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Sosa: Managing for creativity JAIST 2018


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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

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Sosa: Managing for creativity JAIST 2018

  1. 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
  2. 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
  3. 3. Twitter: @designcomputing
  4. 4. Zacoalpan, 1995
  5. 5. Participatory Design Conference, 2014
  6. 6. ‘ROBOT GRACIOUS GAIT’ RoboMods Advances in Mechanical Engineering, 7(3)
  7. 7. Design Studies, 42(1). doi:10.1016/j.destud.2015.10.002
  8. 8. Singapore University of Technology and Design, 2012
  9. 9. Monash Art, Design & Architecture, 2017
  10. 10. Manurewa “Robot for Mayor 2030” project, 2017
  11. 11. - How do we ask for creative ideas? - How do we organise for change?
  12. 12. ‘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”
  13. 13. - 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
  14. 14. 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
  15. 15. 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
  16. 16. 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
  17. 17. 1955
  18. 18. 1995
  19. 19. 2012
  20. 20. a) Support new initiatives across the entire organisation b) Form specialized innovation teams Strategies for organisational creativity
  21. 21. - 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
  22. 22. 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
  23. 23. Rob Axelrod’s (1997) agent-based models: “a new way of looking at the dynamic process of social influence”
  24. 24. features (f) traits (t) f1: {t1, t2... tn} f2: {…} fn
  25. 25. Original Axelrod model
  26. 26. Axelrod model with mobility behaviour (teams)
  27. 27. Axelrod model with dissenting behaviour (innovation teams)
  28. 28. Axelrod model with mobility and dissenting behaviours
  29. 29. Axelrod model with mobility and dissenting behaviours
  30. 30. Axelrod model with mobility and dissenting behaviours
  31. 31. Axelrod model with mobility and dissenting behaviours
  32. 32. Axelrod model with mobility and dissenting behaviours
  33. 33. DS = 1/N DD = 1/f DS = N DD = 1/f DS = N DD = f Scope of dissent DS Degree of dissent DD
  34. 34. “Creative efficiency”: average Δg divided by number of dissenting agents
  35. 35. 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
  36. 36. 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
  37. 37. Prichard and Stanton (1999) empirical study found that a team of just creative people performed less effectively than a more balanced team
  38. 38. Extreme value theory: a complementary perspective on the behaviour of these models (Girotra et al., 2013 p. 3)
  39. 39. 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
  40. 40. 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)
  41. 41. 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
  42. 42. 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)
  43. 43. Twitter: @designcomputing