behavior. As reality attests, that is demonstrably not true. In fact, if we change the information in organizations, we oftentimes do not change people’s behavior, because people frequently ignore or dismiss the information given them.

Consider an informal gedanken experiment: I asked people to choose between a tool that offers an order of magnitude—10×!—improvement in managing all the information that goes across the desktop, phone, personal data assistant, cell phone, and Web, or a tool that offers a 20 percent—0.2×—improve- ment in the ability to persuade one’s bosses, colleagues, and subordinates. The overwhelming majority of people consistently chose the persuasive tool. So what is the real issue most people face in their organizations? Information or persuasion? The role and rhetoric of models and simulations for persua- sion in organizations have very different design emphases and sensibilities compared to the models for informing organizations.

So the common reaction is to question how to develop models, prototypes, and simulations that make a person or his group more persuasive within the organization. That is a legitimate design question. Using myself as a beta site of one, I realize I don’t like being persuaded by others. However, I consider myself open minded enough that I am happy to persuade myself. The design challenge? What is a better investment? A tool that helps a person become more persuasive or a tool that helps that person’s customer, internal or external, persuade themselves.

I submit that building models that enable people to persuade themselves is a different design sensibility challenge from the designing of a model, prototype, or simulation that makes a person or group more persuasive to others. Those kinds of design parameters become more important rather than less important when we talk about accelerating the pace of innovation within an organization, industry, or market segment.

Why are these kinds of design questions more important now than even 5 years ago? We are rich. We have computational wealth and power that transform both the economics and rhetoric of modeling. However, I fear that we are using the wrong unit of analysis for the assessment and measurement of computation-driven influence. People talk about Information Technology and the Information Age, but I think that modeling, simulation, and prototyping of innovation are, frankly, not an information management problem.

Instead of “Bits” management being the issue here, the real source of wealth is “Its”—Iterations. Innovation wealth is a function of a shift from Bits to Its. Instead of managing information, how do we better manage iteration? The new wealth is our ability to iterate and perform more iterations per unit time. What do we do with that as individuals? What do we do with that as teams? How do we use the opportunity to manage more iterations per unit time as a vehicle to reduce coordination costs and transactions costs? How do we create these technologies as a vehicle to facilitate communication of innovation and management of innovation within organizations? The answers to these questions determine how well—and how poorly—organizations will iterate to innovate.

Please make a conceptual leap with me. We know what financial capital is; we understand and appreciate human capital. We hear more and more about social capital. I would like you to think of the explosion of computation-driven iteration to be a form of Capital. Iterative Capital.

So we need to ask ourselves what is our ROI—not Return on Investment but Return on Iteration. What do we want to accomplish as innovators? What kinds of attributes are we iterating around? What is it that we are really trying to learn as we iterate to innovate? Are we interested in the development of a particular structure or material or that structure or material in a certain kind of a context? How do we manage the return on iterations? Ultimately, the more choices you have, the more your values matter.

Front Matter (R1-R12)

Summary (1-6)

1. Overview of Trends in Innovation in the Chemical Industry (7-17)

2. Techniques for Structured Innovation (18-27)

3. The Chemistry Innovation Process: Breakthroughs for Electronics and Photonics (28-36)

4. DARPA's Approach to Innovation and Its Reflection in Industry (37-48)

5. Comments on the Advanced Technology Program (49-55)

6. What Have We Learned from Hot Topics? (56-63)

7. Industrial Innovation with External R&D Programs (64-73)

8. Some New Ideas for Speeding Up the Development of Products from University Research (74-82)

9. From Molecules to Materials to Market: A Rational Framework for Products Formulation and Design (83-89)

10. The Tacit Economics of Modeling: Indifference Curves that Should Defy Indifference (90-98)

11. Successful Innovation Starting in an Academic Environment (99-105)

12. Panel Discussion (106-116)

Appendix A: Workshop Participants (117-120)

Appendix B: Biographical Sketches of Workshop Speakers (121-127)

Appendix C: Origin of and Information on the Chemical Sciences Roundtable (128-130)