How to model it: Ecological models, in particular simulation models, often seem to be formulated ad hoc and only poorly analysed. I am therefore interested in strategies and methods for making ecological modelling more coherent and efficient. The ultimate aim is to develop preditive models that provide mechanstic understanding of ecological systems and that are transparent and structurally realistic enough to support environmental decision making.

Pattern-oriented modelling: This is a general strategy of using multiple patterns observed in real systems as multiple criteria for chosing model structure, selecting among alternative submodels, and inversely determining entire sets of unknown model parameters.

Individual-based and agent-based modelling: For many, if not most, ecological questions individual-level aspects can be decisive for explaining system-level behavior. IBM/ABMs allow to represent individual heterogeneity, local interactions, and/or adaptive behaviour

Ecological theory and concepts: I am particularly interested in exploring stability properties like resilience and persistence.

Modelling for ecological applications: Pattern-oriented modelling allows to develop structurally realistic models, which can be used to support decision making and the management of biodiversity and natural resources. Currently, I am involved in the EU project CREAM, where a suite of population models is developed for pesticide risk assessment.

Standards for model communication and formulation: In 2006, we published a general protocol for describing individual- and agent-based models, called the ODD protocol (Overview, Design concepts, details). ODD turned out to be more useful (and needed) than we expected.

Former professor at the University of Neuchatel in Switzerland and now senior researcher at CIRAD in France, I am doing research on artificial intelligence since 1984. Having begun with logic programming, I naturally applied logics and its extensions (i.e. modal logics of various sorts) to specify agent behaviour. Since 1987, I moved both to embedded intelligence (using mobile robots) and multi-agent systems applied, in particular, to job-shop scheduling and complex system simulation and design. Since 2001, I exclusively work on modelling and simulation of socio-ecosystems in a multidisciplinary team on renewable resources management (GREEN). I am focusing on modelling complex systems in a multi-disciplinary (economist, agronomist, sociologists, geographers, etc.) and multi-actor (stakeholders, decision makers) setting. It includes:
- representing multiple points of view at various scales and levels on a complex socio-ecosystem, using ontologies and contexts
- representing the dynamics of such systems in a variety of formalisms (differential equations, automata, rule-based systems, cognitive models, etc.)
- mapping these representations into a simulation formalism (an extension of DEVS) for running experiments and prospective analysis.
This research is instantiated within a modelling and simulation platform called MIMOSA (http://mimosa.sourceforge.net). The current applications are the assessment of the sustainability of management transfer to local communities of the renewable ressources and the dynamics of agro-biodidversity through networked exchanges.