CatchScape: An integrated multi-agent model for simulating water management at the catchment scale

Abstract: Due to mounting human pressure, stakeholders in northern Thailand are facing crucial natural resources management issues. Among others, the impact of upstream irrigation management on the downstream agricultural viability is a usual source of conflict. It has often both biophysical and social origins. As different ethnic groups with tense relationships are involved, appropriate solutions should only emerge from negotiation. CATCHSCAPE has been developed through a Multi-Agent System approach that enables us to describe the whole catchment features as well with farmer's individual decisions. The biophysical modules simulate the hydrological system with its distributed water balance, irrigated schemes management, crop and vegetation dynamics. The social dynamics are described as a set of resources management processes (water, land, cash, labour force). Water management is described according to the actual different levels of control (individual, scheme and catchment. Moreover, the model's architecture is presented in a way that emphasizes the transparency of the rules and methods implemented. Finally, one simulated scenario is described with its main results as well, according to different viewpoints (economy, landscape, water management)

A methodology for identifying and formalizing farmers’ representations of watershed management: a case study from northern Thailand

Linking modeling tools and the participatory approach for development is not a common combination. Participatory multi-agent system modeling (PMASM) is a tool for sharing viewpoints among stakeholders and facilitating the negotiation process. A key question of this approach is the acquisition and the modeling of the various stakeholders’ representations. Our research team, whose Asian branch is represented in this book, tries to formalize the passage from fieldwork to the model by defining a methodology that can be implemented in the field. This methodology adapts knowledge engineering acquisition techniques to in-field stakeholders’ representations for PMASM. In a northern Thailand watershed, we pursued implementation tests of this methodology. We first explored two ways to tackle fieldwork (ethnographic and project surveys), both showing weaknesses and strengths. We then built a first-version diagram syntax used for representing individual farmers’ representations, and we considered options for analyzing those diagrams. Finally, we tested the elicited representations by leading farmers, through game-like sessions, to rebuild a model of their system structured by elements and links. Results reveal a great heterogeneity of farmers’ representations, which we intend to manage by establishing farmers’ synthetic profiles based on their orientations toward specific elements and aspects of their social and natural environment. Orientations of those profiles convey different conceptions of the functioning of the system with which farmers interact. This also results in decisions and reactions to issues that are different from one profile to another. The identification and formalization will contribute to the implementation of a computer model of farmers’ representations. Perspectives are drawn on two ways to integrate representations into the modeling

Agent-based spatial simulation with NetLogo : volume 1 : introduction and bases

The analysis of complex systems (CS) involves the use of different points of view so as to be able to understand the different aspects of the system’s entities, and to characterize their interactions. Modeling and simulating CS are activities where collaboration between researchers is the rule rather than the exception. Indeed, as a result of the complexity of studied systems, it is often essential to bring together different perspectives from different fields of study. Thus, the popular idea of a solitary researcher that is able to collect data, put together conceptual models and use these with computing tools, is gradually being replaced with that of an interdisciplinary group usually composed of thematicians, computer scientists and mathematicians. For such a group, the model (or the simulation) is at once: (1) the reason for their coming together, (2) their collective objective and (3) the basis for their collective work

Agent-based spatial simulation with NetLogo : volume 1 : introduction and bases

This chapter will aim to present good practice in, and the benefits of, formalization in modeling multiagent systems (MAS). To achieve this, the authors will first reiterate the usefulness of modeling systems, while placing the paradigms associated with a multiagent approach in context. Then, they will argue that the use of graphic modeling languages enhances the exchanges between the parties involved in the design of an MAS. Following this, two types of graphic models based on the same semantic base are presented: Unified Modeling Language (UML) and Agent Modeling Language (AML). The first graphic model is intended for general use and facilitates its users to analyze the ontology and dynamics of the modeled system. The second graphic model uses paradigms specific to agents and facilitates its users to create a design which is closer to the MAS which will be produced. After having discussed the relative merits of each of these graphic model types and presented some possible extensions, the chapter discusses the utility of, and a method for, documenting a multiagent model. In order to do this, the Overview, Design concepts, Details (ODD) protocol, which guides the modeler in the creation of a documentation of the objectives, constitutive elements and specific properties of the model, is presented