STIA-368-01 Innovation, Development, and Complexity
Spring for 2017-2018
Science, technology, and innovation (STI) are widely acknowledged as critical enablers of the United Nations Sustainable Development Goals. Through detailed case studies and simulation modeling exercises, students will develop a greater facility in understanding the potential benefits and unintended consequences of scientific and technical policy interventions in sustainable development.
The first half of the course will discuss the role of science and technology in promoting global development through a series of case studies and illustrative examples. It will examine the potential for STI to address SDG goals and targets (e.g., poverty, food security, health, gender equality, and climate change) while drawing on lessons learned from the Industrial Revolution, Green Revolution, and the current Digital Revolution. The challenges of applying innovation in development will also be explored, including direct and indirect consequences, politics of technology access, and the resistance to new technologies.
The second half of the course will introduce complex systems thinking as a framework for development policy and system dynamics as a tool for policy modeling and simulation. Building on case studies introduced in the first half of the course, students will create systems models with the Integrated model for Sustainable Development Goals strategies (iSDG) simulation framework. These models will be used for STI policy design, scenario exploration, and for understanding the interconnectedness of policies designed to achieve the SDGs and test their likely impacts before adopting them.
Students will complete a final project where they (1) conduct a policy analysis exercise involving modeling to assess the potential impacts of an STI policy intervention in a developing country; (2) and develop policy recommendations for key government stakeholders. The course will end introducing alternative simulation frameworks for policy analysis as well as recent applications of machine learning and artificial intelligence in policy design and evaluation.
Prerequisites: No prior computer modeling experience is required.
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