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Towards a Quantitative Model of Design Thinking: Implications for Silicon Valley

Quantitative Design Thinking: A New Foundational Science for Engineering
Ade Mabogunje
Center for Design Research
Stanford University
(Talk given at  PARC, 2/19/15)
Design thinking has classically attempted to seek coherence in understanding but not in rationality. In particular, design thinking has focused on context dependence, that is the impact of local culture on processes and innovations. At the macro-level, we are interested in what (if anything) makes Silicon Valley unique; in particular, why is Silicon Valley posited at the center of the knowledge revolution?
What is design thinking?
The notion of design as a “way of thinking” in the sciences can be traced to Herbert A. Simon‘s 1969 book The Sciences of the Artificial,[2] and in design engineering to Robert McKim’s 1973 book Experiences in Visual Thinking.[3] Rolf Faste expanded on McKim’s work at Stanford in the 80’s and 90’s,[4][5] teaching “design thinking” as a method of creative action.”[6] Peter Rowe’s 1987 book Design Thinking, which described methods and approaches used by architects and urban planners, was a significant early usage of the term in the design research literature.[7] “Design Thinking” was adapted for business purposes by Faste’s Stanford colleague David M. Kelley, who founded IDEO in 1991.[8]

Design thinking as a tool is key to fostering innovation and hence economic growth. However design thinking can also ameliorate poverty, disability, natural disaster and disease.

Can design thinking be improved?
Bu understanding what  designers do we can start to consider how  we improve design. In the past steps have been taken to improve design thinking by augmenting human intellect (Engelbart bootstrap methodology), or by Automation through AI (Herbert Simon). Studies of designers as learners, including situated learning & cognition, situated plans of action and communities of practice, has shown design thinking to be a form of empathetic thinking that is difficult to describe, quantitate and account. By comparison with engineering, performance improvement is hard to measure and no standards exist. However early quantitative studies on the benefits of design to performance and the development of standards have shown (e.g.) that intensive transmission of information at early stages between design and manufacturing increases performance and reduces confusion. Also, using feedback diagrams that are intended to be both reflective and reflexive has aided in measuring the effects of design thinking processes.  By looking at  communication, disposition and institution variables. Bandura (1986) developed the triadic model of reciprocal determinism as part of social cognition theory.
More recently, Mabugonje has extended this model by combining conceptual gestures, drawings and text to create a notation for design thinking. The impetus for this development was the observation that novel innovations generate their own unique noun phrases that then become common vocabulary (“swipe left” for example.) The importance of questions by engineers: Deep Reasoning Questions (DRQ) (Gresser) vs Generative Design Questions (GDF) (Eris). It was found that a 50-50 split in DRQs and GDQs leads to the most innovation.
Malte Jung (2011) used Gottman’s research on married couples as the basis for measuring performance in engineering teams. He showed positive hedonic balance (wow teams) significantly out-innovate negative hedonic balance. Mabugonje has created a cognitive notation (a notation for ideation) that can describe and quantitate the process of innovation amongst engineering teams using a notational language. It can be thought of as a visual representation to characterize moment-to-moment concept generation in design and engineering teams. Mabugonje defined several primitives for his design notation such as:
– move
– block
-deflect
– overcome
– diversion
etc.
What are the economic & scientific implications of analyzing  innovations in this way?
Design, the process of artifact creation and diffusion, is becoming a qualitative science.
Thus, design is ready to become a foundational science for engineering innovation. However, an innovation culture must be supported by strong legal systems that enforce property rights and the legal structure of property ( see e.g. The Mystery of Capital, Hernando De Soto.) In particular, Silicon Valley succeeds as it follows the “Rainforest” model of innovation (The Rainforest: The Secret to Building the Next Silicon Valley, Victor Hwang and Greg Horowitt) The culture in this model includes the importance of vision; the importance of dealmaker networks; and understanding that most engineers are introverts, while most traditional bankers are “farmers”; while VCs are more like “hunters”. This is key to balancing the cognitive capabilities of young entrepreneurs with experienced VCs. Aging VCs work well with young entrepreneurs as together they can combine world knowledge  (VCs) with processing capacity (entrepreneurs).  In general, Silicon Valley follows the laws of the Rainforest, while Europe and other developed economies follow the law of the Plantation.  For example, the USA follows common-law procedures while Europe follows civil-law procedures. Even within the USA, there are two economic models at play. SV follows an integrative model of behavioral prediction for innovation.
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