This course on technological innovation will be organized around three modules on (1) Data, (2) Theory, and (3) Application. In the first module, we will analyze new, large data sets on technological improvement, many of which were collected by the instructor and are the most expansive of their kind. We will cover statistical analysis methods and decomposition models in order to extract useful insight on the determinants of technological innovation. Examples from energy conversion, transportation, chemicals, metals, information technology, and a range of other industries will be discussed.
In the second module, we will cover theories, that have been developed in recent years and stretching back several decades, to explain technological innovation. We will cover the disciplinary origins of these theories, the empirical evidence for or against them, and the usefulness of these theories for practitioners from various fields including engineering, chemicals, private investment, and public policy.
Building on this insight, in the third module we will focus on applying the data analysis methods and theories covered to inform decisions about technology investment and design. The third module will address questions of specific interest to the class. This module will demonstrate the utility of the material covered and how it can be extended to answer a wide range of important questions relating to investment, research and development, manufacturing, and public policy.
It is highly recommended that you apply for a course at least 6-8 weeks before the start date to guarantee there will be space available. After that date you may be placed on a waitlist. Courses with low enrollment may be cancelled up to 4 weeks before start date if sufficient enrollments are not met. If you are able to access the online application form, then registration for that particular course is still open.
- Developing understanding of how large data sets at various levels of detail can be used to gain insight on the dynamics of technological innovation
- Learning how to compare the rate of progress of various technologies and products
- Understanding the state of the art in theories of technological innovation, and their utility for particular questions faced in private industry and the public sector
- Learning how to apply data analysis and theory to guide investment and design decisions
- Gaining insight on technological innovation-related decisions faced in designing financial portfolios, research and development portfolios, and public policy
Who Should Attend:
This course is designed for people working in industries such as chemicals, life sciences, manufacturing, investment, energy, and public policy makers.
Typical job roles will include:
- Research and development managers
- Production/manufacturing operations managers
- Executive level management in a variety of technology related firms
- Public policy makers working in technology-related areas
- Private investors interested in technology-related portfolio optimization
Laptops with a recent version of Excel are required for this course. Participants should have administrator privileges to install programs, as standard Excel packages will be installed and used. Tablets will not be sufficient for the computing activities in this course.
Monday (Module 1: Data)
- Morning: Lecture on evidence of technology innovation. What does the data suggest?
- Afternoon: Guided exercise on analyzing technology improvement trends. Participants will work in groups and report back on their assessment of the rates of innovation across various industries.
Tuesday (Module 2: Theory)
- Morning: Lecture on proposed models of technological innovation. How do we explain the observed evidence?
- Afternoon: Guided exercise on comparing the predictive ability of proposed models. Participants will fit the data with proposed models and test the performance of the models. We will identify and debate the best-performing models across various industries.
Wednesday (Module 2: Theory)
- Morning: Lecture on proposed theory relating the rate of technological innovation to design features of technologies. Which technologies improve fastest and why?
- Afternoon: Lecture followed by group exercise and discussion on design and investment decisions based on features of a technology’s design. Working in small groups, participants will consider the component dependencies and flexibility of various technologies and industries.
Thursday (Module 3: Application)
- Morning: Lecture on applying insights from data and theory to decision making in private firms and government. How can we optimize technology design decisions and investment portfolios?
- Afternoon: Participants will optimize technology portfolios in a context of interest: engineering design, private investment, or public investment.
Friday (Module 3: Application)
- Morning: Participants will report back on Thursday afternoon’s work on design or portfolio optimization.
- Afternoon: We will have an extended working lunch that will include further discussion and a free-form lecture by the professor on applications of specific interest to the class.
Class runs 9:00 am - 5:00 pm each day except Friday, when it ends at 2:00 pm.
The schedule will include a lunch break and a morning and afternoon coffee break each day.
ASSOCIATE DIRECTOR, STANDARD CHARTERED BANK
"The overall experience was extremely enriching. I think the program was run and organized very well. The course content and teaching pedagogy was very good and the fact that it was over 5 days actually helped me grasp this kind of topic much better since it needs time and lots of hands on work. Overall I would rate my experience quite high at MIT this summer."
MANAGING CONSULTANT, MAXMETRICS GMBH
"Good structure, mixture of lectures and exercises, right class size, knowledgeable professors."
TECHNICAL WARRANT HOLDER, NAVAL SEA SYSTEMS COMMAND
"Dr. Trancik and her team did a great job of blending lecture materials, in class projects, night time reading assignments."
Jessika Trancik is the Atlantic Richfield Career Development Associate Professor of Energy Studies at the MIT Institute for Data, Systems and Society (IDSS). Professor Trancik's research centers on modeling technology innovation and emphasizes the development of new datasets and theory. Her work focuses on evaluating the dynamic costs, environmental impacts, other aspects of technology performance, and setting design targets to help accelerate the development of these technologies in the laboratory. This work involves assembling and analyzing expansive datasets and developing new quantitative models and theory. Many projects focus on electricity and transportation, with an emphasis on solar energy conversion and storage technologies.
Trancik was an Omidyar fellow at the Santa Fe Institute and a fellow at Columbia University’s Earth Institute. She earned a B.S. in materials science and engineering from Cornell University (1997) and a Ph.D. in materials science from Oxford University (2002), where she studied as a Rhodes Scholar. She has also worked for the United Nations and as an advisor to the private sector on investment in low-carbon energy technologies. She has published in journals such as the Proceedings of the National Academy of Sciences, Nano Letters, Environmental Science and Technology, and Environmental Research Letters.
This course takes place on the MIT campus in Cambridge, Massachusetts. We can also offer this course for groups of employees at your location. Please complete the Custom Programs request form for further details.
|Fundamentals: Core concepts, understandings, and tools (30%)||30|
|Latest Developments: Recent advances and future trends (40%)||40|
|Industry Applications: Linking theory and real-world (30%)||30|
|Lecture: Delivery of material in a lecture format (70%)||70|
|Labs: Demonstrations, experiments, simulations (15%)||15|
|Discussion or Groupwork: Participatory learning (15%)||15|
|Introductory: Appropriate for a general audience (50%)||50|
|Specialized: Assumes experience in practice area or field (30%)||30|
|Advanced: In-depth explorations at the graduate level (20%)||20|