François Bousquet

Modelling with stakeholders

Stakeholder engagement, collaboration, or participation, shared learning or fact-finding, have become buzz words and hardly any environmental assessment or modelling effort today can be presented without some kind of reference to stakeholders and their involvement in the process. This is clearly a positive development, but in far too many cases stakeholders have merely been paid lip service and their engagement has consequentially been quite nominal. Nevertheless, it is generally agreed that better decisions are implemented with less conflict and more success when they are driven by stakeholders, that is by those who will be bearing their consequences. Participatory modelling, with its various types and clones, has emerged as a powerful tool that can (a) enhance the stakeholders knowledge and understanding of a system and its dynamics under various conditions, as in collaborative learning, and (b) identify and clarify the impacts of solutions to a given problem, usually related to supporting decision making, policy, regulation or management. In this overview paper we first look at the different types of stakeholder modelling, and compare participatory modelling to other frameworks that involve stakeholder participation. Based on that and on the experience of the projects reported in this issue and elsewhere, we draw some lessons and generalisations. We conclude with an outline of some future directions.

Cormas: Common-Pool Resources and Multi-agent Systems

This paper describes a simulation environment, called Cormas, that relies on multi-agent systems and has been achieved in Smalffalk, using VisualWorks software. Such a simulation tool may prove useful to better understand the complex interactions between natural and social dynamics when studying renewable resource management. The general principles of the Cormas platform are first presented, then the implementation is described. Two models built with Cormas allow to illustrate the use and the genericity of this tool.

Multi-Agent Based Simulation

Messy systems have no clear boundaries; they are composed of so many natural and/or artificial entities with patterns of interaction so dense that they cannot be understood by inspection and system behaviour cannot be predicted by statistical or qualitative analysis. Obvious examples are real social systems and the Internet. Analysing and designing software to exploit such systems requires a different approach to software engineering and mechanism design. The issue addressed in the MABS-2000 workshop and in this volume is the development of a methodology and technology to identify which techniques hold promise and which cannot possibly lead to useful applications for messy software or social systems. 1 Messy Systems and Tidy Systems Much of AI and the great preponderance of papers at conferences such as ICMAS relate to software systems with clear boundaries and well understood relationships among known entities. The perceived development path is by means of step-by-step development of well understood formalisms and algorithms. These are tidy techniques for tidy systems. Real systems are frequently messy. The boundaries of action and opportunity are unclear and probably unknowable. Relationships among entities are either too complex to be understood or shifting faster than we can identify them. In short, we frequently do not know enough about the systems to make them amenable to tidy techniques for toy systems. The differences between messy and tidy systems have been recognised for some considerable time by the agents research community. Wooldridge and Jennings [20] argued that “[i]f a system contains many agents..., then the dynamics can become too complex to manage effectively. There are several techniques that one can use to try to manage a system in which there are many agents. First, one can place it under central control... Another way... is to severely restrict the way in which agents can interact... one can ensure that there are few channels of communication... [or] by restricting the way in which agents interact. Thus very

SHADOC: a multi‐agent model to tackle viability of irrigated systems

The viability of irrigated systems in the Senegal River Valley is being brought into question today due to their under‐utilization. We assume that their viability depends largely on the way their different components behave and interact. We therefore sought to examine in greater depth todays knowledge of the structure of these systems and activities performed within them. This led to the development of a multi‐agent system model, a kind of virtual irrigated system, with a special focus on rules in use for access to credit, water allocation and cropping season assessment as well as organization and coordination of farmers. The purpose of this paper is to show how this kind of tool is relevant to the study of irrigated systems viability. As an example it is used to examine the influence of existing social networks on the viability of irrigated systems.

Position Paper: Modelling with stakeholders

Stakeholder engagement, collaboration, or participation, shared learning or fact-finding, have become buzz words and hardly any environmental assessment or modelling effort today can be presented without some kind of reference to stakeholders and their involvement in the process. This is clearly a positive development, but in far too many cases stakeholders have merely been paid lip service and their engagement has consequentially been quite nominal. Nevertheless, it is generally agreed that better decisions are implemented with less conflict and more success when they are driven by stakeholders, that is by those who will be bearing their consequences. Participatory modelling, with its various types and clones, has emerged as a powerful tool that can (a) enhance the stakeholders knowledge and understanding of a system and its dynamics under various conditions, as in collaborative learning, and (b) identify and clarify the impacts of solutions to a given problem, usually related to supporting decision making, policy, regulation or management. In this overview paper we first look at the different types of stakeholder modelling, and compare participatory modelling to other frameworks that involve stakeholder participation. Based on that and on the experience of the projects reported in this issue and elsewhere, we draw some lessons and generalisations. We conclude with an outline of some future directions.

Adapting Science to Adaptive Managers: Spidergrams, Belief Models, and Multi-agent Systems Modeling

Two case studies are presented in which models were used as focal tools in problems associated with common-pool resource management in developing countries. In the first case study, based in Zimbabwe, Bayesian or Belief Networks were used in a project designed to enhance the adaptive management capacity of a community in a semiarid rangeland system. In the second case study, based in Senegal, multi-agent systems models were used in the context of role plays to communicate research findings to a community, as well as to explore policies for improved management of rangelands and arable lands over which herders and farmers were in conflict. The paper provides examples of the use of computer-based modeling with stakeholders who had limited experience with computer systems and numerical analyses. The paper closes with a brief discussion of the major lessons learned from the two independent case studies. Perhaps the most important lesson was the development of a common understanding of a problem through the development of the models with key stakeholders. A second key lesson was the need for research to be adaptive if it were to benefit adaptive managers. Both case study situations required significant changes in project orientation as stakeholder needs were defined. Both case studies recognized the key role that research, and particularly the development of models, played in bring different actors together to formulate improved management strategies or policies. Participatory engagement with stakeholders is a time-consuming and relatively costly process in which, in the case studies, most of the costs were born by the research projects themselves. We raise the concern that these activities may not be widely replicable if such costs are not reduced or born by the stakeholders themselves. (Resume dauteur)

Companion Modeling, Conflict Resolution, and Institution Building: Sharing Irrigation Water in the Lingmuteychu Watershed, Bhutan

We used multi-agent systems (MAS), following the companion modeling method, to facilitate water management negotiations in Bhutan. We show how this methodology helped resolve a conflict over the sharing of water resources by establishing a concrete agreement and creating an institution for collective watershed management. The conceptual model begins with a role-playing game (RPG). The stakeholders play the game, thus validating the proposed environment, the behavioral rules, and the emergent properties of the game. It is then relatively easy to translate the RPG into computerized MAS that allow different scenarios to be explored. After this first step in the MAS model, stakeholders then create an institution. A second model is developed to facilitate this process. We conclude by discussing the relationship between the models and reality, as well as the use of MAS as a mediation tool and the social process. (Resume dauteur)

Multi-agent based simulation II

This volume presents extended and revised versions of the papers presented at the Third International Workshop on Multi-Agent Based Simulation (MABS 2002), a workshop federated with the First International Joint Conference on Autonomous Agents and Multi-Agent Systems (AAMAS 2002), which was held in Bologna, Italy, in July, 2002. One aim of the workshop was to develop stronger links between those working in the social sciences and those involved with multi-agent systems. We are pleased to note that many important conferences in various disciplines such as geography, economics, ecology, sociology, and physics have hosted workshops on MABS-related topics and that many respected journals publish papers that include elements of MABS. But although MABS is gradually acquiring legitimacy in many disciplinary fields, much remains to be done to clarify the potential use of MABS in these disciplines. Researchers from these disciplines have different points of view on issues such as time-frame, space, geographical scales, organizational levels, etc. Moreover, the interest in MABS goes beyond the scientific community, as MABS models have been developed and used interactively with other communities as well. For instance, research is being done on the interactions between societies of robots and groups of people, and simulation models are being developed with stakeholders for environmental issues in a participatory way, through the Internet or directly in the field. These new approaches lead to new research questions regarding the use of MABS for collective decision making, but also regarding the conceptual and technical aspects of MABS. Within this framework of interactions between artificial and human societies, special attention was given to the conceptual and technical aspects (agent architecture, interaction protocols, simulation platforms, modeling protocols, time and space representation, presentation of simulation results) resulting from these interactions and favoring them. (Resume dauteur)This paper is devoted to exploring the relationships between computational agents, as they can be found in multi-agent systems (MAS) or Distributed Artificial Intelligence (DAI), and the different techniques regrouped under the generic name “multi-agent based simulation” (MABS). Its main purpose is to show that MABS, despite its name, is in fact rarely based on computational agents. We base our demonstration on an innovative presentation of the methodological process used in the development of current MABS systems. This presentation relies on the definition of the different roles involved in the design process, and we are able to show that the notion of “agent”, although shared at a conceptual level by the different participants, does not imply a systematic use of computational agents in the systems deployed. We then conclude by discussing what the use of computational agents, based on the most interesting research trends in DAI or MAS, might provide MABS with.

SINUSE: a multi-agent model to negotiate water demand management on a free access water table

Abstract Many water tables are currently overexploited throughout the world. This situation raises the question of their management. Integrated management of such systems, established in both supply and demand areas, calls for thorough knowledge of the functioning of both the water table and its users, so models are usually required. This study is based on the case of the Kairouan water table, located in Tunisia, which has been continuously and globally decreasing for more than 20 years, due to overexploitation by private irrigators. The field study led to the hypothesis that the dynamics of the system is heavily influenced by local interaction between the resource and its users, and by direct, non-economic interaction between the farmers. The literature shows that several kinds of model have already been used to represent interaction between a water table and its users but none of them are able to take this kind of social behaviour into account. The simulator of a water table and user interaction (SINUSE) based upon multi-agent systems enabled us to overcome these limitations. This model proved to be very useful for representing a complex and distributed system such as the Kairouan water table. It enabled us to explore the interaction between the physical and socio-economic components of the system and to conclude that local and non-economic behaviour do have a major impact on the global dynamics of the system and must therefore be taken into account. The management interventions simulated with the SINUSE model have raised interesting questions, leading to the conclusion that this model could provide a useful tool for negotiating the integrated management of the water table system. Though this model is rather specific, the approach developed could be transferred to other water table systems, to improve the knowledge of their functioning and examine the possible impacts of different management tools.

A multi-agent model for describing transhumance in North Cameroon : Comparison of different rationality to develop a routine

This paper introduces an application using multi-agent system to model an artificial society. The work follows a field research in economics in Cameroon about the behavior of nomad herdsmen securing their access to rangelands. Two central elements were stressed: regularity in meetings and flexibility while facing problems. Experiments run through simulations, testing two rationales: choices based on the notion of cost, or herdsmen taking into account the history of meetings. The system successfully shows regular dynamics, and huge differences are observed, depending on the rationality used. We conclude on the importance of the rationality described in modeling a system.