Finding Optimal and Practical Policy based on Participatory MASS with Optimization Method
Résumé
While MASS is a promising technology to address urban issues, it is not inherently user-friendly for stakeholders involved in urban matters, such as residents and local government officials. However, MASS has the potential to greatly enhance the design of policies for managing complex urban/social systems, as it takes a different approach from existing methods based on traditional paradigms. We have developed a prototype system to support policy-making by engaging diverse stakeholders. While it is crucial to respect the varied perspectives of stakeholders, each grounded in their unique value systems, during policy discussions and investigations, effectively articulating these ideas during deliberations often proves challenging. Our system aims to address this difficulty by integrating functions such as multi-agent simulations, data visualization, and a web-based interface. Furthermore, MASS, which allows for the execution of policies while modifying their parameters, can be utilized to explore optimal policy solutions. While the optimal (or near-optimal) solutions derived solely from MASS computations may not always translate into practical solutions for human stakeholders, they can serve as valuable guidelines for policy deliberations in stakeholder-participatory planning processes on our prototype system. To this end, we have developed and integrated into our system a computational function for optimal policy calculation that works in conjunction with MASS. We applied our system to the area surrounding a train station in Japan and conducted a social experiment with residents as participants. From the experiment, we obtained feedback that is helpful in developing our system into a practical policy-planning tool. Additionally, we confirmed that we can obtain (near-)optimal solutions through multiple executions of MASS.
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