1. The Role of Experimentation in Individual and Team Innovation and Discovery Dan Frey Department of Mechanical Engineering Engineering Systems Division

2. Thomas Edison’s Approach Used “hunt and try” extensively Assembled a system for innovation –people, equipment, information Repeatedly placed devices in more complex environments to progressively approximate their final use conditions Hughes, Thomas P, 1977, “Edison's method,” in Technology at the Turning Point, edited by W. B. Pickett. San Francisco Press Inc., 5-22. Perhaps a scientific understanding of innovation requires studying the 99% perspiration as well as the 1% inspiration.

3. Pharmaceutical R&D A large and growing sector Use many approaches to innovation –Mass screening –Combinatorial chemistry –Bioinformatics –Rational drug design Thomke, S., E. von Hippel, and R. Franke, 1998, “Modes of Experimentation…” Research Policy, 27: 315-332.

4. Computer Aided Engineering Increasingly important means to enable rapid design experimentation Also extremely valuable for –Visualization –Communication –Project management But also a major source of risk –~10 serious faults per 1000 lines of commercially available code –Only 1 or 2 significant figures repeatable in independent implementations of the same code on the same input data Hatton, Les, 1997, “The T Experiments: Errors in Scientific Software”, IEEE Computational Science and Engineering.

5. Design of Experiments A discipline concerned with planning of experiments and analysis of the resulting data Fractional factorial design improves efficiency of search, especially if experimental error is high A “crossed array” employed in “robust design”

6. Experiments and Learning “Because results are usually known quickly, … the natural way to experiment is to use information from each group of runs to plan the next …” “…Statistical training … has resulted in undue emphasis on ‘one-shot’ statistical procedures…” Box, GEP, 1999, “Statistics as a Catalyst to Learning by the Scientific Method Part II – A Discussion,” Journal of Quality Technology, 31(1)16-29. Induction Deduction Induction Deduction Data Theories, Conjectures, Models

7. Adaptive Robust Design

8. Styles of Experimentation Iterative, adaptive, creative, free-form exploration Methods strongly driven by past knowledge, modeling, prediction, etc. (e.g., multi-disciplinary optimization) Highly-structured empirical methods (e.g., Design of Experiments) heuristics & hybrids

9. Some Opportunities for Discussion Can experimentation and ideation methods be integrated? Can experimentation be connected to research on memory? Are there social conditions that promote more productive experimental behaviors?