

[33]  Roger Strand,et al.  Can agent-based models assist decisions on large-scale practical problems? A philosophical analysis , 2000, Complex..

[34]  Marco A. Janssen,et al.  An exploratory integrated model to assess management of lake eutrophication , 2001 .

[35]  Paul Davidsson,et al.  Multi Agent Based Simulation: Beyond Social Simulation , 2000, MABS.

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