Using a systems-based Evolutionary Learning Laboratory to address the “ NEET - Not in Employment, Education, or Training” issue in Japan

Abstract

The purpose of social design is to create a feasible solution in order to solve a particular problem. For some time, social designs have been made by social entrepreneurs using unauthorized methodologies formed through lessons from their own activities and experiences. They regard these methodologies as practical, but they are often difficult to design, especially in complex social systems where multiple stakeholders are involved. Participatory systems analysis (PSA) is another valuable methodology in social design, as it provides various stakeholders with the opportunity to share their mental models with each other, to recognize and understand issues, and to identify potential barriers and drivers towards creating solutions. This method is effective in developing a consensus for finding the best solutions.

The purpose of this study was to create a model to design consensual solutions for an important social issue in Japan involving the high number of NEETS, a term referring to people who are “Not in Employment, Education, or Training.” The mental models of various stakeholders were integrated into a systems structure or causal loop model to develop an understanding of the interrelationships and patterns among the components of the model. The model was used to identify the main leverage points and systemic interventions that could help in solving the NEET problem. Bayesian Belief Network (BBN) modeling was then used for each of the identified leverage points to design an integrated systemic management and operational plan for addressing the NEET issue, which has not yet been successfully addressed in Japan. The systems models (CLM and BBN) were embedded in the Evolutionary Learning Laboratory to create a cyclical social design through which the solutions can be implemented.