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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))

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 the descriptions is not to allow the replication of the models but rather to characterize the types of spatial representation, the properties of the agents and 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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Author information

Authors and Affiliations

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

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

  2. Instituto de Geografía, PUCV, Valparaiso, Chile

    Didier Bazile

  3. CNRS, Laboratoire de geographie PRODIG 2, Paris, France

    Nicolas Becu

  4. Brasilia University, UnB-CDS, 70070-914, Brasilia DF, Brazil

    Pierre Bommel

  5. Ecodevelopment Unit, National Institute for Agronomic Research, Avignon, France

    Michel Etienne

  6. Centre d’Ecologie Fonctionnelle et Evolutive, UMR 5175, CNRS 1919 Route de Mende, 34293, Montpellier, France

    Raphael Mathevet

  7. UMR SADAPT INRA AgroParisTech, B.P. 1, 78850, Thiverval Grignon, France

    Véronique Souchère

  8. CU-CIRAD ComMod Project, Chulalongkorn University, Bangkok, Thailand

    Guy Trébuil

  9. CIRAD, Direction Régionale Ile de France, 42 Rue Scheffer, 75116, Paris, France

    Jacques Weber

Authors
  1. Christophe Le Page
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  2. Didier Bazile
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  3. Nicolas Becu
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  4. Pierre Bommel
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  5. François Bousquet
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  6. Michel Etienne
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  7. Raphael Mathevet
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  8. Véronique Souchère
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  9. Guy Trébuil
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  10. Jacques Weber
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Corresponding author

Correspondence to Christophe Le Page .

Editor information

Editors and Affiliations

  1. Centre for Policy Modelling, Manchester Metropolitan University, Business School, Manchester, United Kingdom

    Bruce Edmonds

  2. Centre for Policy Modelling, Manchester Metropolitan University, Business School, Manchester, United Kingdom

    Ruth Meyer

Appendices

Appendix

Table 1
Full size table
Table 2
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Table 3
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Table 4
Full size table

1.1 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.

1.2 Environment

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

    [none, network, raster, vector] N(x)

    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.)

1.3 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:

name(x) [Ho;HeC;HeB(y)] [Ie;Ii;Ic] [R;C]

  • (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), and “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. 22.3.

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

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.

    López Paredes and Hernández 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.

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Le Page, C. et al. (2017). Agent-Based Modelling and Simulation Applied to Environmental Management. In: Edmonds, B., Meyer, R. (eds) Simulating Social Complexity. Understanding Complex Systems. Springer, Cham. https://doi.org/10.1007/978-3-319-66948-9_22

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