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Collaborative awareness platforms for
   sustainability and social innovation


Geoff Mulgan
Chief Executive
National Endowment for Science,
Technology and the Arts
Elements of collective intelligence:


• Consciousness - and consciousness of being a self or
  agent
• Observation – the ability to see, hear, smell the world
• Attention – the ability to focus
• Cognition – the abilities to think and reason
• Reflexivity – the ability to observe own thought
  processes
• Creation - the ability to imagine and design
• Memory – the ability to remember
• Judgement – the ability to judge
• Wisdom – the ability to make sense of complexity and to
                             2
More need for
                 energy, time     Wisdom




                            Judgement

                         Knowledge

                    Information
Automation,
routinisation,
                                 Data
3
   The premise: evolving Internet enables different
    aggregations, orchestrations, mobilisation of awareness
    and intelligence, at multiple levels:

   Collective awareness
   Collective judgement
   Collective action/behaviour change
The Internet of Thinking and the Internet of Things
   Practitioner networks: SIX, labour intensive, face to face
    and virtual, grounded in community of practice, shared
    ethos, value in orchestrating practical knowledge
   Intelligence networks: eg Intellipedia, market knowledge,
    exclusive, mission oriented, value in quality of
    assessments or pattern recognition
www.planetaryskin.org
   Resource mobilisation networks: eg AirBnB, Taskrabbit,
    simplest in technical terms, value in breadth and scale of
    resources exchanged
   Funding networks: eg Kickstarter, Kiva, Zopa, value in
    scale of money raised
   Media networks: eg Wikileaks, Ohmynews, Audioboo,
    news value in breadth, relevance and quality of information
   Campaign platforms: eg Avaaz, Change.org, value in
    mobilisation and impact on power
   Behaviour change platforms: eg Weightwatchers, 10/10
   Problem-solving platforms: eg Kaggle, Innocentive, value
    is ability to generate useful solutions
Commentary platforms, PeertoPatent, value is quality of
expert commentary and adaptation
Idea platforms, like quirky, value is ability to develop ideas
into action
   Data platforms: eg AMEE, value is precision and quantity
    of data for use
Mobilising support communities, eg Tyze, key
value is quality of support
Public decision support platforms: eg who owns my
neighbourhood, value is linking public wishes to public
decisions
Emerging platforms: platforms to support platforms, value is
usability, standardisation
Emerging platforms: knowledge synthesis platforms, eg Alliance
for Useful Evidence, PatientslikeMe
Emerging platforms: mobilise evidence as well as
  peer knowledge and link to practice, eg AfH
Typology of current platforms:

   Practitioner networks: eg SIX
   Specialist need networks: eg Patientslikeme in healthcare
   Intelligence networks: eg Intellipedia
   Funding networks: eg Kickstarter
   Resource mobilisation networks: eg Taskrabbit
   Media networks: eg Wikileaks
   Campaign platforms: eg Avaaz
   Behaviour change platforms: eg Weightwatchers, 10/10
   Commentary platforms: eg Peertopatent
   Problem-solving platforms: eg Kaggle, Innocentive
   Data platforms: eg AMEE
   Mobilising support communities: eg Tyze
   Decision support platforms: eg Whoownsmyneighbourhood
How much can platforms do?

   To change behaviour – less carbon, less obesity, better
    parenting, less waste – influences likely to include:

       Prohibitions
       Incentives
       Peer influence
       Information
       Environment shaping
   What doesn’t work – internet based platforms alone;
    wisdom of crowds; awareness leading to behaviour.
    Online tools significant but partial role (cf Egypt,
    Libya, Syria, Bahrain), usually in combination with
    others.

   The key questions: what role do they play; what
    interaction with offline; what can they add and what
    can’t they do.

   How to move up the hierarchy from data to
    judgement
38

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Collective Awareness: Geoff Mulgan

  • 1. Collaborative awareness platforms for sustainability and social innovation Geoff Mulgan Chief Executive National Endowment for Science, Technology and the Arts
  • 2. Elements of collective intelligence: • Consciousness - and consciousness of being a self or agent • Observation – the ability to see, hear, smell the world • Attention – the ability to focus • Cognition – the abilities to think and reason • Reflexivity – the ability to observe own thought processes • Creation - the ability to imagine and design • Memory – the ability to remember • Judgement – the ability to judge • Wisdom – the ability to make sense of complexity and to 2
  • 3. More need for energy, time Wisdom Judgement Knowledge Information Automation, routinisation, Data 3
  • 4. The premise: evolving Internet enables different aggregations, orchestrations, mobilisation of awareness and intelligence, at multiple levels:  Collective awareness  Collective judgement  Collective action/behaviour change
  • 5. The Internet of Thinking and the Internet of Things
  • 6. Practitioner networks: SIX, labour intensive, face to face and virtual, grounded in community of practice, shared ethos, value in orchestrating practical knowledge
  • 7. Intelligence networks: eg Intellipedia, market knowledge, exclusive, mission oriented, value in quality of assessments or pattern recognition
  • 8.
  • 10.
  • 11. Resource mobilisation networks: eg AirBnB, Taskrabbit, simplest in technical terms, value in breadth and scale of resources exchanged
  • 12.
  • 13.
  • 14. Funding networks: eg Kickstarter, Kiva, Zopa, value in scale of money raised
  • 15.
  • 16. Media networks: eg Wikileaks, Ohmynews, Audioboo, news value in breadth, relevance and quality of information
  • 17.
  • 18. Campaign platforms: eg Avaaz, Change.org, value in mobilisation and impact on power
  • 19.
  • 20. Behaviour change platforms: eg Weightwatchers, 10/10
  • 21.
  • 22. Problem-solving platforms: eg Kaggle, Innocentive, value is ability to generate useful solutions
  • 23.
  • 24.
  • 25. Commentary platforms, PeertoPatent, value is quality of expert commentary and adaptation
  • 26. Idea platforms, like quirky, value is ability to develop ideas into action
  • 27.
  • 28. Data platforms: eg AMEE, value is precision and quantity of data for use
  • 29.
  • 30. Mobilising support communities, eg Tyze, key value is quality of support
  • 31. Public decision support platforms: eg who owns my neighbourhood, value is linking public wishes to public decisions
  • 32. Emerging platforms: platforms to support platforms, value is usability, standardisation
  • 33. Emerging platforms: knowledge synthesis platforms, eg Alliance for Useful Evidence, PatientslikeMe
  • 34. Emerging platforms: mobilise evidence as well as peer knowledge and link to practice, eg AfH
  • 35. Typology of current platforms:  Practitioner networks: eg SIX  Specialist need networks: eg Patientslikeme in healthcare  Intelligence networks: eg Intellipedia  Funding networks: eg Kickstarter  Resource mobilisation networks: eg Taskrabbit  Media networks: eg Wikileaks  Campaign platforms: eg Avaaz  Behaviour change platforms: eg Weightwatchers, 10/10  Commentary platforms: eg Peertopatent  Problem-solving platforms: eg Kaggle, Innocentive  Data platforms: eg AMEE  Mobilising support communities: eg Tyze  Decision support platforms: eg Whoownsmyneighbourhood
  • 36. How much can platforms do?  To change behaviour – less carbon, less obesity, better parenting, less waste – influences likely to include:  Prohibitions  Incentives  Peer influence  Information  Environment shaping
  • 37. What doesn’t work – internet based platforms alone; wisdom of crowds; awareness leading to behaviour. Online tools significant but partial role (cf Egypt, Libya, Syria, Bahrain), usually in combination with others.  The key questions: what role do they play; what interaction with offline; what can they add and what can’t they do.  How to move up the hierarchy from data to judgement
  • 38. 38