CoOPILOT: Designing an Integrated Platform for Participation & Transition Engineering
2023
Ferrand, Nils | Tronçon, Samuel | Gestion de l'Eau, Acteurs, Usages (UMR G-EAU) ; Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Bureau de Recherches Géologiques et Minières (BRGM)-Institut de Recherche pour le Développement (IRD)-AgroParisTech-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro Montpellier ; Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro) | Sustainability transition, environment, economy and local policy (STEEP) ; Centre Inria de l'Université Grenoble Alpes ; Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jean Kuntzmann (LJK) ; Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP) ; Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP) ; Université Grenoble Alpes (UGA) | Resurgences R&D [Arles] (RR&D)
Participatory policy making, in multi-stakeholders, inter-sectoral and multi-level contexts, is an intricate process which requires addressing various needs, constraints and steps.Moreover, beyond the in-presence protocols which we have implemented, for a sake of social extension (“massification”), autonomization and compliance with current practices, the switch to digital solutions is obviously required. For participants who are often reluctant to engage in a long, complex and sometimes doubtful pathway, it appears crucial to support them step after step in the decision procedure, to value and share all knowledge produced on the way and thereby to improve the efficiency of participation. Ultimately, based on the background of some CoOPLAGE authors, we aim at implementing Artificial Intelligence solutions, i.e. multi-agent based support for participation.In terms of public policy support and transitions, the ultimate goal is to value the large experience of the CoOPLAGE tools and case studies, to transfer it in a generic platformopen for all stakeholders, which would give them the capacity to design, pilot, participate, evaluate some integrated participatory processes. It should propose solutions beyond the existing large set of participation platforms with a focus on the global CoOPLAGE decision cycle (cf. part yyy). It should value the transversal role of participatory modeling (cf. part xxx), support the process of “participatory engineering of participation” (part zzz) and implement its following steps. As such, it is intended as a coherent “companion” to process managers and participants, which should strengthen the actual mobilization of participation in democratic decision making, and foster trust between citizens and institutions. These goals raise several research and design questions, mainly related to the integration of steps and tools in the procedure, and to the capitalization of knowledge.We discuss in a first part the target implementation context and a reference use scenario which shaped the design. In the second part of this chapter, we describe the structuralchoices, the architecture and finally we discuss ongoing evolutions. This chapter is structured as a design document and not as a scientific contribution, which will come later inthe experimental phases.
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