Investigating social conflicts linked to water resources trhough agent-based modelling

International audienceOver the next decades, natural resources and water resources in particular are likely to be one of the major origins of social conflicts. To date however, no model enables the study of coupled dynamics of hydrology, water use and social conflicts. Building such a model requires identifying the key concepts, entities and processes that are present in much field cases and have to be included into the model. The research presented in this paper takes place in a more general project named MAELIA for Multi-agent for Environmental Norms Impact Assessment. The purpose of MAELIA is to provide a decision-support model helping decision makers and stakeholders to manage water resources. This model aims to be generic enough for it to be applied in various field cases at different scales. We describe the actors involved in water management or water use using an agent-based approach. Water monitoring institutions and water users are described as agents in interaction within a stylised representation of a watershed basin. We propose a conceptual model that describes not only the hydrology in the basin and the water consumption behaviour of users, but also the representation of both the users at the institutional level and the power relationships that determine the arbitration of norms about water use. We propose two possible uses of this model. The first is the analysis of the impacts of several norms for detecting potential conflicts. The second possible use explores the local formulation of norms given the balance of powers in already settled social conflicts. This generic platform modelling conflicts on natural resources may thereby provide new insights into the analysis of well-known natural resource related conflicts, such as the Gauvery dispute in India

Combination Framework of BI solution & Multi-agent platform (CFBM) for multi-agent based simulations

International audienceIntegrated environmental modeling in general and specifically Multi-agent-based modeling and simulation approach are increasingly used in decision-support systems with, as a major consequence, to manipulate and generate a huge amount of data for their functioning (parametrization, use of real data in the simulation, ...). Therefore there is a need to manage efficiently these data being either used or generated by the simulation. Practically, existing general-ist simulation platforms lack database access and analysis tools and simulation outputs are usually stored as text files or spreadsheets to be manipulated later by dedicated tools. In this paper, we propose a solution to handle simulation models data, i.e. their outputs as well as corresponding real data. We designed a conceptual framework based on a combination of two components, a Business Intelligence (BI) solution and a multi-agent platform. Such a framework aims at managing simulation models data throughout the lifespan of the simulation, from its execution and its coupling with real data to the generation of simulation results order to use the simulation model as an effective decision-support sys-tem with what-if scenarios

To Calibrate & Validate an Agent-Based Simulation Model - An Application of the Combination Framework of BI solution & Multi-agent platform

National audienceIntegrated environmental modeling approaches, especially the agent-based modeling one, are increasingly used in large-scale decision support systems. A major consequence of this trend is the manipulation and generation of huge amount of data in simulations, which must be efficiently managed. Furthermore, calibration and validation are also challenges for Agent-Based Modelling and Simulation (ABMS) approaches when the model has to work with integrated systems involving high volumes of input/output data. In this paper, we propose a calibration and validation approach for an agent-based model, using a Combination Framework of Business intelligence solution and Multi-agent platform (CFBM). The CFBM is a logical framework dedicated to the management of the input and output data in simulations, as well as the corresponding empirical datasets in an integrated way. The calibration and validation of Brown Plant Hopper Prediction model are presented and used throughout the paper as a case study to illustrate the way CFBM manages the data used and generated during the life-cycle of simulation and validation

Influence of incentive networks on landscape changes: A simple agent-based simulation approach

International audienceThe aim of this paper is to implement a simple model for exploring the influence of different multi-scale incentive networks affecting farmer decision on landscape changes. Three scales of networks are considered: a global ‘policy’ network promoting specific land uses, an intermediate ‘social’ network where land use practices are shared and promoted collectively and a local ‘neighborhood’ network where land use practices are influenced by those of their neighbors. We assess the respective and combined influence of these networks on landscape pattern (fragmentation and heterogeneity) and dynamics, taking into account agronomic constraints (assimilated to crop successions). Simulations show that combination of incentive networks does not have linear and/or cumulative influence on landscape changes. Comparison of simulated scenarios highlights that a combination of two networks tends to improve landscape heterogeneity and fragmentation; scenarios combining all networks could lead to two opposite landscape configuration illustrating emergence of landscape dynamics. Finally, this study emphasizes that landscape complexity has also to be understood through the multiplicity of pathways of landscape changes rather than the assessment of the resulting landscape patterns

The MAELIA multi-agent platform for integrated assessment of low-water management issues

International audienceThe MAELIA project is developing an agent-based modeling and simulation platform to study the environmental, economic and social impacts of various regulations regarding water use and water management in combination with climate change. It is applied to the case of the French Adour-Garonne Basin, which is the most concerned in France by water scarcity during the low-water period. An integrated approach has been chosen to model this social-ecological system: the model combines spatiotemporal models of ecologic (e.g. rainfall and temperature changes, water flow and plant growth) and socio-economic (e.g. farmer decision-making process, management of low-water flow, demography, land use and land cover changes) processes and sub-models of cognitive sharing among agents (e.g. weather forecast, normative constraints on behaviors

Impact Assessment Modeling of Low-Water Management Policy

International audienceWe briefly present the main steps involved in designing and developing a platform for the numerical simulation of environmental and social impacts of the implementation of new environmental norms related to low-water management in France (MAELIA Project: multi-agents for environmental norms impact assessment). Some results are highlighted concerning in particular the structure of the underlying low-water management model and the process and agents' activity modeling

: Recueil de fiches pédagogiques du réseau MAPS

DoctoralLe réseau thématique MAPS «Modélisation multi-Agent appliquée aux Phénomènes Spatialisés » propose depuis 2009 des évènements scientifiques ayant pour but de diffuser les pratiques de modélisations multi-agents au sein des Sciences de l’Homme et de la Société (SHS). Ce collectif pluridisciplinaire de chercheurs, d’enseignants-chercheurs et de doctorants est labellisé en tant que ≪ réseau thématique » par le Réseau National des Systèmes Complexes (GIS RNSC) et bénéficie du soutien du CNRS au titre de la Formation Permanente. Depuis 2009, plusieurs modèles ont été développés au cours d'événements MAPS. Ces modèles ont fait l'objet de fiches pédagogiques détaillées destinées aux communautés éducatives et universitaires et en particulier aux enseignants qui souhaiteraient faire découvrir la modélisation à leurs étudiants, mais aussi à ceux qui envisagent d’approfondir certains aspects avec un public plus averti. Elles sont également destinées à tous les curieux qui souhaiteraient découvrir ce que la modélisation apporte aux SHS, du point de vue heuristique et du point de vue opérationnel. Enfin, elles sont aussi des supports pour toutes les personnes qui souhaiteraient diffuser les réflexions scientifiques sur la modélisation et la simulation qui ont présidé à la rédaction de ces fiches

Simulation sociale orientée agent: Introduction

National audienceLa simulation orientée agent est maintenant une solution largement utilisée pour la modélisation de phénomènes sociaux. Cependant, si la méthode s'est répandue assez rapidement, le cheminement n'a pas toujours été facile : elle a dû se positionner de manière crédible face à d'autres approches établies de longue date, affronter des questions d'ordre épistémologique, dialoguer avec différentes disciplines notamment les sciences humaines et sociales et résoudre les difficultés pratiques inhérentes à toute nouvelle approche. Néanmoins, les réalisations à ce jour sont remarquables, des défis importants ont été surmontés, de nouveaux ont émergé et malheureusement ou heureusement certains restent encore non résolus. La simulation sociale orientée agent est un domaine véritablement pluridisciplinaire qui s'est créé en intégrant les contributions de l'intelligence artificielle et de l'intelligence artificielle distribuée, de la théorie des systèmes complexes et des sciences sociales, sciences cognitives et sociologie en particulier. Si ces différentes disciplines ont largement contribué à la construction du domaine, la simulation sociale a fourni en retour à ces disciplines une nouvelle approche, de nouveaux outils et/ou une nouvelle méthodologie. Ces apports nouveaux ont fait évoluer leur pratique, le regard porté sur leurs objets d'étude respectifs et plus largement la compréhension que nous avons du monde dans lequel nous vivons. L'objectif de ce numéro spécial est de donner une image de la situation actuelle de la simulation sociale orientée agent, en exposant à la fois l'existant et les directions de recherche dans ce domaine. Nous avons été attentifs à ce que les articles retenus maintiennent un bon équilibre entre les sujets traitant de préoccupations d'ordre général, telles que l'applicabilité de l'approche BDI dans le cadre de la SOA, et les implémentations plus spécifiques de systèmes qui illustrent la manière dont certaines questions critiques en simulation sociale orientée agent peuvent être surmontées

Simulation sociale orientée agent

Revue d'Intelligence Artificielle: http://ria.revuesonline.com/resnum.jsp?editionId=1447National audienceno abstrac

Persuasion dynamics

8 Oct 2004, soumis à Physica AWe here discuss a model of continuous opinion dynamics in which agents adjust continuous opinions as a result of random binary encounters whenever their difference in opinion is below a given threshold. We concentrate on the version of the model in the presence of few extremists which might drive the dynamics to generalised extremism. The intricate regime diagram is explained by a combination of meso-scale features involving the first interaction step