A multi-agent system to dynamically devise an LCA framework weighting system taking into account socio-technical and environmental consideration

This research article explores the need to adapt the weighting system of a life cycle assessment (LCA) framework to a wide range of socio-technical and environmental considerations, which are complex and sensitive to external stressors. The study demonstrates the potential of agent-based systems to adapt a weighting system to dynamic conditions, and suggests integrating an agent-based model to handle uncertainties of weighting in environmental impact assessment. Generative adversarial networks (GANs) and multi-agent systems (MAS) are utilized to address data limitations and simulate diverse scenarios. We confirm the potential of agent-based systems to analyse the effective management of uncertainties and customization of weighting systems in environmental impact assessments to improve decision making. Furthermore, the study emphasizes the importance of continuous adaptation and recalibration to ensure the system remains relevant in dynamic environments. The results confirm that MAS is a powerful tool for managing uncertainty, customizing weighting systems and improving decision making in environmental assessments. Moreover, the study acknowledges challenges and sets the groundwork for future research.