Presentation at the JAIST World Conference 2018 in Kanazawa, Japan. Includes a brief bio, early work on the Experimental Design Canvas to assist in the design of creative briefs, and multi-agent simulations to reason about innovation teams. ~45 minutes + Q&A

1. Managing for Creativity
Design briefs + Multi-agent simulations to reason about innovation teams
Ricardo Sosa
Associate Professor, Design and Creative Technologies AUT, New Zealand
and Monash Art, Design & Architecture, Monash University, Australia
ricardo.sosa@monash.edu

2. JWC Organizing Committee
Conference Chair Scientific Advisors
Tetsuo Asano Tetsuo Asano
Yoji Koda
Program Chair Teruo Matsuzawa
Minoru Terano Hiroshi Mizuta
Yukari Nagai Tatsuya Shimoda
Yasuo Tan
Program Committee Yuzuru Takamura
Razvan Beuran Toshiaki Taniike
Tatsuo Kaneko Minoru Terano
Eunyoung Kim Satoshi Tojo
Kazunori Miyata Toshifumi Tsukahara
Kazushi Nishimoto
Yasuo Sasaki Poster Chair
Atsuo Yoshitaka Kei Sakamura
Takaya Yuizono Kentaro Takashima
Jader Zelaya
Publicity and Web Chair
Masahiro Ura
Jader Zelaya
Plannning Committee
Yasuo Sasaki
Kei Sakamura
Kentaro Takashima
(and S. Taniguchi) Masahiro Ura

4. Twitter: @designcomputing

6. Zacoalpan, 1995
http://huaracheblog.wordpress.com
http://www.flickr.com/photos/mexicantextile

7. Participatory Design Conference, 2014

8. ‘ROBOT GRACIOUS GAIT’ RoboMods
Advances in Mechanical Engineering, 7(3)

9. Design Studies, 42(1). doi:10.1016/j.destud.2015.10.002

10. Singapore University of Technology and Design, 2012

11. Monash Art, Design & Architecture, 2017

12. Manurewa “Robot for Mayor 2030” project, 2017

13. - How do we ask for creative ideas?
- How do we organise for change?

14. ‘Reverse engineering” a winning design: EcoHelmet
A foldable, low-cost, made in a honeycomb cardboard structure
to encourage the safe casual use of bike sharing services
Possible design briefs:
1. “Design something that solves a problem”
2. “Encourage casual use of bike sharing services”
3.“Design a bicycle helmet”
4.“Encourage the safe casual use of bike sharing services”
“Design briefs”

15. - What information to present?
- How to frame a problem?
- How much information to present?
- What outcomes do we ask for?
Design Briefs in Creativity Studies

16. To help identify terms that are more suitable for the brief
based on the decisions made across the canvas model:
“To design a helmet [83,100] that…”
“To design a head protection [7,080] device that…”
“To design a head gear [11,800] that…”
“To design a gear [8,850,000] that…”
Abstract terms such as “gear” may be more suitable for divergence
(than more concrete and specific terms)
Polysemy index

17. Experimental Design canvas
Time Method Participants Metrics
Research Question
(or Learning Objectives, NPD Goals)
Outputs Baseline
concepts
Ideation mode and technique(s)
Experience and skillsDesign brief
• Polysemy of terms used
• Innovation activity
• Communication indices

18. Our goal: To provide an explicit and demonstrable way
to synthesise design briefs to help compare outcomes
between studies
Yet, expert judgement and iterative testing are essential
Experimental Design canvas

19. 1955

20. 1995

21. 2012

22. a) Support new initiatives across the entire organisation
b) Form specialized innovation teams
Strategies for organisational creativity

23. - These strategies may lead to different types of
outcomes
- Compelling arguments for both scenarios
- However, their effects are not sufficiently understood
and there is no clear guidance
Strategies for organisational creativity

24. Our work:
- Social simulations for the systematic inquiry of
change agency principles in business organisations
- Agent-based models used here as a lens to examine
key ideas about organisational creativity

25. Rob Axelrod’s (1997) agent-based models:
“a new way of looking at the dynamic process of social influence”

26. features (f)
traits (t)
f1: {t1, t2... tn}
f2: {…}
fn

28. Original Axelrod model

29. Axelrod model with mobility behaviour (teams)

30. Axelrod model with dissenting behaviour (innovation teams)

31. Axelrod model with mobility and dissenting behaviours

32. Axelrod model with mobility and dissenting behaviours

33. Axelrod model with mobility and dissenting behaviours

34. Axelrod model with mobility and dissenting behaviours

35. Axelrod model with mobility and dissenting behaviours

36. DS = 1/N
DD = 1/f
DS = N
DD = 1/f
DS = N
DD = f
Scope of dissent DS
Degree of dissent DD

37. “Creative efficiency”: average Δg divided
by number of dissenting agents

38. When new ideas are more incremental,
it pays to have more agents introduce
ideas
When new ideas are more radical, it
pays to have fewer agents introduce
ideas

39. Whilst increasing the number of
dissenting agents above ~10% produces
a greater number of group changes, this
is achieved at a cost of decreasing
creative efficiency

40. Prichard and Stanton (1999) empirical
study found that a team of just creative
people performed less effectively than a
more balanced team

41. Extreme value theory: a complementary perspective on the
behaviour of these models (Girotra et al., 2013 p. 3)

42. With high levels of degree of dissent (DD = 6), innovation
cases rapidly increase then slow down. With low dissent
(DD = 1), innovation cases remain extremely infrequent

43. Reining and Briggs (2008) identified “a curve with a positive but decreasing
slope in ideation functions [showing that] idea-quantity may not be a useful
surrogate for idea-quality in certain circumstances” (p. 419)

44. Discussion:
- Diminishing returns in creative participation (defies
ideation principles)
- The assumed benefits of open creative participation are
partial and could backfire when implemented in
unsophisticated ways
- Groups with fewer change agents can outperform groups
with more change agents

45. Our work:
- Not predictive, but meant “to show the consequences of a
few simple assumptions” about organisational creativity
- This is useful when “intuition is not a very good guide for
predicting what even a simple dynamic model will produce”
Future work:
- Extensions to the simulation model
- Replication and validation of results
(Axelrod, 1997)

46. Twitter: @designcomputing