Course Meeting Times
Lectures: 2 sessions / week, 1.5 hours / session
Faculty and Staff
Course Director: Dr. Erika Wagner
Associated Faculty and Staff:
Prof. Charlie Cooney
Prof. Fiona Murray
Prof. Dava Newman
Dr. Jeffrey Shames
Dr. Ken Zolot
In 2004, the Ansari X PRIZE for suborbital spaceflight captured the public’s imagination and revolutionized an industry, leveraging a $10M prize purse into over $100M in innovation. Building from that success, the X PRIZE Foundation is now developing new prizes to focus innovation around “Grand Challenge” themes, including genomics, energy, healthcare, and education.
This course will examine the intersection of incentives and innovation, drawing on economic models, historic examples, and recent experience of the X PRIZE Foundation to help develop a future prize in Energy Storage Technologies.
After taking ESD.172, students should be able to:
- Articulate the features of incentive prizes and contexts in which they are most effective
- Describe incentive prizes in the context of existing economic models of innovation
- Analyze effective areas for investment of limited resources
- Understand the diversity of opportunities for improvements in energy storage
- Understand the technological and systems-level blockages to improvements in energy storage
- Develop deep knowledge of a particular issue in energy storage innovation through the term project experience
- Work effectively in teams
- Present their concepts clearly and concisely in both written and oral form
This 9-unit course will be highly action-oriented and interactive, with a team-based term project due at the end of the semester. Twice-weekly workshops include a mixture of lectures and discussions on innovation, incentive structures, and opportunities for revolution in energy innovation.
Groups of 3-5 students will work together to analyze an area of relevance to high-efficiency energy production, distribution, storage, or use, with the ultimate goal of developing a fully-formed X PRIZE concept to incentivize revolutionary breakthroughs in this area.
Course instructors will work together with the teams to identify local experts as team mentors/advisors. It is anticipated that team members will meet with their advisors a minimum of two times during the course of the semester.
Class grades will be assigned on the basis of homework, project performance, and class participation, as follows: