Enabling Innovation: Technology- and System-Level Approaches that Capitalize on Complexity

Abstract

we finish our training most of us believe that it is our job to conceptualize designs, develop products and worry little about what happens after they have been introduced. Our courses are generally too practical to bother with theories about how innovation occurs, who it affects and how we might better manage the process. Diesel, inventor of the diesel engine, distinguished between two phases in technological progress: the conception and carrying out of the idea, which is a happy period of creative mental work in which technical challenges are overcome, and the introduction of the innovation, which is a “struggle against stupidity and envy, apathy and evil, secret opposition and open conflict of interests, a horrible period of struggle with man, a martyrdom even if success ensues.” Diesel is perhaps overstating the difficulties of managing innovation, but nevertheless as engineers we are still taught to prefer technical “invention” and leave dealing with people and the “innovation” side to others. However, engineers ignore the innovation process at their peril. Enabling innovation means building on peoples’ ingenuity and motivations, rather than working against them. In this paper I describe the learning selection approach to enabling innovation that capitalizes on the complexity of social systems at different scales of analysis. In the first part of the paper I describe the approach and how it can be used to guide the early stages of setting up a “grassroots” innovation process. In the second part of the paper I look at how the learn selection model can be used “top-down” to guide research investments to trigger large-scale systemic change.