Towards a Science of Socially Intelligent ICT ASSYST Project Workshop Imperial College London, 3 Aug. 2010. http://assystcomplexity.eu

1. Towards a Science of Socially Intelligent ICT
ASSYST Workshop, Imperial College London, 3 Aug. 2010. http://assystcomplexity.eu
Social Intelligence Systems
for Wicked Problems
Simon Buckingham Shum
Knowledge Media Institute
Open University UK
http://people.kmi.open.ac.uk/sbs
http://creativecommons.org/licenses/by-nc/2.0/uk 1

2. What is ICT-enabled ‘Social Intelligence’?
Working hypothesis:
In the context of wicked problems
(e.g. incomplete, ambiguous data, complex adaptive systems, diverse
perspectives, technical/social/political dimensions, time pressure…)
…Personal and Collective Cognition
break down in particular ways…
We need Theories, Tools and Practices
in order to create Social Intelligence Systems
for tackling such dilemmas
(and we need ways to teach these, both to our children, and the current workforce)
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3. Relevant theories should explain (ideally predict…) when
and why social intelligence fails or excels
Breakdown in personal and/or Relevant theory?
social intelligence
Risk of entrained thinking from experts who fail • Weick and Snowdon’s work on
to recognise a novel phenomenon organisational sensemaking in complexity
• Cognitive science theories of expertise
• Group deliberation research
Breakdown in critical reasoning • Informal logic and argumentation theory
Breakdown in ability to listen deeply to other • Theory-U (Scharmer)
stakeholders • Dialogue/Reconciliation (Isaacs; Kahane)
• Sensemaking for leadership in complex
challenges (Palus & Horth)
Learners cannot adapt fast enough or work • Learning Power in schools and workplace
effectively together to cope with the complexity (Deakin Crick, Claxton)
Inability to reliably predict based on past history • Complexity science
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4. Motivating requirements for a Social Intelligence
System (people + technology + practices)
Social Intelligence Phenomena Social Intelligence System?
Dangers of entrained thinking from experts who • Pay particular attention to exceptions
fail to recognise a novel phenomenon • Computer-supported argumentation
• Make the system open to diverse
perspectives ontologically, and in usability
Complex systems only seem to make sense • Stories and coherent pathways are
retrospectively: narrative is an appropriately important
complex form of knowledge sharing and • Reflection and overlaying of interpretation(s)
reflection for such domains is critical
Patterns are emergent • Generate gestalt views from the data
evidenced in the platform, not from
preconceptions
Much of the relevant knowledge is tacit, shared • Scaffold the formation of significant inter-
through discourse, not formal codifications personal, learning relationships
Many small signals can build over time into a • Enable individuals to highlight important
significant force/change events and connections  aggregate
• Recommend connections based on different
kinds of significant relationship
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Sources include: Weick (1995); Kurtz & Snowden (2003); Browning, L. and Boudès, T. (2005); Hagel et al (2010)

5. SI-System engineering principles?
One approach is to design for resilience
• “Resilience platforms”: When knowledge and understanding are key
variables in the system, resilience depends on the capacity for
learning: e.g. awareness of discrepant evidence, critical practice,
reflection and dialogue when confronted by challenges or shocks to
the system.
Resilience engineering principle Social Intelligence Infrastructure?
build in the potential for diversity • e.g. of worldviews, and the debates this sets
up
make tight feedback loops • e.g. rapid awareness of dis/agreement
amongst peers
promote building of trust/social capital • e.g. through social networking and mutual
support
enable experimentation • e.g. in order to learn through practical action
on the world, or simulations
use a decentralised, modular architecture • e.g. enabling innovation, interoperability and
mashups with diverse end-user tools/data
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6. Blog: www.open.ac.uk/sociallearn
Demo: http://sociallearn.org
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7. SocialLearn provides the ‘glue’ to connect learning
activities, ‘friends’, coaches, and recommendations
…other sites…
Site 1 Site 2 Site 3 Site 4
Interoperability via Google Gadgets
1. Profile
SocialLearn 2. User Interface
3. Social Graph
4. Services 7

8. SocialLearn “dashboard” of gadgets
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9. Embedding SocialLearn gadgets in a partner site
(the OU’s Cloudworks)
People
Recommender
gadget
Cloud
Recommender
gadget
Cloudstream
Recommender
gadget
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10. SocialLearn: accessing my Gadgets from the
browser toolbar while browsing any website
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11. a prototype infrastructure for
collective intelligence/social learning
web annotation/discourse for sensemaking
(A winner in the Mozilla/MacArthur Foundation
Jetpack for Learning Design Challenge)
http://cohere.open.ac.uk
De Liddo, A. and Buckingham Shum, S. (2010). Cohere: A Prototype for Contested Collective Intelligence. In:
ACM Computer Supported Cooperative Work (CSCW 2010) - Workshop: Collective Intelligence In Organizations
- Toward a Research Agenda, February 6-10, 2010, Savannah, Georgia, USA. http://oro.open.ac.uk/19554
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12. — web annotation for sensemaking
De Liddo, A. and Buckingham Shum, S. (2010). Cohere: A prototype for contested collective intelligence. In: ACM Computer Supported Cooperative Work 12
(CSCW 2010) - Workshop: Collective Intelligence In Organizations, February 6-10, 2010, Savannah, Georgia, USA. http://oro.open.ac.uk/19554

13. seeing the connections people make as
they annotate the web using Cohere
Visualizing all the connections that a set of
analysts have made between web resources
— but this may also be confusing
De Liddo, A. and Buckingham Shum, S. (2010). Cohere: A prototype for contested collective intelligence. In: ACM Computer Supported Cooperative Work
(CSCW 2010) - Workshop: Collective Intelligence In Organizations, February 6-10, 2010, Savannah, Georgia, USA. http://oro.open.ac.uk/19554

14. — semantic filter of argument map
Visualizing multiple
learners’ interpretations of
global warming sources
Connections have been
filtered by a set of
semantic relationships
grouped as Consistency
De Liddo, A. and Buckingham Shum, S. (2010). Cohere: A prototype for contested collective intelligence. In: ACM Computer Supported Cooperative Work
(CSCW 2010) - Workshop: Collective Intelligence In Organizations, February 6-10, 2010, Savannah, Georgia, USA. http://oro.open.ac.uk/19554

15. “Semantic Google Scholar”:
Query: What is the lineage of this idea?
Buckingham Shum, S.J., Uren, V., Li, G., Sereno, B. and Mancini, C.
(2007).Modelling Naturalistic Argumentation in Research Literatures:
Representation and Interaction Design Issues. International Journal of
Intelligent Systems, (Special Issue on Computational Models of Natural
Argument, Eds: C. Reed and F. Grasso, 22, (1), pp.17-47. http:// 15
oro.open.ac.uk/6463

16. — geospatial mashup of ideas
Nodes in the semantic
network containing
geolocation data can be
visualized in Google Maps

17. — timeline viz. mashup of ideas
Nodes in the semantic
network containing temporal
data can be visualized in MIT
Simile’s timeline

18. In more detail…
articles, books, news, movies, software, community…
http://cohere.open.ac.uk
www.open.ac.uk/sociallearn
http://projects.kmi.open.ac.uk/hyperdiscourse
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