Developement of Multi-Agent system (MAS) model for Bac Lieu case study

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Developement of Multi-Agent system (MAS) model for Bac Lieu case study

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The objective of the past work was to capture the main processes, both biophysical and economic, to reproduce them in a virtual world similar to reality. There have been two stages. In a first step, the team tried to select relevant knowledge to set-up a first conceptual model. As an hydrological model was already developed (Hoanh, et al. 2001), the emphasis was put on the economic and social processes. The first idea was to work on the interactions between decisions making at different scales. Are the decisions making processes of the different stakeholders (farmers, traders, hamlet, commune, water company, etc.) well......

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  1. Development of Multi-Agent Systems (MAS) model for Bac Lieu case study --- oOo --- F. Bousquet , LC. Dung2, LA. Tuan2 1 1 IRRI-Cirad, Bangkok, Thailand 2 Cantho University, Vietnam 1 INTRODUCTION Beside the DFID project “Accelerating poverty elimination through sustainable resource management in coastal lands protected from salinity intrusion” a complementary modeling experiment started in September 2001. The objective is to test the use of a new methodology using Multi-Agent Systems for participatory modeling. This approach was initiated by Cirad (Bousquet, et al. 1999) and has already been developed in collaboration with IRRI. For instance this approach is developed and used by IRRI researchers in North Vietnam for the study of farmer’s practices (Castella, et al. 2001). MAS is a methodology which put emphasis on the understanding of decision making processes of different stakeholders, linked to bio-physical dynamics. Thus, it seems to be a method complementary to the others method already used in DFID project (spatial analysis for land management unit, hydrological modeling and economic survey). It may be helpful for the integration of knowledge coming from different disciplines. It has been decided to conduct a one year project on MAS modeling to train the participants and assess if this method is relevant and of some interest for the case study. The team is composed of a modeler and two scientists from Cantho University specialized in Economics and Hydrology. The IRRI-DFID project experts are also participating by delivering the expert knowledge and giving access to the data collected. In January 2002 the team met for the first time with the DFID project to discuss on the issues and dynamics at stake. Then, during April 2002 the modeling team produced a conceptual model of farmers’ decision-making process that is presented in this paper. The objective is to inform and provoke discussions with researchers and stakeholders and orient the research for the remaining six months. The objective of the past work was to capture the main processes, both biophysical and economic, to reproduce them in a virtual world similar to reality. There have been two stages. In a first step, the team tried to select relevant knowledge to set-up a first conceptual model. As an hydrological model was already developed (Hoanh, et al. 2001), the emphasis was put on the economic and social processes. The first idea was to work on the interactions between decisions making at different scales. Are the decisions making processes of the different stakeholders (farmers, traders, hamlet, commune, water company, etc.) well coordinated? What is the influence of a stakeholder’s decision on another one? 1
  2. Then, a research question has been proposed in April 2002 by the researchers from University of Cantho: how rice/shrimp systems affect economic differentiation? The associated hypothesis is that rich farmers will get richer by applying shrimp production system, poor will get poorer. The rice farmers will get poorer because of low rice price, and as there is high risk of shrimp failure the poor may have difficulties to recover from these failures. In this paper, we present firstly some principles of multi-agent systems. Then we present the model, which have been designed and partly implemented. It has been implemented with the Cormas platform (Bousquet, et al. 1998). The design of the model leads to new questions. Lastly we propose a discussion on the next steps and possible perspectives for this research. 2 PRINCIPLES OF MAS 2.1 What is a MAS? The aim of multi-agent systems (Ferber 1999, Weiss 1999) is to understand how independent processes in direct competition are coordinated. An agent is thus a computerized process, something that comes between a computer program and a robot. An agent can be described as autonomous because it has the capacity to adapt when its environment changes. A MAS is made up of a set of computer processes that occur at the same time, that is, several agents exist at the same time, share common resources and communicate with each other. The key issue in the theory of MAS is formalizing the necessary coordination among agents. The theory of agents is therefore a theory of 1. Decision-making: what decision-making mechanisms are available to the agent? What are the linkages among their perceptions, representations, and actions? 2. Control: What are the hierarchical relationships among agents? How are they synchronized? 3. Communication: What kinds of messages do they send to each other? What syntax do these messages obey? For which elaborate formulas are put forward. Here are the key questions that can be explored using MAS: How do individuals make up a group? How is an institution created? The individual cannot be considered as an autonomous entity that is independent of its social environment. Therefore, how are individuals constrained by collective structures that they themselves have set up and how do they make these structures evolve (Gilbert 1995)? What degrees of freedom are given to the definition of individual practices? Here are just some of the questions that can be explored using MAS and that can be summarized as follows: How are collective structures set up and how do they function when they are based on agents with different capacities of representation, that exchange information, goods, or services, etc., draw up contracts, and are thrust into a dynamic environment that responds to their actions? 2
  3. We think that such questions are relevant to some important issues in INRM, and thus the interdisciplinary research was very promising when some of us started to work on this methodology ten years ago. This was confirmed by the successful development of several applications (Barreteau and Bousquet 2000, Bousquet, et al. 2001, Rouchier, et al. 2001). 2.2 The use of MAS The new INRM approaches developed by the CGIAR go with new scientific opportunities and breakthroughs, among which spatial modeling is a key one. “Models will have increased capacity to integrate social and bio-economic information”(Bilderberg 1999) Adaptive management is defined as “building and maintaining stocks of biodiversity that ensure that functional integrity of the system can increase the adaptive capacity. Adaptive capacity is dependent on knowledge — its generation and free interchange — the ability to recognize points of intervention and to construct a bank of options for resource management”. The role of modeling is 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 models for scenario planning.” This is how we would like to use the models: we try to integrate knowledge from various sources in a iterative way. Then the model is used with the stakeholders to explore various scenarios. This approach has been called companion modeling (Bousquet, et al. 1999). 3 MODELS 3.1 Conceptualisation The first model we designed was initiated during a meeting in the field in September 2001 and discussions with the scientists involved in the project. The research question identified at that time was: what are the socio- economic impacts of different water control scenarios? The hypothesis is that there are two main driving forces: the price of rice and shrimps and the water quality (salinity). IRRI scientists already produced a model to control the water dynamics (Hoanh, et al. 2001). This model is used to assess various scenarios of management of the sluices. It has been validated and is used on the field: for given needs the model helps the management of different sluices. The main potential input of multi-agent modelling is the representation of decision- making process. The bio-physical model does not take into account the dynamics of the stakeholders decisions and the heterogeneity of the stakeholders. Thus we started to conceptualise a model to take into account the decisions across different scales. Decisions are made by different stakeholders at the level of the Province, the district, the commune, the hamlet and the farm household. There are also other stakeholders who influence the systems like the water company (linked with the Province), the 3
  4. bank or different kind of traders. The purpose is to use the tool to understand the coordination and explore different scenarios. Among the various choices we made, we decide to design firstly a generic model for Bac Lieu context. The purpose is not to represent exactly such or such reality but to capture the interactions between the main processes from which emerges the complexity. The assumption is that, once these processes captured, the model can be conceptualised anywhere in the study area. We made different choices: • Biophysical representation. The spatial model is represented in raster mode. Each cell represents 0.5 hectares: a farmer plot is thus composed of one to 8 cells (0.5 to 4 hectares). In the model each farmer owns only one plot. The model represents main canal, primary, secondary and tertiary canals. The acidity of the soil has been classified in three groups high land non-acid surface (high), medium deep acid surface soil (medium), low shallow acid surface soil (low). Sluices at the intersection between main canal and primary canal are open or closed. Data are available to simulate the diffusion of salinity depending on the state of the sluices. Each plot is connected to the closest canal. Secondary canal Tertiary canal Primary canal Sluices Main Canal Figure 1. The interface of the first model, Representing the plots, the canals and the sluices • Farmer’s decision-making process. There has been several discussions on the decision making process of the farmers. We decided that the farmer agent in the model would represent the level of the household. Each individual is not represented. Finally we worked out the diagram presented in figure 2. The decision depends on three parameters: did the rainfall come early or not? It is assumed that the water company will open or close the sluices accordingly. What is the 4
  5. topographic position of the plot? And finally what are the economic conditions of the farmers? Depending on the combination of these three parameters, the household will have different kind of activities on his plot. It also depends on its access to credit. Along the year the farmer will change crop according to the loans he could or could not get. Figure 2. The flow chart of the crop decision of the household agents • Other agents’ decisions for water management. Two agents are involved : the district and the water company. From the observations on the field the commune and then the district compile a calendar. This calendar is transmitted to the water company, which operates the sluices accordingly. • Traders. The rice and the shrimps produced are sold to traders. As one of the driving forces is the price, it is important to capture the set of interactions for the two commodity chains. The model of interactions for the rice is the following: the trader proposes price depending on request (quality demanded), the trader has spatial zone and social relations (social network), the trader has a limitation of volume (boat), the household chooses best price. For the shrimp there are also middlemen who buy the shrimps, depending on their quality. 5
  6. 3.2 Questions The conceptualisation is an on-going process. Several questions are unsolved yet. Some are related to the objective of the model. Some are related to the knowledge we introduced in the model. Some questions are related to the purpose and the use of the model. For instance, the farmers’ decision-making process presented above is not linked to the dynamics of the water salinity. It is assumed that, depending on the rainfall; the sluices will be opened or closed. Thus, at this level of resolution, there is no need of simulating the water dynamics. There is no spatial differentiation between the plots. When the sluices are operated it takes only a few hours for the salinity level to diffuse. Therefore at a weekly time step we can consider the salinity situation as homogenous. This means that a model to study the economic differentiation does not need to be linked to hydraulic model, but only to the decision of the district and water company to open or close the sluices. At this stage of the process this conclusion has to be discussed. The model can be used as a framework to guide the gathering of data in the different surveys achieved by the DFID project. We give some examples of needed knowledge to complete this conceptualisation process : • We need to understand better how the information is transmitted across levels of organization, particularly in the ascendant way: how are the upper levels informed of the decisions of the households and how is it taken into account? • How do the economic conditions of a household change? In the household decision making diagram, the crop decision is dependent on the economic conditions of the household. What is the set of rule, which changes these conditions according to the cropping pattern of the household? How detailed must be the model? We can try to simulate the budget of the household; by using data we have for costs, yields and prices. It is maybe not necessary to have precise simulation of the household budget. Simple transitions rules can also be used such as: o If the household economic conditions are average and he invest in shrimps, it has 66% of chance to become rich, 17% to remain average, and 17% to become poor. • How do the households loan money? • The market of the land has to be introduced. When agents become too poor they may have to sell their land. Other agents may in turn increase their area. This process is important to study the economic differentiation 6
  7. 3.3 Computer model We started the implementation of the above described model. It has been implemented with the Cormas platform using Smalltalk object-oriented language. The time step of this model is the week. We can create artificially a topology. In the figure we created 4000 plots belonging to 4000 farmers. At initialisation 33% of the farmers are poor, 33% medium, and 33% are rich farmers. The rainfall is randomly drawn. What is presented below is just an illustration: only the decisions making of the farmers have been implemented. The transitions from an economic state to another are non-documented. The behaviour and decisions of other agents is not implemented yet. Topology Economic conditions Figure 3. Two snapshots of interface at initialisation representing a point of view on the topology and the initial economic conditions (uniform random distribution). 7
  8. The following results are given as illustrations of the type of model output. One can observe which crops the farmers have chosen. Figure 4. Snapshot of the interface during the simulation It is also possible to define and observe some aggregated indicators. For instance it is possible to observe the economic differentiation for the full set of farmers or for specific set of farmers. For instance in the following figure, again given as an illustration, one can observe the evolution of the proportion of the different economic classes. Figure 5. Example of indicators: Differentiation of economic conditions for the full set of agents 8
  9. 4 PERSPECTIVES This modeling experiment is an on-going process, which will be assessed in November 2002. This paper was written as a presentation of the process and a mid-term report. The objective is to stimulate discussion. After this preliminary work, the perspectives are: • To complete and validate the conceptualisation of the model presented above. The decisions making process of the households has to be validated, and we need to input knowledge on economic dynamics and social decisions for water control. • To implement the conceptual model in Cormas software. This tool also allows to import data form GIS. For instance we can use the map of soil quality, as this is the main biophysical parameter in our model (figure 6). Thus, it will be possible to simulate the model on landscapes similar to reality and validate the model by comparison between simulated data and data from the field. Figure 6. Soil Characteristics • To define the use of such model. As any simulation model, it can be used to explore different scenarios to enhance the understanding of the scientists and help them communicate their results. It is possible to go further. We try to use these models in what we call companion- modelling approach. It means that we try to develop and use these models with the different stakeholders to help better decisions and coordination. For that objective, we have developed a methodology based on the coordinated use of role games, interviews and simulations with the stakeholders. This allows discussions among stakeholders and the model can be used to help decision makers at different levels. This mediation approach can be used on the Bac Lieu case study. It requires the agreement and participation of the institution in charge of the decisions, the appropriation of the methodology by local and legitimised scientists and their medium –term involvement to follow-up the process and its impact. 9
  10. 5 REFERENCES Barreteau, O. and Bousquet, F. 2000. SHADOC: a Multi-Agent Model to tackle viability of irrigated systems. Annals of Operations Research 94:139-162. Bilderberg. 1999. Integrated Natural Resource Management, The Bilderberg Consensus. in. Bousquet, F., Bakam, I., Proton, H. and Le Page, C. 1998. Cormas: Common-Pool Resources and Multi-Agent Systems. Lecture Notes in Artificial Intelligence 1416:826-837. Bousquet, F., Barreteau, O., Le Page, C., Mullon, C. and Weber, J. 1999. An environmental modelling approach. The use of multi-agents simulations. Pages 113-122 in F. Blasco and A. Weill, editors. Advances in Environmental and Ecological Modelling. Elsevier, Paris. Bousquet, F., LePage, C., Bakam, I. and Takforyan, A. 2001. Multi-agent simulations of hunting wild meat in a village in eastern Cameroon. Ecological modelling 138:331-346. Castella, J. C., Boissau, S., Lan Anh, H. and Husson, O. 2001. Enhancing communities' adaptability to a rapidly changing environment in Vietnam uplands: the SAMBA role-play. in J. Suminguit, Caidic, J, editor. Sustaining Upland Development in Southeast Asia: Issues, Tools & Institutions for Local Natural Resource Management. SANREM CRSP/ Southeast Asia, Makati, Metro Manila, Philippines. Ferber, J. 1999. Multi-Agent Systems : an Introduction to Distributed Artificial Intelligence. Addison-Wesley, Reading, MA. Gilbert, N. 1995. Emergence in social simulation. Pages 144-156 in R. Conte and N. Gilbert, editors. Artificial societies. The computer simulation of social life. UCL Press. Hoanh, C. T., Tuong, T. P., Kam, S. P., Phong, N. D., Ngoc, N. V. and Lehmann, E. 2001. Using GIS-linked hydraulic model to manage conflicting demands on water quality for schrimp and rice production in the Mekong river delta, vietnam. Pages 221-226 in F. Ghassemi, M. McAleer, L. Oxley and M. Scoccimaro, editors. Modsim 2001. The Modelling and Simulation Society of Australia and New Zealand (MSSANZ),, Canberra, Australia. Rouchier, J., Bousquet, F., Requier-Desjardins, M. and Antona, M. 2001. A multi- agent model for transhumance in North Cameroon. Journal of Economic Dynamics and Control 25:527-559. Weiss, G., editor. 1999. Multiagent Systems : a Modern Approach to Distributed Artificial Intelligence. MIT Press. 10


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