How Design Thinking works, or: Design Thinking Unpacked: an evolutionary algorithm?

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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.

Published in: Design, Technology, Business

How Design Thinking works, or: Design Thinking Unpacked: an evolutionary algorithm?

  1. 1. Why “design thinking” works? Or “Design Thinking Unpacked: An Evolutionary Algorithm” J. M. Korhonen & L. Hassi
  2. 2. in this presentation - why design thinking works - when does it work - what does it mean in practice
  3. 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. 4. “what designers do”
  5. 5. Design is defined here as a knowledge-generating activity
  6. 6. Product development =
  7. 7. Product development = search for best possible designs
  8. 8. Imagine an (almost) infinite library of all designs... (cf. “The Library of Babel” by Jorge Luis Borges)
  9. 9. Trinity College, Dublin
  10. 10. If we visualize what’s in the library: (mobile phones section)
  11. 11. Differences in design
  12. 12. Differences in design
  13. 13. Differences in design
  14. 14. Differences in design
  15. 15. Differences in design Utility (fitness for purpose)
  16. 16. EXAMPLE CASE: Janne’s choice, 2004 Utility (fitness for purpose) Differences in design
  17. 17. EXAMPLE CASE: Janne’s choice, 2004 Utility (fitness for purpose) X Differences in design
  18. 18. X Differences in design Utility (fitness for purpose)
  19. 19. Differences in design Utility (fitness for purpose)
  20. 20. FITNESS LANDSCAPE Utility (fitness for purpose) Differences in design
  21. 21. PERFECTLY ORDERED (NON-RANDOM) Utility (fitness for purpose) Problem type: Defined, quantitative Differences in design
  22. 22. ROUGH-CORRELATED (REAL LIFE) Utility (fitness for purpose) Problem type: Wicked, qualitative Differences in design
  23. 23. What does rough-correlated fitness landscape mean in practice?
  24. 24. Usually, small changes have small effects on fitness for purpose...
  25. 25. Mirra Chair (c) Herman Miller
  26. 26. But sometimes, small changes can have large effects on fitness...
  27. 27. Mirra Chair (c) Herman Miller
  28. 28. Mirra Chair (c) Herman Miller
  29. 29. ?? ? Mirra Chair (c) Herman Miller
  30. 30. [x] Metric [x] Imperial
  31. 31. On the other hand, some large changes may have only small effects on the fitness for purpose...
  32. 32. Mirra Chair (c) Herman Miller, Office Chair (c) vcf.com
  33. 33. ROUGH-CORRELATED (REAL LIFE) Utility (fitness for purpose) Differences in design
  34. 34. How to reach the highest possible peaks?
  35. 35. The optimum strategy for getting to the top in rough-correlated landscapes: evolutionary algorithms
  36. 36. Informal definition: Algorithm is a process that performs some sequence of operations
  37. 37. EVOLUTIONARY ALGORITHM Utility (fitness for purpose) X Differences in design
  38. 38. EVOLUTIONARY ALGORITHM Utility (fitness for purpose) X X X X X X Differences in design
  39. 39. EVOLUTIONARY ALGORITHM Utility (fitness for purpose) X X X X X X Differences in design
  40. 40. evolutionary algorithm = - radical experimentation - incremental improvement - test, eliminate, retain
  41. 41. evolutionary algorithm = - diversity of ideas - iterative prototyping - rapid real-life testing
  42. 42. evolutionary algorithm...? - multidisciplinary teams - human-centred exploration - fast and iterative prototyping ≈ evolutionary algorithm...?
  43. 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. 44. Some implications: - Explaining “design thinking” - When to use design thinking (- NPD process modeling) (- Technology S-curves)
  45. 45. Provisional theoretical explanation: why design thinking works
  46. 46. Provisional theoretical explanation: why design thinking works (and where it works best)
  47. 47. In short, design thinking-like approaches may be theoretically near-optimum strategies when the fitness landscape is rough-correlated
  48. 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. 49. Could we estimate the proper exploratory/exploitative (inductive/deductive) mix in actual projects?
  50. 50. Could we estimate the proper exploratory/exploitative (inductive/deductive) mix in actual projects? Could this affect resource planning?
  51. 51. When to use design thinking
  52. 52. PERFECTLY ORDERED (NON-RANDOM) Utility (fitness for purpose) NOT GOOD Problem type: Defined, quantitative Differences in design
  53. 53. ROUGH-CORRELATED (REAL LIFE) Utility (fitness for purpose) GOOD Problem type: Wicked, qualitative Differences in design
  54. 54. HOWEVER, when “zooming in” by defining the problem better, qualitative can become quantitative
  55. 55. Problem type: Defined, quantitative
  56. 56. Well-defined problems are best solved through formal, analytical approaches
  57. 57. ...of course, getting to “well- defined” is the trick: engineers are really good at finding answers, but how to ask the questions?
  58. 58. FI(n)NISH janne.korhonen@seos.fi lotta.hassi@tkk.fi www.seos.fi www.aaltodesignfactory.fi

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