A computational framework for modeling socio-technical agents in the life-cycle sustainability assessment of supply networks

Supply networks are now more complex and intertwined than ever, and it is necessary to have the capacity of proposing effective policies that consider aspects of complexity and human behavior. To address this complexity, practitioners commonly rely on Agent-Based Modeling (ABM) as modeling paradigm since it permits to analyze unpredictable system properties that arise from the interaction of agents. Nevertheless, current ABM implementations in life-cycle sustainability studies lack of modularity, meaning that practitioners repeat efforts when modeling socio-technical agents. To deal with this issue, a common framework for modeling production and consumption entities is necessary, for which we propose an algebraic framework for representing computational agents. Our framework provides notions and instructions to set an operational configuration that describes socio-technological agents in a systematic manner to then be used in an ABM model. The framework is rooted on principles of Life Cycle Assessment, and it is a bottom-up generalization of the Stochastic Technology-of-Choice Model. The framework's components are conceived to depict the technical and non-technical relationships of an agent's decision space, while also being suited to handle the logic of different decision problems, such as the sourcing problem. To demonstrate the capabilities of our framework, we provide a proof of concept in which we study the effects of introducing agents with sustainable behaviors (Agents of Change) in a system dominated by profit-driven agents. We evaluated two criteria to introduce Agents of Change: strategic and random. The results indicate that a strategic introduction of Agents of Change can be beneficial from a financial and environmental perspective. We show that for a particular strategy, Agents of Change contribute to the reduction of the financial risk of other Agents of Change, while also decreasing the impact of the system. Finally, this proof of concept shows that our framework can be used to address fundamental sustainability inquiries while still being compatible with current life-oriented methods.