Participatory Modelling in Agricultural Living Labs: Linking Society and Practice to Foster Biodiversity

Living Labs in agricultural systems serve as real-world platforms where farmers, researchers, policymakers, and communities co-create solutions to address complex sustainability and biodiversity challenges. Bridging the gap between scientific knowledge and local practice remains a key challenge for advancing sustainable agroecosystems. Recent studies highlight the potential of participatory modelling, such as agent-based models, to support collaborative decision-making, with broader implications for environmental management and governance. Participatory modelling facilitates structured interactions between scientific teams and stakeholders to address trade-offs, synergies, hidden causalities, and systemic solutions.

Despite the growing application of living lab approaches, a systematic understanding of how participatory modelling can support stakeholder engagement and decision-making within these processes remains underexplored.

This study reviews participatory modelling approaches in the context of agricultural Living Labs, highlighting their potential, challenges, and opportunities for supporting collaborative decision-making. Agent-based models are discussed as a prominent example of such approaches, illustrating how computational simulations can capture stakeholder interactions, emergent patterns, feedback, and system-level outcomes. For this purpose, peer-reviewed studies from major databases, including Web of Science and Scopus, were screened to identify key participatory modelling approaches, stakeholder engagement strategies, and reported outcomes.

Findings indicate that participatory modelling and agent-based models contribute to living lab processes by exploring complex system interactions and trade-offs; supporting biodiversity-centric scenarios analysis for sustainable agricultural management; fostering co-learning and shared understanding among stakeholders; and enabling biodiversity-oriented decision-making at multiple spatial scales. Despite challenges such as data availability, stakeholder capacity, and model complexity, participatory modelling shows substantial potential as a facilitation and transformation tool in living labs. Furthermore, integrating agent-based model outputs with artificial intelligence–driven analyses enables multi-scale assessment, identification of biodiversity hotspots, and optimization of management strategies.

This overview lays the foundation for future empirical studies and demonstrates how participatory agent-based models, complemented by AI, can bridge science, practice, and policy toward more sustainable and resilient agricultural systems.