Knowledge From Crowds - Better with Institutions + Algorithms

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Crowds can support learning and knowledge creation. A framework using institutions and algorithms can help assure good outcomes - Wikipedia, Edx.org and Giffgaff are used to explain the framework. …

Crowds can support learning and knowledge creation. A framework using institutions and algorithms can help assure good outcomes - Wikipedia, Edx.org and Giffgaff are used to explain the framework.
Presentation for KM 2012 in Sao Paulo, Brazil.

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  • Investment and partnerships with 12 companies
  • + first contest – nobody made it + second – most finished + winner is the basis for…
  • Google is actually the greatest recycling company…
  • Investment and partnerships with 12 companies
  • Add colaboratorie to logo – priority 2
  • + what exactly are we talking about? + most organizations depend on some combination of these resources + the most interesting thing is that e are changing how we get access to these resource + lower cost devices + broadband + marketplaces + analytics + reputation ---  dramatically lowering cost of getting access
  • now we have an explosion of buzzwords and this is how I think they fit together
  • now we have an explosion of buzzwords and this is how I think they fit together
  • Add colaboratorie to logo – priority 2
  • + how profound is the impact? + newspaper impact…craigslist set things in motion by removing classifieds revenues for many newspapers +
  • This has something to do with managing the conversations – getting collaborative at every step of the game
  • Add colaboratorie to logo – priority 2
  • Its one thing to have disagreement and debate
  • Add colaboratorie to logo – priority 2
  • now we have an explosion of buzzwords and this is how I think they fit together
  • Add colaboratorie to logo – priority 2
  • Add colaboratorie to logo – priority 2

Transcript

  • 1.    Knowledge  from  Crowds  –  Be1er  with  Ins6tu6ons  +  Algorithms    h1p://goo.gl/q1DNL          Shaun  Abrahamson      @shaunabe      
  • 2.  Tap  the  crowd  for  learning  
  • 3.  Where  did  Stanley  come  from?  
  • 4.  Crowd  data  recycled  into  knowledge  
  • 5.  Collec6ve  contribu6ons  into  holis6c  understanding  
  • 6.  Compe6ng  for  be1er  predic6ons  
  • 7.  Our  signals  into  rankings  
  • 8.    New  type  of  ins6tu6on  to  deliver  mobile  services  
  • 9. Influence    Capital        Assets   Labor    Data    The  crowd  as  the  gateway  to  critical  resources  
  • 10. Open Innovation, Co-Creation, Micro Tasks   Sharing, Commenting, Reviewing   “Big Data”   Influence    Capital        Assets   Labor*    Data    *  very  often  “knowledge”  work  
  • 11. Labor  /  Knowledge  Work       Influence    Capital        Assets    Data  Institutions                        vs                        Algorithms  
  • 12. 4 PERSPECTIVES! ! OUTCOMES! PEOPLE! TOOLS!ORGANIZATION!
  • 13. OUTCOMES!
  • 14. Ronald  Coase   “Given  that  produc6on  could  be   carried  on  without  any  organiza6on   that  is,  firms  at  all,  why  and  under   what  condi6ons  should  we  expect   firms  to  emerge?”   About  75  years  ago          Why  do  we  organize  work  in  a  certain  way?  
  • 15. ! R + D! Production! Operations! Marketing! Sales! !    New  Ins6tu6ons  +  tools  
  • 16. ! R + D! Production! Operations! Marketing! Sales! !    New  data  +  algorithms  
  • 17. CASE:!What went wrong at Wikipedia?!
  • 18. Source: Mary Meeker presentation at All things D. !    The  end  of  all  other  encyclopedia  business  models  
  • 19. Retention after 1 year (%) Active Editors    Why  aren’t  editors  staying?  
  • 20. PEOPLE! !FINDING AND MOTIVATING THE MOST IMPORTANT KNOWLEDGE RESOURCES!
  • 21. 175 million people on !    What  skills  might  you  tap  into?  
  • 22. Stuff! Money! Attention! Experience! Good!    Why  will  they  par6cipate?  
  • 23.    What  data  by-­‐products  might  you  rely  on?  
  • 24.    Why  did  you  start  contribu6ng  to  Wikipedia?  
  • 25.    What  kind  of  work  environment  do  you  want  ?  
  • 26. ORGANIZATION! ! WHAT INSTITUTITIONSARE CRITICAL TO BENEFIT FROM CROWD LABOR + INFLUENCE!
  • 27. Elinor  Ostrom   “Its  a  problem,  its  just  not  necessarily  a   tragedy  ...  The  problem  is  that  people  can   overuse  [a  shared  resource],  it  can  be   destroyed,  and  it  is  a  big  challenge  to   figure  out  how  to  avoid  that.”   About  2  years  ago          Organizing  to  resolve  the  “Tragedy  of  Commons”  
  • 28.    Who  owns  what?  Brand  vs  IP  vs  Confiden6ality  
  • 29.    Cheap  access  to  dispute  resolu6on  
  • 30. Collec6ve  choice  processes  
  • 31. Sanc6on  bad  behavior  -­‐  Don’t  feed  the  trolls  
  • 32.    Nes6ng  to  scale  
  • 33. The  communitys  role,  as  some  kind  of  nebulous  science-­‐fic6on   super-­‐en6ty,  is  to:          +  Organize  and  edit  individual  pages        +  Structure  naviga6on  between  pages        +  Resolve  conflict  between  individual  members        +  Re-­‐engineer  itself  -­‐-­‐  crea6ng  rules  and  pa1erns  of            behavior     There  are  other  roles  J     Source  -­‐  h1p://meta.wikimedia.org/wiki/The_Wikipedia_Community        How  Wikipedia  community  sees  itself  
  • 34. TOOLS! ! !COLLECTING DATA AND CREATING NEW UNDERSTANDING!
  • 35.    Making  it  easier  to  contribute  
  • 36. My Klout! My Giving (Crowdtwist)! My Creative Impact(Jovoto)!    Understanding  individual  contribu6ons  
  • 37.    Understanding  collec6ve  health  and  performance  
  • 38.    Rocket  Science  vs  People  Science?  
  • 39.  Making  sense  of  all  that  data  
  • 40.    Wikimedia  founda6on’s  focus  People  +  Tools  
  • 41. CASE: EDX.ORG! ! NEW EDUCATIONAL INSTITUTIONS ! + !DATA TO GET SMARTER ABOUT EDUCATION!
  • 42.    Content  +  Community  =  Learning  
  • 43. 155,000 registered! 23,000 tried the first problem set! 9,000 passed the midterm! 7,157 passed the course!  Represents  about  40  years  worth  of  classes  at  MIT  
  • 44.    Ricardo  +  Arthur  doing  “online  learning”  
  • 45. “One of the best things about 6.002x was the community built by the students themselves. The atmosphere was great: people shared their enthusiasm and knowledge, and lended a hand to those like me who didn’t have the basics for the course.” - Arthur Amaral, 18 years old, Brazil Source: http://blog.edx.org/    Community  not  just  content  
  • 46.    Ins6tu6ons  -­‐  Nes6ng  +  Collec6ve  Choice  
  • 47. Anant  Agarwal   “We  can  watch  how  many  a1empts   students  made  before  they  got  an   exercise  right,  and  if  they  got  it  wrong,   what  they  used  to  try  to  find  a  solu6on.   Did  they  go  to  the  textbook,  go  back  and   watch  the  video,  go  to  the  forum  and  post   a  ques6on?”   About  1  month  ago          Data  to  learn  how  to  teach  
  • 48. CASE: GIFFGAFF [TELEFONICA]! ! NEW INSTITUTIONS!TO BENEFIT FROM THE KNOWLEDGE OF ! CUSTOMERS!
  • 49.    Social  produc6on  for  a  complex  service  
  • 50.    Homepage  hints  at  how  this  works  
  • 51.    What  tasks  can  be  performed?  
  • 52. Value Created! ! R + D! Production! Operations! Marketing! Sales! !       Income/Expenses!  Who  is  doing  what  on  GiffGaff  
  • 53.  From  sales  +  support  to  new  app  development  
  • 54.  Encouraging  Par6cipa6on  +  Rewarding  Behavior  
  • 55.     Gaming  the  system  -­‐  posts  from  users  who  you  suspect  are  abusing  the  payback  system   by  using  mul6ple  accounts  to  give  themselves  solu6ons  or  kudos.     Tou0ng  for  SIMS/Kudos  -­‐  posts  which  are  ac6vely  asking  for  kudos  or  solu6ons,  it  is  fine   to  have  this  in  your  signature  but  not  to  ask  in  a  post/topic.     Incorrect  Accepted  Solu0ons  -­‐  if  you  spot  an  accepted  solu6on  which  is  incorrect  or  if  a   user  has  accepted  one  of  their  own  responses  as  a  solu6on  unjus6fiably.     Incorrect  Tags  -­‐  If  you  see  that  a  post  has  been  tagged  with  an  irrelevant  or   inappropriate  tag.     Inappropriate  Content  -­‐  Posts  which  are  disrespecmul  to  other  users,  profanity,   adver6sing,  naming  and  shaming  and  generally  causing  discord  or  disharmony  on  the   forum.    Self  policing  mechanisms  
  • 56. How  is  this  growing?  Also  NPS  =  73  (Apple  =  79)  
  • 57. Labor  /  Knowledge  Work       Influence    Capital        Assets    Data  Institutions                        vs                        Algorithms  
  • 58. Anant  Agarwal   “We  can  watch  how  many  a1empts   students  made  before  they  got  an   exercise  right,  and  if  they  got  it  wrong,   what  they  used  to  try  to  find  a  solu6on.   Did  they  go  to  the  textbook,  go  back  and   watch  the  video,  go  to  the  forum  and  post   a  ques6on?”   About  1  month  ago        Data  +  Algorithms  for  Knowledge  Management  
  • 59. Elinor  Ostrom   “Its  a  problem,  its  just  not  necessarily  a   tragedy  ...  The  problem  is  that  people  can   overuse  [a  shared  resource],  it  can  be   destroyed,  and  it  is  a  big  challenge  to   figure  out  how  to  avoid  that.”   About  2  years  ago          Community  +  Institutions  for  Knowledge  Management  
  • 60.    Knowledge  from  Crowds  –  Be1er  with  Ins6tu6ons  +  Algorithms    h1p://goo.gl/q1DNL          Shaun  Abrahamson      @shaunabe