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Agent-Based Modelling and Simulation Applied to Environmental Management

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Simulating Social Complexity

Part of the book series: Understanding Complex Systems ((UCS))

Why Read This Chapter?

To understand the recent shift of paradigms prevailing in both environmental modelling and renewable resources management that led to the emerging rise in the application of ABMS. Also, to learn about a practical way to characterize applications of ABMS to environmental management and to see this framework applied to review a selection of recent applications of ABMS from various fields related to environmental management including the dynamics of land use changes, water, forest and wildlife management, agriculture, livestock productions and epidemiology.

Abstract

The purpose of this chapter is to summarize how agent-based modelling and simulation (ABMS) is being used in the area of environmental management. With the science of complex systems now being widely recognized as an appropriate one to tackle the main issues of ecological management, ABMS is emerging as one of the most promising approaches. To avoid any confusion and disbelief about the actual usefulness of ABMS, the objectives of the modelling process have to be unambiguously made explicit. It is still quite common to consider ABMS as mostly useful to deliver recommendations to a lone decision-maker, yet a variety of different purposes have progressively emerged, from gaining understanding through raising awareness, facilitating communication, promoting coordination or mitigating conflicts. Whatever the goal, the description of an agent-based model remains challenging. Some standard protocols have been recently proposed, but still a comprehensive description requires a lot of space, often too much for the maximum length of a paper authorized by a scientific journal. To account for the diversity and the swelling of ABMS in the field of ecological management, a review of recent publications based on a lightened descriptive framework is proposed. The objective of these descriptions is not to allow the replication of the models but rather to characterize the types of spatial representation, the properties of the agents, the features of the scenarios that have been explored, and also to mention which simulation platforms were used to implement them (if any). This chapter concludes with a discussion of recurrent questions and stimulating challenges currently faced by ABMS for environmental management.

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Authors and Affiliations

  1. CIRAD, UPR47 Green, F-34398, Montpellier, France

    Christophe Le Page, Didier Bazile, Pierre Bommel, François Bousquet & Guy Trébuil

  2. CNRS, UMR8586 Prodig, 75005, Paris, France

    Nicolas Becu

  3. PUC University, Rio de Janeiro, Brazil

    Pierre Bommel

  4. INRA, Unité d’Ecodéveloppement, Site Agroparc, 84914, Avignon cedex 9, Avignon, France

    Michel Etienne

  5. Centre d’Ecologie Fonctionnelle et Evolutive, UMR 5175 – CNRS 1919, Route de Mende, F34293, Montpellier, France

    Raphael Mathevet

  6. UMR SADAPT INRA AgroParisTech, 1, 78850, Thiverval Grignon, France

    Véronique Souchère

  7. CIRAD, Direction Régionale Ile de France, 42 rue Scheffer, F-75116, Paris, France

    Jacques Weber

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Corresponding author

Correspondence to Christophe Le Page .

Editor information

Editors and Affiliations

  1. CPM - Centre for Policy Modelling, Manchester Metropolitan University, Manchester, M1 3GH, United Kingdom

    Bruce Edmonds

  2. , Center for Policy Modelling, Manchester Metropolitan University, Aytoun Street, Manchester, M1 3GH, United Kingdom

    Ruth Meyer

Appendices

Further Reading

  1. 1.

    The special issue of JASSS in 2001Footnote 1 on “ABM, Game Theory and Natural Resource Management issues” presents a set of papers selected from a workshop held in Montpellier in March 2000, most of them dealing with collective decision-making processes in the field of natural resource management and environment.

  2. 2.

    Gimblett (2002) is a book on integrating GIS and ABM, derived from a workshop held in March 1998 at the Santa Fe Institute. It provides contributions from computer scientists, geographers, landscape architects, biologists, anthropologists, social scientists and ecologists focusing on spatially explicit simulation modelling with agents.

  3. 3.

    Janssen (2002) provides a state-of-the-art review of the theory and application of multi-agent systems for ecosystem management and addresses a number of important topics including the participatory use of models. For a detailed review of this book see Terna (2005).

  4. 4.

    Paredes and Iglesias (2008) advocate why agent based simulations provide a new and exciting avenue for natural resource planning and management: researches and advisers can compare and explore alternative scenarios and institutional arrangements to evaluate the consequences of policy actions in terms of economic, social and ecological impacts. But as a new field it demands from the modellers a great deal of creativeness, expertise and “wise choice”, as the papers collected in this book show.

Appendix

Publications

Model name

Topic

Issue

Environment

Agents

Software

Polhill et al. 2001

FEARLUS

LUCC

Land-use and land ownership dynamics

Raster 7,7 region

Land manager (49) HeB(10) Ie

Swarm

Brown et al. 2004

 

LUCC

Greenbelt to control residential development

Raster X,80 suburb

Resident (10) Ho Ie R

Swarm

Castella et al. 2005a

SAMBA (Generic)

LUCC

LU changes under decollecti vization in north Vietnam

Raster 50,50 (25 Ha) commune level (Abstract)

Household (50) HeP Ie R

Cormas

Castella et al. 2005b

SAMBA-GIS (More realistic)

LUCC

LU changes under decollectivization in north Vietnam

Raster regional level (More realistic)

Household (x) HeP Ie R (x = nb household in each village)

Cormas ArcView

D’Aquino et al. 2003

SelfCormas

LUCC; agriculture; livestock

Competing rangeland and rice cropping land-uses in Senegal

Raster 20,20 (5Ha) village

Farmer (20) HeB(2) Ie R

Cormas

Etienne et al. 2003

Mejan

LUCC; livestock; forestry; wildlife

Pine encroachment on original landscapes in France

Raster 23793 (1 ha) plateau

Farmer (40) HeB Ie,Ii,Ic C forester (2) HeB Ie,Ii,Ic R national park (1) Ho Ie,Ii,Ic C

Cormas MapInfo

Mathevet et al. 2003a

GEMACE

LUCC; agriculture; wildlife

Competing hunting and hunting activities in Camargue (South of France)

Raster region

Farmer Ie, Ii hunting manager Ie, Ii

Cormas

Barreteau and Bousquet 2000; Barreteau et al. 2004

SHADOC

Water; agriculture

Viability of irrigated systems in the Senegal river valley

 

Farmer

 

Le Bars et al. 2005

Manga

Water

  

Farmer (n) Ie,Ii C water supplier (1) Ic C

 

Feuillette et al. 2003

Sinuse

Water; agriculture

Water table level

Raster 2400 (1Ha) watershed

Farmer HeP Ie,Ii,Ic

Cormas

Lansing and Kremer 1993; Janssen 2007

 

Water; agriculture

Water management and water temple networks in Bali

Network 172 watershed

Village (172) HeB(49) Ie,Ic R

 

Raj Gurung et al. 2006

Limbukha

Water management

Negotiation of irrigation water sharing between two Bhutanese communities

Grid 8,13 (10.4 Ha) (Abstract) Village

Farmer (12) HeB Ii

Cormas

Hoffmann et al. 2002

LUCIM

Forestry; LUCC

Deforestation and afforestation in Indiana USA

Raster 100 state

Farmer (10) HeB Ie R

 

Purnomo and Guizol 2006

 

Forestry

Forest plantation comanagement in Indonesia

Raster 50,50 () Forest massif

Developer Ie,Ii,Ic Smallholder Ie,Ii,Ic C Broker Ie,Ii,Ic government Ie,Ii,Ic

 

Mathevet et al. 2003b

ReedSim

Wildlife

Water management in Mediterranean reedbeds

  

Cormas

An et al. 2005

 

Wildlife

Impact of the growing rural population on the forests and panda habitat in China

   

Bousquet et al. 2001

Djemiong

Wildlife

Traditional hunting of small antelopes in Cameroon

Raster 2042 (4Ha) village

Hunter (90) HeP Ie,Ic R antelop (7350) HeB(3) Ie,Ic R

Cormas

Anwar et al. 2007

 

Wildlife

Interactions between whale-watching boats and whales in the St. Lawrence estuary

Raster

Boat C whale R

Repast

Elliston and Beare 2006

 

Agriculture; epidemiology

Agricultural pest and disease incursions in Australia

  

Cormas

Happe et al. 2006

AgriPolis

Agriculture; lifestock

Policy impact on structural changes of W. Europe farms

Raster 73439 (1Ha) region

Farms (2869) HeP Ic

 

Barnaud et al. (forthcoming)

MaeSalaep 2.2

Agriculture; LUCC

LU strategies in transitional swidden agricultural systems, Thailand

Raster (300 Ha) (Realistic) village catchment

Farmer (12) HeP Ii R credit sources (3)

Cormas

Gross et al. 2006

 

Lifestock

Evaluation of behaviours of rangeland systems in Australia

 

Entreprise C government

 

Courdier et al. 2002

Biomas

Livestock; agriculture

Collective management of animal wastes in La Reunion

Network region

Livestock farm (48) HeP Ie,Ii,Ic crop farm (59) HeP Ie,Ii,Ic shipping agent (34) HeP Ii

Geamas

Bagni et al. 2002

 

Epidemiology; livestock

Evaluation of sanitary policies to control the spread of a viral pathology of bovines

Abstract farm

Farm sector Cow

Swarm

Rateb et al. 2005

 

Epidemiology

Impact of education on malaria healthcare in Haiti

Raster country

Inhabitant HeP R

StarLogo

Muller et al. 2004

 

Epidemiology; agriculture; livestock

Risk and control strategies of African Trypanosomiasis in Southern Cameroon

Network village

Villager Ic R

MadKit

2.1 Topic and Issue

When multiple topics are covered by a case study, the first in the list indicates the one we used to classify it. Within each topic we have tried to order the case studies from the more abstract and theoretical ones to the more realistic ones. This information can be retrieved from the issue: only case studies representing a real system mention a geographical location.

2.2 Environment

  • First line: mode of representation, with the general following pattern:

    $$ \left[ {\mathrm{ none};\ \mathrm{ network};\ \mathrm{ raster},\ \mathrm{ vector}} \right]\ \mathrm{ N}\left( \mathrm{ x} \right) $$

    N indicates the number of elementary spatial entities (nodes of network, cells or polygons), when raster mode, N is given as number of lines x number of columns, unless some cells have been discarded from the rectangular grid because they were out of bound (then only the total number is given), and (x) indicates the spatial resolution.

  • Second line: level of organization at which the issue is considered (for instance village; biophysical entity (watershed, forest massif, plateau, etc.); city; conurbation; province, country, etc.)

2.3 Agents

One line per type of agent (the practical definition given in this paper applies, regardless of the terminology used by the authors). The general pattern of information looks like:

$$ \mathrm{ name}\left( \mathrm{ x} \right)\left[ {\mathrm{ Ho};\mathrm{ HeP};\mathrm{ HeB}\left( \mathrm{ y} \right)} \right]\left[ {\mathrm{ Ie};\mathrm{ Ii};\mathrm{ Ic}\left]\ \right[\mathrm{ R};\mathrm{ C}} \right] $$

  • (x) indicates the number of instances defined when initializing a standard scenario, italic mentions that this initial number change during simulation.

  • When x > 1, to account for the heterogeneity of the population of agents, we propose the following coding: “Ho” stands for a homogeneous population (identical agents), “He” stands for a heterogeneous population. “HeP” indicates that the heterogeneity lies only in parameter values, while “HeB” indicates that the heterogeneity lies in behaviours. In such a case, each agent is equipped with one behavioural module selected from a set of (y) existing ones. Italic points out adaptive agents updating either parameter value (HeP) or behaviour (HeB) during simulation.

  • [Ie, Ii, Ic] indicates the nature of relationships as defined in the text and shown in Fig. 19.3.

  • [R; C] indicates if agents are clearly either reactive or cognitive

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Page, C.L. et al. (2013). Agent-Based Modelling and Simulation Applied to Environmental Management. In: Edmonds, B., Meyer, R. (eds) Simulating Social Complexity. Understanding Complex Systems. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-93813-2_19

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