A WORKSHOP FOR UTS STUDENTS Simon Buckingham Shum (CIC Director) & Kailash Awati (Senior Lecturer in Data Science) This half-day workshop will provide you with hands-on experience mapping issues, ideas and arguments using the research-validated Compendium visual hypertext tool for mapping wicked problems. No technical expertise required. Compendium QuickStart Guide: http://simon.buckinghamshum.net/wp-content/uploads/2017/12/Compendium_QuickStart.pdf

1. Simon Buckingham Shum1 & Kailash Awati2
1 Connected Intelligence Centre & 2 Faculty of Transdisciplinary Innovation
University of Technology Sydney
@sbuckshum • @kailashawati • http://utscic.edu.au
Visualisation for Wicked Problems
Summer@UTS session, 15th Feb. 2018
Facultyof

2. “wicked problems”
2

3. Characteristics of “Wicked Problems”
(opposite: “tame problems” with validated methods for identifying optimal, agreed solution)
are difficult to clearly define – conflicting worldviews.
have many interdependencies and are often multi-causal.
attempts to address often lead to unforeseen consequences.
are rarely stable.
there may be no agreement on what counts as a solution.
are socially complex – cultural/values conflicts.
have no stopping rule – go until resources run out.
are characterised by chronic policy failure.

4. Tackling “Wicked Problems” requires…
Quality dialogue: stakeholders need to commit to listen and
learn from each other
Readiness to change behaviour.
Multiple logics/rationalities: technical, economic, user
experience, values, historical, emotional…
Good representations: ways to visualise different
perspectives, and ‘the system’ in ways that open up the
conversation

5. The mapping approach we’ll learn
today can be used with groups,
but you’ll start with it as a
personal thinking tool
5

6. kids can do this!…
6

7. My 11 year old…
7

8. My 11 year old…
8

9. My 11 year old…
9

10. My 11 year old…
10

11. My 11 year old…
11

12. My 11 year old…
12

13. My 11 year old…
13

14. My 11 year old…
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15. My 11 year old…
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…and now gives his speech using this map as his notes

16. The Issue-Based
Information System (IBIS)
“the DNA of conversations…”
16

17. Issue Mapping: Questions, Ideas, Decisions, Pros + Cons

18. real world examples…
18

19. “We need to build a road through your community…”
Also used for:
• Corporate strategy and org redesign
(private and public sector)
• Procurement strategy for $500M+ civil
infrastructure projects
• Project inceptions and lessons learnt
Copyright SevenSigma 2011
Stirling Alliance: Long Term Transport Plan (Perth, AUS)
http://www.sevensigma.com.au/what-we-have-done/case-studies.html

20. Hostage recovery scenario: how to apply political pressure?
The collective intelligence available in
the room and online: Dialogue Map
capturing the team’s deliberations
Visual background structures
the display for planning
http://www.aiai.ed.ac.uk/project/co-opr

21. 21
NASA Mobile Agents Field Trials:
Simulating an Earth/Mars geology science team
William Clancey, et al. (2005) "Automating CapCom Using Mobile Agents and Robotic Assistants",
1st Space Exploration Conference, Orlando FL: Continuing the Voyage of Discovery: https://doi.org/10.2514/6.2005-2659

22. Organising ideas and media for academic work
22
http://simon.buckinghamshum.net/2011/03/mapping-phd-research-in-compendium/
This and other academic research examples: http://cloudworks.ac.uk/cloud/view/5106

23. Organising ideas and media for academic work
23
http://projects.kmi.open.ac.uk/e-dance/2009/09/14/choreographic-video-annotation/
This and other academic research examples: http://cloudworks.ac.uk/cloud/view/5106

24. A bit more about IBIS mapping
24

25. Issue Mapping: Questions, Ideas, Decisions, Pros + Cons

26. IBIS Syntax

27. Mapping Dialogues Using IBIS

28. Mapping Dialogue Using IBIS

29. Mapping Dialogue Using IBIS

30. Mapping Dialogue Using IBIS

31. Mapping Dialogue Using IBIS

32. Mapping Dialogue Using IBIS

33. Mapping Dialogue Using IBIS

34. Mapping Dialogue Using IBIS

35. Mapping Dialogue Using IBIS

36. Mapping Dialogue Using IBIS

37. Mapping Dialogue Using IBIS

38. Example: a business meeting
Scenario: An IT team is discussing how they
should go about implementing a new system
for which the budget has been approved.

39. Example: a business meeting

40. Example: a business meeting

41. Example: a business meeting

42. Example: a business meeting

43. Example: a business meeting

51. Hands-on exercises:
mapping a conversation
as an IBIS network
51

53. Where shall we go out on Saturday night?
53
Nice simple question?...
What are our options?...
What are the tradeoffs?...

54. A more complex example: Budget planning
54
Task
The general manager of a local council in the US has called a meeting with his direct reports to discuss
next year’s budget. The following section is an edited excerpt of the discussion. Create an issue map (IBIS
map) of the discussion, taking care to identify questions, ideas and arguments carefully.
Dialogue
General Manager: I’ve called this meeting to discuss what we should do to about our budget.
Chief of Operations: I think we have ample scope to increase revenue.
Divisional Head: Hmm…I’m not sure about that. How can we increase revenue?
CFO: Well, we could petition local authorities for an increase in county tax.
Chief of Operations: Yes, and another possibility is to start charging fees for our special programs.
CFO: Another option to deal with the budget issue is to look into cutting costs.
Divisional Head: How? I think cutting costs is not a good idea, we already went through cost cutting last
year.
CFO: May be so, but we can still eliminate some special programs.
Divisional Head: Which ones?
CFO: Another possibility is to renegotiate salaries.
HR Director: Oh no, that takes way too long and it is unlikely to work.
CFO: OK, another possibility is to cut staff….

55. Budget mapping exercise - a solution
55

56. “The Offer” – map this…
A recruiter calls and offers you a dream job. It pays 20% more than your current role,
with more responsibility and the opportunity to gain experience in a completely new
area. The additional responsibilities include: managing a bigger team and overseeing
more functions than you do in your current role. There are also better career
prospects as the company is much larger than your current one.
On the other hand, you are in your comfort zone in your current role and the working
conditions are excellent. Moreover the company is stable (compared to the new one).
But you feel you are in a dead end job and, perhaps most important, you are getting
terminally bored.
56

57. Getting tougher: A fictional meeting between you (a data scientist)
with a telecoms client
Imagine you recorded this meeting, and now you want to summarise this as clearly as possible as an issue map
which you will send your client as a record of the key issues, the options considered, the decisions made, and why.
Client: Could you run some analytics on customer comments to see if there’s anything interesting?
You: Well there are many approaches we could take: what are you looking for?
Client: Basically, can we predict if they’re about to switch from us?
You: There’s research evidence that they follow their friends and family in switching phone provider. As for comments, the evidence
seems to be that most tweet this, though some will complain to you first. Sentiment mining is a possibility. Twitter gives you social
networks too.
Client: That social stuff is really interesting, and I know Belstra are testing this. But won’t customers find it creepy that we analyse their
tweets?
You: Possibly, and remember that twitter feed is always filtered. OK, well it’s safer to analyse your own databases. Is it just phone or
are you interested in other services too?... And do you have data on any social ties between customers?
Client: Internet and TV are also relevant but let’s start with phone. The customer DB knows about families. OK let’s just mine our
CRM data for telltale comments to start with, and see if that tallies with family members following each other out the door.
You: OK, we can merge datasets and test a predictive model of each independently, and combined. 57

58. Example map from this exercise: how does yours vary?
58

59. Example map from this exercise: how does yours vary?
59

60. Hands-on Compendium:
Visual hypermedia
for IBIS mapping
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61. Hands-on with the Compendium visual hypermedia tool
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Just a taster – full details: http://compendiuminstitute.net/training/training.htm
Start a new project
Create a map
Add nodes
Add links
Hyperlink to media docs
Create and add tags
Embed a node in 2 places
Publish to the web
+

62. 62
All books in the UTS Library. Plus these blogs, papers, demos…
Jeff Conklin: http://cognexus.org
Al Selvin: http://bit.ly/alselvin
Paul Culmsee: http://www.cleverworkarounds.com/category/dialogue-mapping
Simon Buckingham Shum: http://simon.buckinghamshum.net/tag/compendium
Kailash Awati: https://eight2late.wordpress.com/category/issue-based-information-system
Tim van Gelder: https://www.rationaleonline.com
To go deeper…

63. extra slides on
argument analysis
63

64. 64
Example: analysing a scientific argument on
National Front extremist website
What are the facts? … on every measure of intellectual ability and
educational attainment Blacks perform significantly worse,
on average, than Whites. In the case of average IQ, for
example, the average Negro figure is only 85% of the White
average.
Readers can consult Race by Dr. John R. Baker, former Reader in Cytology at Oxford University, published by the Oxford University Press, or
The Testing of Negro Intelligence, an exhaustive review of hundreds of studies demonstrating racial differences in intellectual ability by Dr.
Audrey M. Shuey, and of course there is The Bell Curve by Herrnstein and Murray.

65. Analysing the NF negro intelligence case
using argument mapping
Red link=
challenges
Green link=
supports
Hyperlink to evidence on
a website
http://bit.ly/aP4M0P (View in Safari)

70. Refuting the NF negro intelligence argument
using argument mapping
http://bit.ly/aP4M0P (View in Safari)

71. precision analysis of a pro or con
71

72. Precision analysis of Pros and Cons
Pros and Cons are relatively informal summaries of arguments, captured quickly
in the way that we normally speak, e.g.

73. Precision analysis of Pros and Cons
Pros and Cons are relatively informal summaries of arguments, captured quickly
in the way that we normally speak, e.g.
Philosophy of Argumentation would recognise this as an Argument from Analogy,
which has a particular template. We can use this to probe the Con in detail…

74. Template for an
“Argument from
Analogy”
CLAIM
SUPPORTING
PREMISE
CRITICAL
QUESTIONS
SUPPORTING
PREMISE
https://www.reasoninglab.com/patterns-of-argument/argumentation-schemes/waltons-argumentation-schemes

75. Template for an
“Argument from
Analogy”
Instantiating the
“Argument from
Analogy” template

76. See also… Rationale: an Argument Mapping tool
https://www.rationaleonline.com

77. See also… Rationale: an Argument Mapping tool
https://www.rationaleonline.com

78. See also… Rationale: an Argument Mapping tool
https://www.rationaleonline.com

79. See also… Rationale: an Argument Mapping tool
https://www.rationaleonline.com

80. See also… Rationale: an Argument Mapping tool
https://www.rationaleonline.com