Modeling the Impact of Policy Interventions for Sustainable Development
Modeling the Impact of Policy Interventions for Sustainable Development
Sowmith Nandan Rachuri, Arpitha Malavalli, Niharika Sri Parasa, Pooja Bassin, Srinath Srinivasa
Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence
Demo Track. Pages 7167-7170.
https://doi.org/10.24963/ijcai.2023/841
There is an increasing demand to design policy interventions to achieve various targets specified by the UN Sustainable Development Goals by 2030. Designing interventions is a complex task given that the system may often respond in unexpected ways to a given intervention. This could be due to interventions towards a given target, affecting other unrelated variables, and/or interventions leading to acute disparities in nearby geographic areas. In order to address such issues, we propose a novel concept called Stress Modeling that analyzes the holistic impact of a policy intervention by taking into account the interactions within a system, after the intervention. The simulation is based on the postulate that complex systems of interacting entities tend to settle down into "low energy'' configurations by minimizing differentials in capabilities of neighbouring entities. The simulation shows how policy impact percolates through geospatial boundaries over time and can be applied at any granularity. The theory and the corresponding package have been explained along with a case study analyzing a fertilizer policy in the Agro-climatic Zones of the state of Karnataka, India.
Keywords:
Multidisciplinary Topics and Applications: MDA: Computational sustainability
AI Ethics, Trust, Fairness: ETF: AI and law, governance, regulation
Machine Learning: ML: Explainable/Interpretable machine learning
Multidisciplinary Topics and Applications: MDA: Energy, environment and sustainability
Uncertainty in AI: UAI: Bayesian networks