Data supporting A Multi-Agent System to Dynamically Devise an LCA Framework Weighting System Taking into Account Socio-technical and Environmental Consideration

The dataset supports a study that explores the need to adjust Life Cycle Assessment (LCA) weighting systems to incorporate socio-technical and environmental factors that are sensitive to external changes. The study demonstrates the potential of multi-agent systems (MAS) in dynamically adjusting environmental impact assessments by simulating various scenarios and addressing data limitations using generative adversarial networks (GANs) and MAS. The research emphasizes the importance of continuous system adaptation and positions MAS as a robust tool for managing uncertainties in environmental assessments.

The zip file contains two datasets: one for main agents and another for all agents, along with two Python scripts—one prioritizing the supervisor agent and the other prioritizing "friends" within the network. A Readme file is also included.

The data in both datasets is the same, but based on the logic of the code, they are used separately. Each dataset includes the following columns: agent number, age group number, education group number, gender, media impact, age increase, education increase, and environmental impact category weights. These weights reflect the environmental impact categories associated with each agent. The datasets track changes in these weights over time as agents are influenced by media, age, education increases, and interactions with a supervisor and six friends.