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MIT Professional Education

Understanding and Predicting Technological Innovation: New Data and Theory

Next dates

Jul 22—26
5 days
Cambridge, Massachusetts, USA
USD 4100
USD 820 per day


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.


  • 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


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.

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


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 ...
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