how design thinking works, or: design thinking unpacked: an evolutionary algorithm?
DESCRIPTION
A presentation accompanying a paper* presented at EAD 2009 conference in Aberdeen, Scotland. We're trying to develop a theory why "design thinking" works in practice, and what may be its limits. The idea is that "design thinking" has similarities to a general class of algorithms known as evolutionary algorithms, and some comparisons can be made.* Korhonen, J. M. & Hassi, L. (2009). Design Thinking Unpacked: An Evolutionary Algorithm. In Proceedings of the Eight European Academy of Design International Conference, 261-265. Aberdeen, UK.TRANSCRIPT
Why “design thinking” works?
Or “Design Thinking Unpacked: An Evolutionary Algorithm”J. M. Korhonen & L. Hassi
in this presentation- why design thinking works- when does it work- what does it mean in practice
“Design thinking”-like approach in practice is defined here as:
- multidisciplinary teams- human-centred exploration- fast and iterative prototyping
(process perspective; Jahnke 2009)
“what designers do”
Design is defined here as aknowledge-generating activity
Product development =
Product development =search for best possible designs
Imagine an (almost) infinite library of all designs...
(cf. “The Library of Babel” by Jorge Luis Borges)
Trinity College, Dublin
If we visualize what’s in the library:(mobile phones section)
Differences in design
Differences in design
Differences in design
Differences in design
Differences in design
Utility (fitness for purpose)
Differences in design
EXAMPLE CASE: Janne’s choice, 2004
Utility (fitness for purpose)
Differences in design
EXAMPLE CASE: Janne’s choice, 2004
Utility (fitness for purpose)
X
Differences in design
Utility (fitness for purpose)
X
Differences in design
Utility (fitness for purpose)
Differences in design
Utility (fitness for purpose)
FITNESS LANDSCAPE
Differences in design
Utility (fitness for purpose)
PERFECTLY ORDERED (NON-RANDOM)
Problem type: Defined, quantitative
Differences in design
Utility (fitness for purpose)
ROUGH-CORRELATED (REAL LIFE)
Problem type: Wicked, qualitative
What does rough-correlated fitness landscape mean in practice?
Usually, small changes have small effects on fitness for purpose...
Mirra Chair (c) Herman Miller
But sometimes, small changes can have large effects on fitness...
Mirra Chair (c) Herman Miller
Mirra Chair (c) Herman Miller
?? ?
Mirra Chair (c) Herman Miller
[x] Metric[x] Imperial
On the other hand, some large changes may have only small effects on the fitness for purpose...
Mirra Chair (c) Herman Miller, Office Chair (c) vcf.com
Differences in design
Utility (fitness for purpose)
ROUGH-CORRELATED (REAL LIFE)
How to reach the highest possible peaks?
The optimum strategy for getting to the top in rough-correlated landscapes:
evolutionary algorithms
Informal definition:Algorithm is a process that performs some sequence of operations
Differences in design
Utility (fitness for purpose)
EVOLUTIONARY ALGORITHM
X
Differences in design
Utility (fitness for purpose)
EVOLUTIONARY ALGORITHM
X
XX
X
XX
Differences in design
Utility (fitness for purpose)
EVOLUTIONARY ALGORITHM
X
XX
X
XX
evolutionary algorithm =- radical experimentation- incremental improvement - test, eliminate, retain
evolutionary algorithm =- diversity of ideas- iterative prototyping- rapid real-life testing
evolutionary algorithm...?- multidisciplinary teams- human-centred exploration- fast and iterative prototyping ≈ evolutionary algorithm...?
Evolutionary algorithm
Radical experimen-tation (lots of ideas)
Incremental improvement
Test, eliminate, retain
“Design thinking”
Multidisciplinary teams
Human-centred exploration
Fast and iterative prototyping
Some implications:- Explaining “design thinking”- When to use design thinking(- NPD process modeling)(- Technology S-curves)
Provisional theoretical explanation: why design thinking works
Provisional theoretical explanation: why design thinking works (and where it works best)
In short, design thinking-like approaches may be theoretically near-optimum strategies when the fitness landscape is rough-correlated
In short, design thinking-like approaches may be theoretically near-optimum strategies when the fitness landscape is rough-correlated(that is, in most cases)
Could we estimate the proper exploratory/exploitative (inductive/deductive) mix in actual projects?
Could we estimate the proper exploratory/exploitative (inductive/deductive) mix in actual projects?Could this affect resource planning?
When to use design thinking
Differences in design
Utility (fitness for purpose) Problem type: Defined, quantitative
NOT GOODPERFECTLY ORDERED (NON-RANDOM)
Differences in design
Utility (fitness for purpose)
ROUGH-CORRELATED (REAL LIFE)
Problem type: Wicked, qualitative
GOOD
HOWEVER, when “zooming in”by defining the problem better,
qualitative can become quantitative
Problem type: Defined, quantitative
Well-defined problems are best solved through formal, analytical approaches
...of course, getting to “well-defined” is the trick: engineers are really good at finding answers, but how to ask the questions?
