Companion Modeling and Multi-Agent Systems for Integrated Natural Resource Management in Asia

352 Pages·2008·4.1 MB·English

Companion Modeling and
Multi-Agent Systems for
Integrated Natural Resource
Management in Asia
Edited by F. Bousquet, G. Trébuil, and B. Hardy
2005
The International Rice Research Institute (IRRI) was established in 1960 by the Ford and
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Suggested citation:
Bousquet F, Trébuil G, Hardy B, editors. 2005. Companion modeling and multi-agent systems
for integrated natural resource management in Asia. Los Baños (Philippines): International
Rice Research Institute. 360 p.
COVER DESIGN: Juan Lazaro IV
LAYOUT AND DESIGN: Emmanuel Panisales
ISBN 971-22-0208-9
Contents
Foreword v
Acknowledgments vii
Introduction to companion modeling and 1
multi-agent systems for integrated natural resource
management in Asia
F. Bousquet and G. Trébuil
Part 1. The modeling process: from reality to conceptual model
Multi-agent systems for collective management of a northern 21
Thailand watershed: model abstraction and design
P. Promburom, M. Ekasingh, B. Ekasingh, and C. Saengchyoswat
A methodology for identifying and formalizing farmers’ 41
representations of watershed management: a case study
from northern Thailand
N. Becu, O. Barreteau, P. Perez, J. Saising, and S. Sungted
Developing a multi-agent systems model of agroforestry 63
adoption on smallholder farms in the Philippine uplands
D.B. Magcale-Macandog, P.A.B. Ani, M.E.M. Delgado, and P.C. Campo
Part 2. Models and role-playing games
Co-evolution of a research question and methodological development: 85
an example of companion modeling in northern Vietnam
S. Boissau
Companion modeling to examine water-sharing arrangements 101
among rice-growing villages in west-central Bhutan:
preliminary results
Tayan Raj Gurung
Role-playing games to understand farmers’ land-use decisions 121
in the context of cash-crop price reduction in upper
northeast Thailand
N. Suphanchaimart, C. Wongsamun, and P. Panthong
Participatory modeling for managing rainfed lowland rice variety 141
and seed systems in lower northeast Thailand: methodology and
preliminary findings
C. Vejpas, F. Bousquet, W. Naivinit, G. Trébuil, and N. Srisombat
iii
Part 3. Multi-agent simulations
A multi-agent model linked to a GIS to explore the relationship 167
between crop diversification and the risk of land degradation
in northern Thailand highlands
G. Trébuil, F. Bousquet, B. Ekasingh, C. Baron, C. Le Page
Modeling a biophysical environment to better understand the 191
decision-making rules for water use in the rainfed lowland
rice ecosystem
G. Lacombe and W. Naivinit
Economic differentiation of rice and shrimp farming systems 211
and riskiness: a case of Bac Lieu, Mekong Delta, Vietnam
Le Canh Dung, Nguyen Nhi Gia Vinh, Le Anh Tuan, and F. Bousquet
Dynamic simulation of land-use changes in a periurban 237
agricultural system
Sk. Morshed Anwar and F. Borne
Integrating multi-agent systems and geographic information 255
systems modeling with remote-sensing data for participatory
natural resource management in coastal Bohol, Philippines
P.C. Campo
Modeling multi-stakeholder forest management: the case of forest 275
plantations in Sabah
Ph. Guizol and H. Purnomo
Part 4. Learning processes
The Thai traditional learning process in folk culture: implications 295
for the companion modeling approach
I. Patamadit and F. Bousquet
Training on multi-agent systems, social sciences, and integrated 309
natural resource management: lessons from an Inter-University
Project in Thailand
F. Bousquet and G. Trébuil
A methodology for building agent-based simulations of common-pool 327
resources management: from a conceptual model designed with
UML to its implementation in CORMAS
C. Le Page and P. Bommel
International workshop on multi-agent systems for integrated natural 351
resource management in Southeast Asia
iv
Foreword
For several years, agricultural research organizations such as the Consultative Group
on International Agricultural Research (CGIAR) centers and Center for International
Cooperation in Agricultural Research for Development (CIRAD) have been opening
their portfolio of research methods to the sciences of complexity. The recognition of
the importance of these methods is linked to the strategic decision to increase research
on integrated natural resource management (INRM) on their agendas. The CGIAR
organized a series of meetings on INRM research during the last five years: starting
with the Bilderberg meeting, followed by the workshops in Penang, Cali, and Los
Baños. Collaborative research activities in this field between IRRI and CIRAD started
in 2001 and were based on the recognition of several principles that emerged from
the earlier CGIAR meetings.
The founding Bilderberg consensus stated that INRM research requires that
(1) stakeholders and national agricultural research and extension systems (NARES)
partners should participate in all stages of the research process from conception to
delivery of research results, and, (2) to help the poor, agricultural research must go
beyond the traditional paradigm (…) rather than focusing narrowly on increasing and
maintaining the productivity of commodities, and that, accordingly, CGIAR projects
in the field of INRM should satisfy the following minimum set of criteria:
• Be defined in a collaborative and equitable manner with all relevant stake-
holders and partners.
• Generate new knowledge as international/regional public goods based on
both indigenous knowledge and modern science.
• Effectively communicate and disseminate results and conclusions to all
stakeholders.
• Reform and strengthen institutions from local to policy levels.
It is in the mandate of both CGIAR centers and CIRAD to develop innovative
methodologies based on new approaches and paradigm shifts. In the case of INRM,
given its above-mentioned objectives, new research approaches originate from new
scientific opportunities and recent breakthroughs, among which spatial modeling and
adaptive management of renewable resources are key ones.
During 2001-04, joint efforts by IRRI and CIRAD led to the implementation of
a Thailand-based companion modeling project with a regional mandate. Its comple-
mentary objectives were to create and train a regional network of NARES practitioners
of companion modeling in Southeast Asia and to support them in the development of
their own applications and case studies. Following an initial course on multi-agent
systems for INRM offered at IRRI headquarters in late 2000, and thanks to a specific
grant from the ASIA IT&C initiative of the European Community, a series of 11
short courses attended by some 80 trainees was organized in recent years. These were
followed by the construction of some 15 applications dealing with concrete resource
management problems in five different countries.
v
Upon completion of this three-year-long companion modeling project, the pres-
ent volume is one of the first collective outputs and its various contributions show work
in progress. Its production benefited from strong support from the NARES involved
in the project activities, particularly in Thailand. A group of former trainees is now
continuing the effort through the establishment of the Asia Pacific Social Simulation
Association (www.apssa.net). They are also involved in a new series of projects,
particularly under the Challenge Program on Water and Food.
We are very pleased to present the products of these successful collaborative
training and research activities in the field of companion modeling and multi-agent
systems for integrated natural resource management in Southeast Asia. We are also
glad to see that this young regional network succeeded in finding ways to pursue this
collective endeavor through new research and training projects funded by various do-
nors during 2005-08. This illustrates convincingly that IRRI and CIRAD had a correct
vision when they began this collaboration, and that they played their respective roles
as strong partners of the NARES and other users in facilitating the dissemination of
the companion modeling approach, methodology, and tools across Southeast Asia.
Ren Wang Jean-Pierre Muller
Deputy Director General for Research Head of the GREEN Research Unit
IRRI CIRAD TERA
Philippines France
vi
Acknowledgments
The research presented in this book comes from a joint IRRI-CIRAD project. Several
other donors and supporting institutions were also involved.
- The donors were
Asia IT&C program of Service de coopération régionale de
the European Com- l’Ambassade de France à Bangkok
mission
- The Thai Universities that hosted training courses and workshops were
Faculty of Science, Chulalong- Multiple Cropping Center, Faculty of Agriculture, Khon
korn University, Bangkok Chiang Mai University Kaen University
The set of papers presented in this book would not exist without the active involvement
of the 12, mostly European, trainers who delivered new knowledge and stimulated new
research. We are very grateful to Prof. Nigel Gilbert (University of Surrey, UK), Dr.
Marco Janssen (Indiana University, Bloomington, Ind., USA), Prof. Alexis Drogoul
(Paris 6 University, France), Dr. Olivier Barreteau (Cemagref, France), Dr. S.P. Kam
(IRRI, Philippines), Dr. C. Le Page (CIRAD, France), Dr. Wander Jager (University
of Groningen, The Netherlands), Prof. Scott Moss (Manchester University, UK),
Prof. Nils Röling (Wageningen University and Research Centre, The Netherlands),
Prof. Jean-Pierre Müller (University of Neuchatel, Switzerland), Prof. Alan Kirman
(Greqam, France), and Martine Antona (CIRAD, France).
We would also like to thank the reviewers of the contributions who helped to improve
the quality and the clarity of the chapters: Rhodora Gonzalez, Marco Janssen, Chris-
tophe Le Page, Wander Jager, Louis Lebel, Michel Etienne, Olivier Barreteau, Sylvain
Perret, Williams Daré, John Caldwell, Didier Bazile, Suan Pheng Kam, Agnès Bégué,
Tom Evans, Alexis Drogoul, Aurélie Botta, Eric Penot, Marcel Djama, Sigrid Aubert,
Gilles Trouche, Marcel de Raïssac, Marcel Kuper, Robert Najlis, Bruno Locatelli,
Nourollah Ahmadi, and Flavie Cernesson.
vii
Introduction to companion modeling and
multi-agent systems for integrated
natural resource management in Asia
F. Bousquet and G. Trébuil
This introductory chapter recalls the origins of this publication at the interface
between the personal interest of several colleagues from different Southeast
Asian countries and a growing interest in methodological innovation in the field
of integrated natural resource management (INRM) in the Consultative Group on
International Agricultural Research. The historical development of the so-called
“companion modeling” (ComMod) approach relying on the use of multi-agent
systems (MAS) for INRM is also described, and its main principles and objectives
are defined: to develop simulation models integrating various stakeholders’ points
of view and to use them within the context of platforms for collective learning.
The ComMod methodology used to facilitate such a process in INRM is presented,
with an emphasis on the combination of key tools used with stakeholders, such as
conceptual models, MAS, and role-playing games. A final section introduces the
diversity of the Asian experiences presented in this book and its content.
In late 1998, Dr. Benchaphun Ekasingh and her colleagues from the Multiple Cropping
Center at the Faculty of Agriculture, Chiang Mai University (MCC-CMU), began or-
ganizing the first training course in Asia on multi-agent systems (MAS) and integrated
natural resource management (INRM). Based on Dr. Ekasingh’s strong experience
in the field of systems approaches in agriculture, she perceived the need to introduce
innovative approaches belonging to the emerging sciences of complexity and new
tools developed by researchers working in this field. This perception was confirmed
a few years later when the Consultative Group on International Agricultural Research
(CGIAR) organized several scientific workshops focusing on INRM. An important
point was made at the Penang meeting in 2000 with the mention of the adaptive
management concept, together with social learning and action research. Adaptive
management was seen as a way to “…ensure that functional integrity of the system
can increase the adaptive capacity. Adaptive capacity is dependent on knowledge
(…) the ability to recognize points of intervention and to construct a bank of options
for resource management.” Then, a new role for modeling was formulated in this
context: “Modeling proceeds iteratively by successive approximations usually from
simple to more complex representations of system dynamics. This iterative modeling
is done in close interaction with stakeholders, who, along with the modelers, use the
Introduction to companion modeling and multi-agent systems... 1
models for scenario planning.” Thus, Dr. Ekasingh invited members of the GREEN
(French acronym for “Renewable resource management and the environment”) team
of CIRAD to hold a two-week course at MCC-CMU in late 1999.
Since the creation of the GREEN research team by J. Weber in 1993, several
researchers have been developing modeling activities to better understand the inter-
actions between social and ecological dynamics. A basic principle was to go beyond
disciplinary approaches tackling the problem exclusively either from the angle of “an
ecological system subject to anthropologic disturbance” or from the angle of “a social
system subject to natural constraints.” In the first case, scientists carefully describe
the dynamics of the resource and management is considered as the various forms of
anthropologic exploitation of the ecosystem that can be sustained over the long term.
Social dynamics are represented in terms of the type of resource exploitation they
entail. In the second case, researchers generally focus on the problem of resource
usage and position themselves as isolated economic agents who wish to maximize
the benefits obtained from a limited resource and place the collective use of common
resources within a framework of competitive exploitation. Unlike the ecological ap-
proach or the economic approach, both of which postulate hypotheses of equilibrium
and optimization to formalize situations of competition or interaction, GREEN re-
searchers look at renewable resource management in a different light by integrating
the dynamics of the ecological and social dimensions and eliciting their interactions.
Their main research theme is the decision-making process. Unlike the conventional
decision-making process, which defines a decision as a rational calculation on the part
of a more or less fully informed decision-maker, GREEN researchers consider the
decision-making process as a series of interactions among stakeholders having various
objectives, different perceptions, levels, or kinds of information, and varying degrees
of importance and influence. Figure 1 illustrates such a frame of mind. The objective
of the researcher working on such a system is to try to understand the interactions
between key processes, the social ones being driven by various interacting points of
view.
In the field of modeling, a choice was made to use and develop tools called
multi-agent systems.1 The aim of multi-agent systems is to understand how different
processes in direct competition are coordinated. Woolridge (1999) defines an agent
as “a computer system that is situated in some environment, and that is capable of
autonomous action in this environment in order to meet its design objectives.” An
agent can be described as autonomous because it has the capacity to adapt when its
environment changes. For Ferber (1995), an agent is a physical or virtual entity, which
operates in an environment, is able to perceive it and act on it, which can communicate
with other agents, and which exhibits an autonomous behavior that can be seen as a
consequence of its knowledge, its interactions with other agents, and the goals it is
pursuing. A multi-agent system (MAS) is made up of a set of computer processes
1In computer science, this kind of model is called a multi-agent system. In ecology, they were called individual-based
models. While other disciplines introduce multi-agent systems in their research field, one observes the emergence
of new terms such as agent-based modeling (ABM). Some people, such as our group, think that ABM reflects the
use of agents but does not emphasize interactions, which is the main innovation in our approach. This is why some
researchers, most of them in social sciences, use multi-agent-based simulation (MABS). For the sake of simplicity, we
use the MAS acronym in this introduction.
2 F. Bousquet and G. Trébuil
that occur at the same time, that is, several agents that exist at the same time, share
common resources, and communicate with each other. Figure 2 shows a schematic
representation of a MAS. It illustrates the conceptual relationship between a MAS
and the definition of our research object shown in Figure 1.
In the field of MAS modeling for INRM, complementary activities were devel-
oped by GREEN researchers, PhD students, or associated researchers.
1. Developing abstract models, also called artificial societies, that help to under
stand the generic properties of interacting processes: models on nonmerchant
exchanges and reputation, models on economic tools for the regulation of
economic exchanges, and models on spatial dynamics.
2. Developing models applied to concrete and local problems to understand the
dynamics of natural and renewable resources and their management. Ap-
plications were developed in irrigation, wildlife management, and pasture
management.
3. Developing a simulation platform (CORMAS, common-pool resources and
multi-agent systems). This platform was developed in an inductive way by
trying to select generic aspects while working on concrete applications and
by integrating them into this tool.
4. Developing a companion modeling (ComMod) methodology for the use of
these MAS tools within the community of approaches dealing with participa-
tory modeling for collective learning and action. The ComMod method uses
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Introduction to companion modeling and multi-agent systems... 3