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KIMONO, a Descriptive Agent-Based Modelling Method for the Exploration of Complex Systems: an Application to Epidemiology

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KIMONO, a Descriptive Agent-Based Modelling Method for the Exploration of Complex Systems: an Application to Epidemiology KIMONO, a descriptive agent-based modelling method for the exploration of complex systems: an application to epidemiology. Edouard Amouroux To cite this version: Edouard Amouroux. KIMONO, a descriptive agent-based modelling method for the exploration of complex systems: an application to epidemiology.. Modeling and Simulation. Université Pierre et Marie Curie - Paris VI, 2011. English. tel-00630779 HAL Id: tel-00630779 https://tel.archives-ouvertes.fr/tel-00630779 Submitted on 5 Dec 2011 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. THESE DE DOCTORAT DE L’UNIVERSITE PIERRE ET MARIE CURIE Spécialité: Informatique (Ecole doctorale: EDITE) Présentée par Edouard AMOUROUX Pour obtenir le grade de DOCTEUR de l’UNIVERSITÉ PIERRE ET MARIE CURIE Sujet de la thèse : KIMONO: une méthode de modélisation descriptive centrée agent pour l'explication des systèmes complexes, une application en épidémiologie soutenue le 30/09/2011 devant le jury composé de : M. Directeur de thèse: Alexis Drogoul, Directeur de Recherche, IRD - UMMISCO / IFI - MSI Rapporteurs: David Hill, Professeur des universités, Université Blaise Pascal, LIMOS UMR CNRS 6158 François Roger, Directeur de recherche, CIRAD, AGIRs Faculty of Veterinary Medicine, Kasetsart University Examinateurs: Jean-François Perrot Professeur émérite, Université Pierre et Marie Curie Éric Ramat Professeur Université du littoral Côte d'Opale Ho Tuong Vinh, Professeur, Institut de la Francophonie pour l'Informatique (IFI) - Équipe de recherche MSI Nicolas Marilleau Ingénieur de recherche, IRD - UMMISCO Université Pierre & Marie Curie - Paris 6 Bureau d’accueil, inscription des doctorants et base de données Esc G, 2ème étage 15 rue de l’école de médecine 75270-PARIS CEDEX 06 Tél. Secrétariat : 01 42 34 68 35 Fax : 01 42 34 68 40 Tél. pour les étudiants de A à EL : 01 42 34 69 54 Tél. pour les étudiants de EM à MON : 01 42 34 68 41 Tél. pour les étudiants de MOO à Z : 01 42 34 68 51 E-mail : [email protected] 2 Abstract Since a few years, in many different domains (e.g. sociology, ecology or economy), there is a growing trend in designing models that can be used for exploratory purposes rather than purely predictive ones. Loosely following a «complex systems» paradigm, exploratory models are often based on explicit and detailed representations of the components of the systems studied, and offer a large degree of freedom in the parameter or structural adjustments available to their final users (researchers, decision-makers or stakeholders). They are intended to be used as a support of «as-if experiments» as they allow, through these adjustments, for the formulation of detailed hypotheses at various levels of description of the system. These hypotheses then lead to the generation of scenarios whose outcomes are explored and compared by way of repeated simulations. Epidemiology is an interesting example of this situation. Its long modelling history can be characterised as a search for simple predictive models, but recent examples like the outbreaks of avian influenza in South-East Asia have shown their limits: without properly taking into account the interplays between social, ecological and biological dynamics to understand how this pandemic evolves, these models become useless as far as prediction goes. And when some of these dynamics are tentatively taken into account, the resulting models often become dependent on incomplete or qualitative data (for instance, the decision-making processes of social actors or the behaviours of birds), which prevents them to be used for any serious predicting purposes. As a consequence, there has been a recent shift of focus of the community on the design of exploratory models, which are meant to allow understanding the links between these dynamics, generating and studying various hypotheses, and measuring, with respect to these hypotheses, the impact of local or global policies in complex scenarios. However, designing and using such models gives rise to serious methodological issues and existing modelling and simulation methods do not cope very well with this new type of models. When they are adapted to take their peculiarities into account, this often results in ad hoc solutions, which can barely be reused for other models in the same domain, let alone in different domains. The objective of this thesis is to propose a domain-independent method (KIMONO) to facilitate the design and use of such exploratory models. Based on a series of examples from various domains (road traffic, social segregation, soil dynamics and more extensively epidemiology), I proceed from an account of the design requirements (taking conflicting and evolving hypotheses into account during the modelling process, producing highly-modular models, enabling an iterative modelling cycle, allowing for the collaboration of different experts and the combination of different formalisms, etc.) to a concrete proposal involving dedicated computer tools and a common accessible formalism, both aimed at facilitating the collaboration, communication and the implementation of «world models» (the name given in this proposal to open exploratory models). The method I propose focuses on two elements: the implication of the experts and a detailed representation of the system. Experts are at the centre of the modelling process, which starts with extensive descriptions of their knowledge, possibly reusing their formalism, and further proceeds through iterative amendments (of increasing or decreasing complexity) that they are able to evaluate and validate in interaction with the modellers. The iterative process comes to an end when the experts estimate that they have a sufficient insight of the system or when further investigations require field experiments. Regarding the kind of representation suitable for supporting this process, I propose an adaptable and modular combination of two implementation systems: Agent-Based Modelling (ABM) and Geographical 3 Information Systems (GIS). I show that this combination provides for an arbitrary level of description of the components of a system, that it allows both qualitative and quantitative knowledge to be equally represented and that it supports a high level of evolution of the hypotheses during the modelling process. The interactions between modellers and experts are based on two abstractions of these implementation details, using both the ODD (Overview, Design concepts, Details) protocol for communication purposes, and the GAML modelling language for the collaborative programming of the model. The method proposed has been applied and validated in the context of a large study undertaken in South-East Asia (especially North-Vietnam) by epidemiologists and veterinarians to understand the role of various hypotheses in explaining the recurring outbreaks of the avian influenza epidemics among domestic poultry. During a four years long interdisciplinary collaboration, several «world models» have been co- designed, implemented on the GAMA platform and used as «virtual laboratories» by experts. This collaboration, and its unique outcomes, have allowed them to test a broad range of hypotheses (especially on the local conditions of persistence), better understand the role of various spatial, ecological or social factors in the survival and propagation of the virus and reorient some of their field studies in consequence. 4 Résumé Depuis plusieurs années, on peut observer une tendance, dans de nombreux domaines (Sociologie, Ecologie, Economie, etc.), à construire des modèles vers l'exploration des systèmes qu’ils représentent que vers la prédiction ou l’explication. S’inscrivant dans le paradigme des "systèmes complexes", ces modèles exploratoires utilisent généralement une représentation explicite et détaillée des composants du système étudié. Ils offrent aussi aux utilisateurs finaux (chercheurs, décideurs, parties prenantes) une grande liberté d'adaptation en termes de paramètres et de structure du modèle. Ces modèles servent de support à des expériences "as-if" en permettant, au travers de ces ajustements, la formulation d'hypothèses détaillées et ce à différents niveaux de descriptions du système. A partir de ces hypothèses, des scénarios sont construits, dont les résultats sont explorés et analysés grâce à des simulations répétées. L'épidémiologie est exemplaire de cette situation. Elle a une longue histoire de modélisation qui peut être caractérisée comme la recherche de modèles essentiellement prédictifs. Cependant, certaines situations, comme l'émergence de foyers épidémiques de grippe aviaire en Asie du Sud Est, montre la limite d'une telle approche: sans une prise en compte adéquate des interactions entre les dynamiques sociales, écologiques et biologiques, l'utilisation de modèles prédictifs est sans fondement. De plus, dès lors que l'on tente de prendre en compte ces dynamiques, les modèles deviennent dépendants de données incomplètes ou qualitatives (le processus de décision des acteurs sociaux ou bien le comportement des oiseaux, par exemple). En conséquence, on assiste
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