Social Intelligence Systems for Wicked Problems
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Social Intelligence Systems for Wicked Problems

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Towards a Science of Socially Intelligent ICT

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

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Social Intelligence Systems for Wicked Problems Social Intelligence Systems for Wicked Problems Presentation Transcript

  • 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
  • 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) 2
  • 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 3
  • 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 4 Sources include: Weick (1995); Kurtz & Snowden (2003); Browning, L. and Boudès, T. (2005); Hagel et al (2010)
  • 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 5
  • Blog: www.open.ac.uk/sociallearn Demo: http://sociallearn.org 6
  • 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
  • SocialLearn “dashboard” of gadgets 8
  • Embedding SocialLearn gadgets in a partner site (the OU’s Cloudworks) People Recommender gadget Cloud Recommender gadget Cloudstream Recommender gadget 9
  • SocialLearn: accessing my Gadgets from the browser toolbar while browsing any website 10
  • 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 11
  • — 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
  • 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
  • — 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
  • “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
  • — geospatial mashup of ideas Nodes in the semantic network containing geolocation data can be visualized in Google Maps
  • — timeline viz. mashup of ideas Nodes in the semantic network containing temporal data can be visualized in MIT Simile’s timeline
  • 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 18