Knowledge	  from	  Crowds	  –	  Be1er	  with	  Ins6tu6ons	  +	  Algorithms	  	  h1p://goo.gl/q1DNL	  	  	  	  	  Shaun...
 Tap	  the	  crowd	  for	  learning	  
 Where	  did	  Stanley	  come	  from?	  
 Crowd	  data	  recycled	  into	  knowledge	  
 Collec6ve	  contribu6ons	  into	  holis6c	  understanding	  
 Compe6ng	  for	  be1er	  predic6ons	  
 Our	  signals	  into	  rankings	  
 	  New	  type	  of	  ins6tu6on	  to	  deliver	  mobile	  services	  
Influence	                                         	  Capital	  	  	                                                       ...
Open Innovation, Co-Creation, Micro Tasks	                                       Sharing, Commenting, Reviewing	          ...
Labor	  /	  Knowledge	  Work	  	  	                                                   Influence	                           ...
4 PERSPECTIVES!       !   OUTCOMES!    PEOPLE!     TOOLS!ORGANIZATION!
OUTCOMES!
Ronald	  Coase	                                        “Given	  that	  produc6on	  could	  be	                            ...
!        R + D!                 Production!    Operations!   Marketing!   Sales!                                          ...
!        R + D!                  Production!   Operations!   Marketing!   Sales!                                          ...
CASE:!What went wrong at Wikipedia?!
Source: Mary Meeker presentation at All things D. !	  	  The	  end	  of	  all	  other	  encyclopedia	  business	  models	  
Retention after 1 year (%)                                                   Active Editors	  	  Why	  aren’t	  editors	  ...
PEOPLE!            !FINDING AND MOTIVATING  THE MOST IMPORTANT  KNOWLEDGE RESOURCES!
175 million people on !	  	  What	  skills	  might	  you	  tap	  into?	  
Stuff!                     Money!      Attention!                                       Experience!                       ...
 	  What	  data	  by-­‐products	  might	  you	  rely	  on?	  
 	  Why	  did	  you	  start	  contribu6ng	  to	  Wikipedia?	  
 	  What	  kind	  of	  work	  environment	  do	  you	  want	  ?	  
ORGANIZATION!           ! WHAT INSTITUTITIONSARE CRITICAL TO BENEFIT  FROM CROWD LABOR +       INFLUENCE!
Elinor	  Ostrom	                                  “Its	  a	  problem,	  its	  just	  not	  necessarily	  a	               ...
 	  Who	  owns	  what?	  Brand	  vs	  IP	  vs	  Confiden6ality	  
 	  Cheap	  access	  to	  dispute	  resolu6on	  
Collec6ve	  choice	  processes	  
Sanc6on	  bad	  behavior	  -­‐	  Don’t	  feed	  the	  trolls	  
 	  Nes6ng	  to	  scale	  
The	  communitys	  role,	  as	  some	  kind	  of	  nebulous	  science-­‐fic6on	   super-­‐en6ty,	  is	  to:	   	   	  	  	 ...
TOOLS!          !            !COLLECTING DATA AND   CREATING NEW   UNDERSTANDING!
 	  Making	  it	  easier	  to	  contribute	  
My Klout!                                    My Giving (Crowdtwist)!              My Creative Impact(Jovoto)!	  	  Underst...
 	  Understanding	  collec6ve	  health	  and	  performance	  
 	  Rocket	  Science	  vs	  People	  Science?	  
 Making	  sense	  of	  all	  that	  data	  
 	  Wikimedia	  founda6on’s	  focus	  People	  +	  Tools	  
CASE: EDX.ORG!           !  NEW EDUCATIONAL   INSTITUTIONS !          + !DATA TO GET SMARTER  ABOUT EDUCATION!
 	  Content	  +	  Community	  =	  Learning	  
155,000 registered!  23,000 tried the first problem set!  9,000 passed the midterm!  7,157 passed the course!	  Represents...
 	  Ricardo	  +	  Arthur	  doing	  “online	  learning”	  
“One of the best things about 6.002x was the     community built by the students themselves.     The atmosphere was great:...
 	  Ins6tu6ons	  -­‐	  Nes6ng	  +	  Collec6ve	  Choice	  
Anant	  Agarwal	                                              “We	  can	  watch	  how	  many	  a1empts	                   ...
CASE: GIFFGAFF     [TELEFONICA]!           ! NEW INSTITUTIONS!TO BENEFIT FROM THE    KNOWLEDGE OF !      CUSTOMERS!
 	  Social	  produc6on	  for	  a	  complex	  service	  
 	  Homepage	  hints	  at	  how	  this	  works	  
 	  What	  tasks	  can	  be	  performed?	  
Value Created!                                                                                 !       R + D!             ...
 From	  sales	  +	  support	  to	  new	  app	  development	  
 Encouraging	  Par6cipa6on	  +	  Rewarding	  Behavior	  
 	     Gaming	  the	  system	  -­‐	  posts	  from	  users	  who	  you	  suspect	  are	  abusing	  the	  payback	  system	 ...
How	  is	  this	  growing?	  Also	  NPS	  =	  73	  (Apple	  =	  79)	  
Labor	  /	  Knowledge	  Work	  	  	                                                   Influence	                           ...
Anant	  Agarwal	                                    “We	  can	  watch	  how	  many	  a1empts	                             ...
Elinor	  Ostrom	                               “Its	  a	  problem,	  its	  just	  not	  necessarily	  a	                  ...
 	  Knowledge	  from	  Crowds	  –	  Be1er	  with	  Ins6tu6ons	  +	  Algorithms	  	  h1p://goo.gl/q1DNL	  	  	  	  	  Shaun...
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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.
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
  • Knowledge From Crowds - Better with Institutions + Algorithms

    1. 1.    Knowledge  from  Crowds  –  Be1er  with  Ins6tu6ons  +  Algorithms    h1p://goo.gl/q1DNL          Shaun  Abrahamson      @shaunabe      
    2. 2.  Tap  the  crowd  for  learning  
    3. 3.  Where  did  Stanley  come  from?  
    4. 4.  Crowd  data  recycled  into  knowledge  
    5. 5.  Collec6ve  contribu6ons  into  holis6c  understanding  
    6. 6.  Compe6ng  for  be1er  predic6ons  
    7. 7.  Our  signals  into  rankings  
    8. 8.    New  type  of  ins6tu6on  to  deliver  mobile  services  
    9. 9. Influence    Capital        Assets   Labor    Data    The  crowd  as  the  gateway  to  critical  resources  
    10. 10. Open Innovation, Co-Creation, Micro Tasks   Sharing, Commenting, Reviewing   “Big Data”   Influence    Capital        Assets   Labor*    Data    *  very  often  “knowledge”  work  
    11. 11. Labor  /  Knowledge  Work       Influence    Capital        Assets    Data  Institutions                        vs                        Algorithms  
    12. 12. 4 PERSPECTIVES! ! OUTCOMES! PEOPLE! TOOLS!ORGANIZATION!
    13. 13. OUTCOMES!
    14. 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. 15. ! R + D! Production! Operations! Marketing! Sales! !    New  Ins6tu6ons  +  tools  
    16. 16. ! R + D! Production! Operations! Marketing! Sales! !    New  data  +  algorithms  
    17. 17. CASE:!What went wrong at Wikipedia?!
    18. 18. Source: Mary Meeker presentation at All things D. !    The  end  of  all  other  encyclopedia  business  models  
    19. 19. Retention after 1 year (%) Active Editors    Why  aren’t  editors  staying?  
    20. 20. PEOPLE! !FINDING AND MOTIVATING THE MOST IMPORTANT KNOWLEDGE RESOURCES!
    21. 21. 175 million people on !    What  skills  might  you  tap  into?  
    22. 22. Stuff! Money! Attention! Experience! Good!    Why  will  they  par6cipate?  
    23. 23.    What  data  by-­‐products  might  you  rely  on?  
    24. 24.    Why  did  you  start  contribu6ng  to  Wikipedia?  
    25. 25.    What  kind  of  work  environment  do  you  want  ?  
    26. 26. ORGANIZATION! ! WHAT INSTITUTITIONSARE CRITICAL TO BENEFIT FROM CROWD LABOR + INFLUENCE!
    27. 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. 28.    Who  owns  what?  Brand  vs  IP  vs  Confiden6ality  
    29. 29.    Cheap  access  to  dispute  resolu6on  
    30. 30. Collec6ve  choice  processes  
    31. 31. Sanc6on  bad  behavior  -­‐  Don’t  feed  the  trolls  
    32. 32.    Nes6ng  to  scale  
    33. 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. 34. TOOLS! ! !COLLECTING DATA AND CREATING NEW UNDERSTANDING!
    35. 35.    Making  it  easier  to  contribute  
    36. 36. My Klout! My Giving (Crowdtwist)! My Creative Impact(Jovoto)!    Understanding  individual  contribu6ons  
    37. 37.    Understanding  collec6ve  health  and  performance  
    38. 38.    Rocket  Science  vs  People  Science?  
    39. 39.  Making  sense  of  all  that  data  
    40. 40.    Wikimedia  founda6on’s  focus  People  +  Tools  
    41. 41. CASE: EDX.ORG! ! NEW EDUCATIONAL INSTITUTIONS ! + !DATA TO GET SMARTER ABOUT EDUCATION!
    42. 42.    Content  +  Community  =  Learning  
    43. 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. 44.    Ricardo  +  Arthur  doing  “online  learning”  
    45. 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. 46.    Ins6tu6ons  -­‐  Nes6ng  +  Collec6ve  Choice  
    47. 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. 48. CASE: GIFFGAFF [TELEFONICA]! ! NEW INSTITUTIONS!TO BENEFIT FROM THE KNOWLEDGE OF ! CUSTOMERS!
    49. 49.    Social  produc6on  for  a  complex  service  
    50. 50.    Homepage  hints  at  how  this  works  
    51. 51.    What  tasks  can  be  performed?  
    52. 52. Value Created! ! R + D! Production! Operations! Marketing! Sales! !       Income/Expenses!  Who  is  doing  what  on  GiffGaff  
    53. 53.  From  sales  +  support  to  new  app  development  
    54. 54.  Encouraging  Par6cipa6on  +  Rewarding  Behavior  
    55. 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. 56. How  is  this  growing?  Also  NPS  =  73  (Apple  =  79)  
    57. 57. Labor  /  Knowledge  Work       Influence    Capital        Assets    Data  Institutions                        vs                        Algorithms  
    58. 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. 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. 60.    Knowledge  from  Crowds  –  Be1er  with  Ins6tu6ons  +  Algorithms    h1p://goo.gl/q1DNL          Shaun  Abrahamson      @shaunabe      
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