Multi-level Organization
Islem Henane
, Sameh Hadouaj
and Khaled Ghedira
Higher Institute of Management, Tunis, Tunisia
Faculty of Economic Sciences and Management, Nabeul, Tunisia
Laboratory of Strategy Optimisation of Information and Knowledge (SOIE), Tunis, Tunisia
Keywords: Multi-agent Based Modeling, Complex System, Multi-level Organisation, Emergence, Multi-agent
Abstract: Pastoral systems in arid and semi arid areas are characterized by a continued deterioration. This degradation
is the result of the mismanagement of resources in response to natural, economic and social mutations.
These systems are considered as complex systems, given the large number of stakeholders in interaction and
levels of granularity. To address this situation, analytical and systemic approaches are no longer adequate.
In this paper, we propose a multi-agent based model of Tunisian pastoral dynamics taking into account the
interaction dynamics of the different stakeholders and the different levels of granularity. The completion of
this work is within the scope of the development of the Intelligent Decision Support System PASDES
(Pastoral Strategies Definition System). PASDES aims to support pastoral strategic decision making in short
and long terms.
Tunisian rangelands spread over almost one third of
the country's area. Almost half of these rangelands
are collectives. These figures reflect the importance
of the pastoral activity. However, the Tunisian
pastoral resources are in a continued deterioration.
This deterioration is caused by the inappropriate
strategies undertaken to address climate (high
temperature, unstable precipitation) economical
(increase in grain prices, global crisis) and social
changes (privatization, competition over resources).
The sustainability of the system is threatened.
The pastoral system is a complex system
characterised by a large number of interacting
entities (plants, animals, shepherds, economic actors,
state, etc) and different levels of granularity (
vegetation dynamics, animal dynamics, shepherds
interactions and negotiations, etc). Thus, the study of
such a system is conducted in the context of solving
complex problems. In literature, three approaches
are mainly used to study such a system. First, the
analytical approach was adopted by economists. It
focuses on the elementary study of the system
components. For example, the study can be based on
the maximizing of an objective function on a
particular element of the system (Lalba, Zoundi and
Tiendrebeogo, 2005) (Dutilly-Diane, 2006). This
approach deals with one variable at a time which
prevents the study of the overall system dynamics.
Second, the systemic approach was adopted by
ecologists. It is based on the study of the overall
system behavior (Costanza, Wainger, Folke and
Mäler, 1993) without taking into account the micro-
dynamics. In this context, the emergence of global
properties is evoked (Bergandi , 2000) (Oprisan and
Oprisan, 2006). Third, the constructivist approach
brings together the contributions of the analytical
(micro-level study) approach as well as the systemic
approach (macro- level study). It focuses on the
study of micro-order interactions of different entities
as well as the global system behavior. In this
approach, we can distinguish the use of cellular
automata (Soares-Filho, Cerqueira and Pennachin,
2002) and multi-agent systems. When using cellular
automata, the environment is represented by a set of
cells. The behavior of individuals is defined by a
finite set of states, transition rules and
neighbourhood relationship. However, cellular
Henane I., Hadouaj S. and Ghedira K..
DOI: 10.5220/0003832802540258
In Proceedings of the 4th International Conference on Agents and Artificial Intelligence (ICAART-2012), pages 254-258
ISBN: 978-989-8425-96-6
2012 SCITEPRESS (Science and Technology Publications, Lda.)
automata are unable to take into account the
complexity and heterogeneity of stakeholders’
behaviors. On the other side, multi-agent systems are
characterised by their ability of modeling
interactions between autonomous agents and with
their environment. In this work, we opted for the use
of multi-agent systems. In fact, using multi-agent
systems in a complex system solving context takes
benefits of (1) ease of modeling of distributed
systems (2) possibility of modeling of the cognitive
decision processes (3) explanatory ability of
dynamics and micro and macro variables (self-
organization, emergence). The completion of this
work is within the scope of the development of the
Intelligent Decision Support System PASDES
(PAstoral Strategies Definition System). PASDES
aims to support pastoral strategies decision making
in short and long terms. In this paper, we propose a
multi-level modeling of the Tunisian pastoral system
taking into account the interaction dynamics of
different stakeholders and climatic, economic and
social variables. Such multi-level organization
allows design and modeling of each level in its own.
It allows understanding more the system dynamics.
When modeling all levels of granularity is achieved,
prediction of the future system evolution becomes
possible. Defining adequate strategies is then
feasible. In this paper, we propose our multi-level
model and we detail the first level modeling. The
paper is organized as follows: section 2 presents a
state of the art of multi-agent works studying the
management of renewable and pastoral resources,
section 3 describes our multi-agent model of the
pastoral dynamic using multi-level organisation and
interaction and introduces our first level multi-agent
based model, and we end this work with conclusion
and perspectives.
The pastoral system is a complex system
characterized by a large number of entities of
different nature (reactive: plants, animals, cognitive:
shepherds, state). These entities interact with the
environment or with each other to achieve their
goals. In a multi-agent context, some models of
natural resources management focused on modeling
the interaction between human and resources. These
models are based on the stigmergy notion; that is a
change in the environment by an agent affects the
decisions of other agents (Omicini. et al, 2004).
Other models are based on interactions (conflicts,
negotiations, etc.) between agents to make collective
use of resources. In this context, note the role
playing games used to evaluate the impact of
negotiations and decision taken by various
stakeholders on the development of common
resources. In literature, role playing games were
used to study individual and groups behaviors in
economic social context. During such games, each
actor plays the role of the realist stakeholder in a
fictive environment. Role playing games are a
powerful tool to support negotiation process and
participant training (Guyot and Honiden, 2006). In a
multi-agent context, several works tried to jointly
use multi-agent systems and role playing games. For
example, Dray, et al. (2006) introduced the role-
playing game "AtollGame" based on a multi-agent
system to study the problem of drinking water
supplies. The Role playing game "SylvoPast"
proposed by Etienne (2003) studies the negotiation
process between farmers and forestry in order to
prevent fires in Mediterranean forest areas.
Note that most multi-agent systems modeling
natural and pastoral resource management focus on
decisions making and negotiation between economic
and social actors, using variables of macro level (e.g.
hydraulic state of the year: wet year; dry year).
These variables are empirical or dependent on a
number of assumptions. Therefore, such approach
can lead to biased results that can be the cause of
non-adequate decision making. However the
ecological literature is rich on studies of the micro
dynamics of such a system. For example, the study
of hydraulic dynamics provides us with information
about runoff emergence. It is then possible to take
advantages of such information to define with
precision the best shepherd displacement. Such
information is much more interesting then
characterizing the hydraulic state by criteria such as
“dry year” and “wet year”. The study of animals
behavior (imitation, displacement behavior, grazing)
can also be used for space management. For
example, studying the behavior of selectivity
(animal preference) supports reflexions on
introducing mixed herds (with different preferences)
instead of working on criteria such as “dense
vegetation cover” and “naked ground”.
Throughout this work, we opted to go down to
the micro-level modeling of the Tunisian pastoral
dynamics. Taking into account the different levels of
granularity allows best comprehension of the system
dynamic and adequate decision making.
Figure 1: Pastoral system: Multi-level organization.
Figure 2: Pastoral System: Design and Modeling by organization level.
3.1 Multi-level Organization
In order to manage our system complexity, the basic
idea is to make a multi-level modeling (shown in
Figure 1). This organization will enable us to make
the design and modeling of each level of granularity
in its own (See Figure 2). The model includes five
levels of granularity. The first level of granularity
includes three sub-levels:
The dynamics of soil affected by climatic
conditions (hydrology, fertility, water
infiltration, runoff).
The vegetation dynamics (plant growth,
physiological and chronological cycle,
The Animal agent behavior (food needs,
The interaction dynamic of the soil, vegetations
and climatic variables lead to the emergence of
global properties such as the hydraulic state of the
soil and vegetation density. In order to validate
results at this level, we consider the use of GIS
ICAART 2012 - International Conference on Agents and Artificial Intelligence
(Geographic Information System).
The second level of granularity focuses on
animals’ group behavior (imitation, leadership,
competition over resources). The interaction
dynamics in this level of granularity lead to the
emergence of the groups’ displacement and
consumption behavior.
The third level of granularity focuses on
modeling the decisional behavior of the cognitive
Shepherd agent in response to the macro-variables
values provided by the lower level of granularity
(size of the herd, vegetation density).
The fourth level of granularity focuses on
modeling the interactions between Shepherd agents
(negotiation, competition over resources). In order to
stick with reality, we consider the use of
participatory simulation in this level. The simulation
results on the emergence of collective behavior (e.g.
rules of access to resources).
The fifth level of granularity focuses on the
strategic decision level of the State agent defining
the strategies to meet the needs of different
stakeholders while maintaining the sustainability of
the system (grain subsidy, renewal of vegetation
In this model, the different levels of granularity
are fed by the macro indicators of lower levels (see
Figure 2). Note that indicators are macro variables or
emergent properties resulting of the underlying local
dynamics. The higher levels take into account these
indicators and act by feedback. Taking into account
this feedback, the lower layer dynamics proceed by
adaptation to the occurred changes. In addition to
these indicators, we define for each level of
granularity thresholds. When indicators exceed the
thresholds values, alarming situations are risen (land
cover degradation, soil degradation), so the
underlying higher levels make decision to manage
3.2 Modeling Multi-level Interaction
To simplify access to data of the different layers of
our model, we opted for the use of an Observer
agent by each layer (See Figure 3). An Observer
agent is characterized by an overall perception of its
layer. It detects the emergence of global properties.
It collects the values of macro indicators, makes
comparison with the thresholds defined to indicate
an alarming situation (as soil degradation). The
Observer agents are charged of the communication
between the different layers. Agents from layer A,
requiring information on a layer B, ask the Observer
agent A for it, which sends a request to the Observer
agent of the layer B. So, this agent responds to the
request. The agents of the A layer act according to
information provided by feedback (flow of action).
When studying interaction between levels of
granularity, we have not taken into account the
notion of time. In fact the appearance of emergent
properties and the establishment of macro variables
require consideration of the underlying dynamics. It
is undeniable that the decision of agents at higher
levels can only be made if there is stabilization in
the variables studied. They subsequently react with
Figure 3: Multi-level interactions.
3.3 Micro-level Modeling
In this section, we focus on modeling the first
organization level of our system architecture. Our
efforts are concentrated on soil hydraulic processes
and more specifically the dynamics of infiltration.
To do this, we are inspired by the principle of eco-
resolution (Ferber, 1995). Our basic model consists
of two agents (see Figure 4): Drop of Water agent
and Air Bubble agent. Initially, the Air Bubble agent
is occupying a pore in the soil, The Drop of Water
agent is on the soil surface. The Drop of Water agent
moves to achieve its satisfaction which is occupying
a pore. The Air Bubble agent leaves the pore if it is
attacked. Since we are interested to infiltration
dynamic, we are limited to study the case that. The
Drop of Water agent has a speed which is lower than
the runoff speed. In this case, the Drop of Water
agent attacks close Drop of Water agents. If there is
no Drop of Water agent in its neighbourhood, it
attacks the nearest Air Bubble agent occupying a
pore forming positive angle with the current position
of the Drop of Water agent.
Figure 4: Infiltration process.
We presented a multi-agent multi-level model of the
Tunisian pastoral system. The multi-level
organization allows best comprehension of the
pastoral dynamics. In terms of development, such
organization offers the possibility to design each
granularity level in its own. The validation of each
level allows moving to the next level of granularity.
Simulation taking into account all levels of
granularity allows the future system dynamics
prediction. This aspect will provide our system
PASDES with the ability to define adequate
strategies taking into account micro- level indicators.
Up to now, we have started the first level of
granularity modeling, we have attacked the soil
hydraulic dynamics and more specifically the
infiltration process. A very important step at this
level is the acquisition of ecological and climatic
data needed to supply our first level models, hence
the need of the collaboration with experts in the
field. The use of GIS for the validation of the
ecological model will allow us to stick well with
reality. Interactions between cognitive agents in
participatory simulation will take advantages of
efficient data provided by this model.
Bergandi, D., 2000. Eco-cybernetics: the ecology and
cybernetics of missing emergences. Kybernetes 29 (8).
Costanza, R., Wainger, L., Folke, C., Mäler, K. G., 1993.
Modeling complex ecological economic systems:
toward an evolutionary, dynamic understanding of
people and nature. BioScience. 43(8), pp. 545-555.
Dray, A., Perez., P., Jones, N, Le Page, C., D'Aquino, P.,
White, I. and Auatabu, T., 2006. The AtollGame
Experience: from Knowledge Engineering to a
Computer-Assisted Role Playing Game. Journal of
Artificial Societies and Social Simulation, 9 (1).
Drogoul A. and Guyot P., 2004. Multi-Agent Based
Participatory Simulations on Various Scales, In:
Ishida, T., Gasser, L. and Nakashima H., eds.
Massively Multi-agent Systems (MMAS), 2004.
Lecture Notes in Artificial Intelligence, Springer-
Verlag Berlin Heidelberg, pp 149-160.
Dutilly-Diane, C., 2006. Gestion collective des parcours
en zone agro-pastorale : le cas de Ait Ammar (Maroc).
Afrique contemporaine, 3(219), pp.103-117.
Etienne, M., 2003. Sylvopast: a multiple target role-
playing game to assess negotiation processes in
sylvopastoral management planning. Artificial
Societies and Social Simulation, 6(2).
Ferber. J., 1995. Les systèmes multi-agents. Vers une
intelligence collective. InterEditions. Paris.
Guyot, P. and Honiden S., 2006. Agent-Based
Participatory Simulations: Merging Multi-Agent
Systems and Role-Playing Games. Journal of
Artificial Societies and Social Simulation, 9(4).
Lalba, A., Zoundi, J. S., Tiendrebeogo, J. P., 2005.
Politiques agricoles et accès aux parcours communs
dans le terroir de Ouara à l’ouest du Burkina Faso: une
analyse économique et environnementale à l’aide de la
programmation linéaire. Biotechnologie, agronomie,
société et environnement, 9 (1), pp.43-52.
Omicini, A., Ricci, A., Viroli, M., Castelfranchi, C. and
Tummolini, L., 2004. Coordination Artifacts:
Environment-Based Coordination for Intelligent
Agents. In Proceeding of AAMAS, July 2004, pp.286-
Oprisan, S. A., and Oprisan, A., 2006. A computational
model of oncogenesis using the systemic approach.
Axiomathes, 16 (2).
Soares-Filho, B. S., Cerqueira, G. C. and Pennachin, C. L.,
2002. DINAMICA - a stochastic cellular automata
model designed to simulate the landscape dynamics in
an Amazonian colonization frontier. Ecological
Modelling, 154(3), pp.217-235.
ICAART 2012 - International Conference on Agents and Artificial Intelligence