The main objective of the project is to develop a new generation agent-based model (ABM) tool taking advantage of the latest progress in big data, AI algorithms and others, as a means to overcome challenges to improving policy design and socio-economic and environmental assessments.
As part of the project, the consortium will develop a European data-sources index tool and a farmer behavioural model using AI algorithms and based on participatory research. To develop an evolved agent-based model with improved capacity to model policies dealing with agriculture, the project team will elaborate on a dynamic quadratic model explicitly accounting for agent interactions and building on recent advancements in the capacities of mathematical solvers and ICT. The partners will integrate all modules within the AGRICORE suite into a simulation environment with both ex-ante (for policy design) and ex-post (for monitoring) analysis capacity. Such an integrated suite will allow its connection with biophysical models and a large set of databases including multiple data sources and geo-referenced datasets (interoperability).
The project partners will apply the AGRICORE suite to the ex-post (2014-2017) and ex-ante (2018-2020) policy assessment in three use cases selected to test the tool at various geographic scales and for different policy instruments (UC1 policy instrument relates to environmental impacts, UC2 relates to the delivery of ecosystem services and UC3 relates to the socioeconomic aspects of the integration of agriculture in rural society).
