Guest post by Jim Manzi, founder and Chairman of Applied Predictive Technologies, and the author of Uncontrolled: The Surprising Payoff of Trial-and-Error for Business, Politics and Society.
I often criticize social scientists for making overly-aggressive claims for understanding causality in complex systems by building regression and other pattern-finding models. This is not evidence of some unique weakness of social scientists The same thing happens in business all the time, but business analyses tend not to be published for obvious reasons. A good example of one that has been published is in the current Harvard Business Review. This matters a lot, because HBR holds a unique position as the most important serious business publication in America.
Anne Marie Knott, professor of strategy at Washington University's Olin Business School, has written an article called "The Trillion-Dollar R&D Fix." The article proposes a new measurement of R&D effectiveness: RQ. In her words, RQ is a measure of "how effective your company is at R&D."
What is so striking to me about this article is how unvarnished Knott is in claiming that she has discovered a tool to do exactly what I say is so hard: make useful, reliable and non-obvious predictions for the effect of interventions in social systems. She writes that "Using standard regression analysis, the calculation tells us in a very precise way how productive each of the inputs is in generating output. It tells us, for instance, how much a 1% increase in R&D spending would increase a firm's revenue." Knott asserts that RQ allows the management if a company "to see how changes in your R&D expenditure affect the bottom line and, most important, your company's market value." She even names names: providing a table of what she thinks each of the top 20 public corporations in America should have spent on R&D, and how much more each would be worth if they followed her recommendations.