or rating - a decision on sustainability is derived. The decision-making process needs to be scientifically sound, locally accepted and transparent to all
stakeholders. The involvement of all stakeholders, who have different educational backgrounds, in the decision-making process is a necessary condition for co-
management. The decision-making process was observed during the field study.
Figure 3.6. KBS inference engine
3.3.5. Building a Simplified Artificial Society of Forest Actors
The purpose of building an artificial society was to observe whether the localized CI by which local knowledge on sustainable forest management was
embedded, could be applied in real life. It is almost impossible to see the full effects of using localized CI in real life - it would take a long time, beyond the
research period. Simulation techniques are well-known methods for addressing this matter. In this research, a Multi-Agent System MAS was used to simulate
the behavior of each agent and the interaction between agents. The agents are
located in a spatial system environment. In Figure 3.7, for example, there are
four kinds of agents in the simulation: a firm, villagers, non-government organization NGO and local government. The firm’s forest concession and the
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Input indicators
Classification to form criteria
Multi- criteria
analysis Decision on
sustainability
villagers are located in a forest. The local government has an obligation to maintain this forest’s sustainability by providing rules to the firm and local people.
As shown in Figure 3.8, the concession is located in a certain area. They log wood by taking into consideration the distance between the logging site and
available wood. The NGO advocates on behalf of villagers to help them realize their rights. The villagers move to the best site for collecting NTFP Non-Timber
Forest Products.
Figure 3.7. An example of model components and their interaction located in the spatial system
NGO
Government
Villager Firm
Legally allocated to the firm Communication
NGO
Figure 3.8. Spatial representation of the firm’s activities and the movement of villagers
Figure 3.9. Communication among forest stakeholders Firm
Villagers
Local Government
proposition 1 announcement 3
agreement or disagreement 2
Reasoning based on price
and beliefs offering 4
contract or sorry 5
Telling the others what they
do
Reasoning based on CI,
looking at sustainability
Reasoning based on a
firm’s performance
Good villagers
Firm’s performance
NGO advocating6
A model of the communication process among stakeholders was arranged to meet actual conditions in the field with some simplifications. An
example of the communication process is shown in Figure 3.9. The firm send a message proposition to local government that they need wood - more than
they get from the current logging area. The government considers this message and then gives either an agreement or disagreement message in response.
Since the sites are not allocated to the firm but to the villagers, the firm sends a message demanding to the villagers, asking if they want to sell wood to the
firm. The villagers consider this message, and then some of them send a message an offering to the firm. The firm finds the best offer and makes
contact with them. The villagers who have the contract will not move to collect NTFP, and will tell other villagers and the government they have the contract.
The firm will record the performance of each villager for future use. The artificial society was developed with Smalltalk Computer Language in
a CORMAS Common Pool Resources and Multi-agent Systems environment. CIRAD Fôret, France, developed CORMAS Bousquet et al. 1998. CORMAS is
a simulation platform based on the Visual Works programming environment, which allows for the development of applications in Smalltalk.
CORMAS is a programming environment dedicated to the creation of multi-agent systems, with specificity in the domain of natural-resources
management. It provides a framework for developing simulation models of coordination modes between individuals and groups that jointly exploit common
resources CIRAD 2001. There exist more and more programming environments dedicated to the creation of multi-agent systems. Some of them are
oriented towards communication between distributed systems, while some others are more oriented towards the building of simulation models such as Ascape,
MODULECO, MadKit and Mobydic.
The CORMAS programming environment belongs to this second category. It provides a framework that is structured in the following three
modules. The first module allows for defining the entities of the system to be modeled which are called informatics agents, and their interactions. These
interactions are expressed in terms of direct communication processes transfer of messages andor the sharing of the same spatial support. The second
module deals with the control of overall dynamics ordering of different events during a time-step of the model. The third module allows for the defining of
observations of the simulation based on different viewpoints. This feature allows for the integration, within the modeling process, of representation modes.
CORMAS facilitates the construction of a model by offering predefined elements. Among these items are the CORMAS entities. These are Smalltalk
generic classes from which, by specializing and refining, the user can create entities specific to the needs of his application. The data used in the simulation is
gathered from the secondary data and interviews. Key phases in the development of the model Grant et al. 1997 were:
•
Forming a conceptual model: stating the objectives, bounding the system of interest, categorizing its components, identifying relationships, and
describing the expected patterns of model behavior;
•
Quantifying the model: identifying the functional forms of model equations, estimating the parameters, representing it in CORMAS and executing
baseline simulations;
•
Evaluating the model: re-assessing the logic underpinning the model, comparing model predictions with expectations and with the real system;
and
•
Using the model: developing scenarios, testing hypotheses and communicating results.
3.3.6. Testing Method of the Second Hypothesis
