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.

1. Why “design
thinking” works?
Or
“Design Thinking Unpacked: An
Evolutionary Algorithm”
J. M. Korhonen & L. Hassi

2. in this presentation
- why design thinking works
- when does it work
- what does it mean in practice

3. “Design thinking”-like approach
in practice is defined here as:
- multidisciplinary teams
- human-centred exploration
- fast and iterative prototyping
(process perspective; Jahnke 2009)

4. “what designers do”

5. Design is defined here as a
knowledge-generating activity

6. Product development =

7. Product development =
search for best possible designs

8. Imagine an (almost) infinite
library of all designs...
(cf. “The Library of Babel” by Jorge
Luis Borges)

9. Trinity College, Dublin

10. If we visualize what’s in the
library:
(mobile phones section)

11. Differences in design

12. Differences in design

13. Differences in design

14. Differences in design

15. Differences in design
Utility (fitness for purpose)

16. EXAMPLE CASE: Janne’s choice, 2004
Utility (fitness for purpose)
Differences in design

17. EXAMPLE CASE: Janne’s choice, 2004
Utility (fitness for purpose)
X
Differences in design

18. X
Differences in design
Utility (fitness for purpose)

19. Differences in design
Utility (fitness for purpose)

20. FITNESS LANDSCAPE
Utility (fitness for purpose)
Differences in design

21. PERFECTLY ORDERED (NON-RANDOM)
Utility (fitness for purpose)
Problem type: Defined, quantitative
Differences in design

22. ROUGH-CORRELATED (REAL LIFE)
Utility (fitness for purpose)
Problem type: Wicked, qualitative
Differences in design

23. What does rough-correlated
fitness landscape mean in
practice?

24. Usually, small changes have
small effects on fitness for
purpose...

25. Mirra Chair (c) Herman Miller

26. But sometimes, small changes
can have large effects on
fitness...

27. Mirra Chair (c) Herman Miller

28. Mirra Chair (c) Herman Miller

29. ?? ?
Mirra Chair (c) Herman Miller

30. [x] Metric
[x] Imperial

31. On the other hand, some large
changes may have only small
effects on the fitness for
purpose...

32. Mirra Chair (c) Herman Miller, Office Chair (c) vcf.com

33. ROUGH-CORRELATED (REAL LIFE)
Utility (fitness for purpose)
Differences in design

34. How to reach the highest
possible peaks?

35. The optimum strategy for getting
to the top in rough-correlated
landscapes:
evolutionary algorithms

36. Informal definition:
Algorithm is a process that
performs some sequence of
operations

37. EVOLUTIONARY ALGORITHM
Utility (fitness for purpose)
X
Differences in design

38. EVOLUTIONARY ALGORITHM
Utility (fitness for purpose)
X X
X
X
X
X
Differences in design

39. EVOLUTIONARY ALGORITHM
Utility (fitness for purpose)
X X
X
X
X
X
Differences in design

40. evolutionary algorithm =
- radical experimentation
- incremental improvement
- test, eliminate, retain

41. evolutionary algorithm =
- diversity of ideas
- iterative prototyping
- rapid real-life testing

42. evolutionary algorithm...?
- multidisciplinary teams
- human-centred exploration
- fast and iterative prototyping
≈ evolutionary algorithm...?

43. Evolutionary algorithm “Design thinking”
Radical experimen-
Multidisciplinary teams
tation (lots of ideas)
Incremental Human-centred
improvement exploration
Fast and iterative
Test, eliminate, retain
prototyping

44. Some implications:
- Explaining “design thinking”
- When to use design thinking
(- NPD process modeling)
(- Technology S-curves)

45. Provisional theoretical
explanation: why design
thinking works

46. Provisional theoretical
explanation: why design
thinking works
(and where it works best)

47. In short, design thinking-like
approaches may be
theoretically near-optimum
strategies when the fitness
landscape is rough-correlated

48. In short, design thinking-like
approaches may be
theoretically near-optimum
strategies when the fitness
landscape is rough-correlated
(that is, in most cases)

49. Could we estimate the proper
exploratory/exploitative
(inductive/deductive) mix in
actual projects?

50. Could we estimate the proper
exploratory/exploitative
(inductive/deductive) mix in
actual projects?
Could this affect resource
planning?

51. When to use design thinking

52. PERFECTLY ORDERED (NON-RANDOM)
Utility (fitness for purpose)
NOT GOOD
Problem type: Defined, quantitative
Differences in design

53. ROUGH-CORRELATED (REAL LIFE)
Utility (fitness for purpose)
GOOD
Problem type: Wicked, qualitative
Differences in design

54. HOWEVER, when “zooming in”
by defining the problem better,
qualitative can become quantitative

55. Problem type: Defined, quantitative

56. Well-defined problems are best
solved through formal,
analytical approaches

57. ...of course, getting to “well-
defined” is the trick: engineers
are really good at finding
answers, but how to ask the
questions?

58. FI(n)NISH
janne.korhonen@seos.fi
lotta.hassi@tkk.fi
www.seos.fi
www.aaltodesignfactory.fi