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Was Nate Silver Wrong?

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By Bram Wessel, first presented at the 2017 Information Architecture Summit. "Was Nate Silver Wrong: The Crisis of Modeling in an Era of Clickbait, Fake News, and Alternative Facts." This talk explores what election forecasting and information architecture can learn from each other and how your business can survive in our noisy information environment.

Transcript: http://factorfirm.com/posts/transcript-was-nate-silver-wrong/

Published in: Internet
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  • Transcript: http://factorfirm.com/posts/transcript-was-nate-silver-wrong/
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Was Nate Silver Wrong?

  1. 1. @bramwessel | @factorfirm | factorfirm.com
  2. 2. @bramwessel @factorfirm factorfirm.com
  3. 3. @bramwessel @factorfirm factorfirm.com WRONG?
  4. 4. @bramwessel | @factorfirm | factorfirm.com
  5. 5. @bramwessel @factorfirm factorfirm.com
  6. 6. @bramwessel @factorfirm factorfirm.com
  7. 7. @bramwessel @factorfirm factorfirm.com
  8. 8. @bramwessel @factorfirm factorfirm.com
  9. 9. @bramwessel @factorfirm factorfirm.com
  10. 10. @bramwessel @factorfirm factorfirm.com
  11. 11. @bramwessel @factorfirm factorfirm.com PLACES | THINGS MADE OF INFO
  12. 12. @bramwessel @factorfirm factorfirm.com Attention Economy
  13. 13. @bramwessel @factorfirm factorfirm.com Hidden costs?
  14. 14. @bramwessel @factorfirm factorfirm.com INFLECTION POINT IS INFLECTED
  15. 15. @bramwessel @factorfirm factorfirm.com Is the public less informed?
  16. 16. @bramwessel @factorfirm factorfirm.com Fake News @bramwessel | @factorfirm | factorfirm.com
  17. 17. @bramwessel @factorfirm factorfirm.com TRUTHINESS
  18. 18. @bramwessel @factorfirm factorfirm.com ALTERNATIVE FACTS
  19. 19. @bramwessel @factorfirm factorfirm.com STRAW POLL…
  20. 20. @bramwessel @factorfirm factorfirm.com Clickbait ad image
  21. 21. @bramwessel @factorfirm factorfirm.com
  22. 22. @bramwessel @factorfirm factorfirm.com
  23. 23. @bramwessel @factorfirm factorfirm.com @bramwessel | @factorfirm | factorfirm.com
  24. 24. @bramwessel @factorfirm factorfirm.com CONTENT FARMS DGAF ABOUT UR CONTENT
  25. 25. @bramwessel @factorfirm factorfirm.com PRODUCT IS US SUCH BIZ MODEL SO ATTENTIVE
  26. 26. @bramwessel @factorfirm factorfirm.com TFW human interpretation doesn’t matter THIS IS NOT THE CRAP WE’RE LOOKING FOR
  27. 27. @bramwessel @factorfirm factorfirm.com
  28. 28. @bramwessel @factorfirm factorfirm.com @bramwessel | @factorfirm | factorfirm.com
  29. 29. @bramwessel @factorfirm factorfirm.com
  30. 30. @bramwessel @factorfirm factorfirm.com
  31. 31. @bramwessel @factorfirm factorfirm.com Doing more work for the machines @bramwessel | @factorfirm | factorfirm.com
  32. 32. @bramwessel @factorfirm factorfirm.com WHAT INFLUENCE DO WE REALLY HAVE?
  33. 33. @bramwessel @factorfirm factorfirm.com
  34. 34. @bramwessel @factorfirm factorfirm.com RECURSIVE FEEDBACK LOOP IS RECURSIVE @bramwessel | @factorfirm | factorfirm.com
  35. 35. @bramwessel @factorfirm factorfirm.com
  36. 36. @bramwessel @factorfirm factorfirm.com Dopamine Rush NORMAL COCAINE
  37. 37. @bramwessel @factorfirm factorfirm.com Who benefits? FOLLOW THE MONEY
  38. 38. @bramwessel @factorfirm factorfirm.com NOW WITH MORE LOSSY EFFICIENT MARKET
  39. 39. @bramwessel @factorfirm factorfirm.com DDOS ON MEANING
  40. 40. MUGGLES DON’T HAVE TIME NEITHER DO WIZARDS
  41. 41. @bramwessel @factorfirm factorfirm.com
  42. 42. @bramwessel @factorfirm factorfirm.com WRONG?
  43. 43. @bramwessel @factorfirm factorfirm.com How is Data Journalism like IA?
  44. 44. @bramwessel @factorfirm factorfirm.com@bramwessel | @factorfirm | factorfirm.com
  45. 45. @bramwessel @factorfirm factorfirm.com What’s your evidence?
  46. 46. @bramwessel @factorfirm factorfirm.com Data vs. Access Quant vs. Qual
  47. 47. @bramwessel @factorfirm factorfirm.com But, getting good data is getting harder.
  48. 48. @bramwessel @factorfirm factorfirm.com Downstream Effects
  49. 49. @bramwessel @factorfirm factorfirm.com WRONG?
  50. 50. @bramwessel @factorfirm factorfirm.com “So number one, and this kind of goes back to the supply chain, but if you're working with actually trying to manufacture something out of data, to come to some analysis or some conclusion, you have to make sure the data is good to begin with.” — Nate Silver, 2011 IAS Keynote
  51. 51. @bramwessel @factorfirm factorfirm.com The information supply chain. Input Output Noise The Information Supply Chain
  52. 52. @bramwessel @factorfirm factorfirm.com First principle: the data is wrong.
  53. 53. @bramwessel @factorfirm factorfirm.com Embracing entropy.
  54. 54. @bramwessel @factorfirm factorfirm.com We can’t read users’ minds.
  55. 55. @bramwessel @factorfirm factorfirm.com How to correct for wrong data?
  56. 56. @bramwessel @factorfirm factorfirm.com
  57. 57. @bramwessel @factorfirm factorfirm.com What inputs do we trust?
  58. 58. @bramwessel @factorfirm factorfirm.com Prioritizing Input
  59. 59. @bramwessel @factorfirm factorfirm.com
  60. 60. @bramwessel @factorfirm factorfirm.com
  61. 61. @bramwessel @factorfirm factorfirm.com
  62. 62. @bramwessel @factorfirm factorfirm.com
  63. 63. @bramwessel @factorfirm factorfirm.com
  64. 64. @bramwessel @factorfirm factorfirm.com User Research recruiting
  65. 65. @bramwessel @factorfirm factorfirm.com How likely is someone to vote?
  66. 66. @bramwessel @factorfirm factorfirm.com Likely voter models
  67. 67. @bramwessel @factorfirm factorfirm.com
  68. 68. @bramwessel @factorfirm factorfirm.com
  69. 69. @bramwessel @factorfirm factorfirm.com Moving target
  70. 70. Changes over time
  71. 71. @bramwessel @factorfirm factorfirm.com Changes over time BUT HER EMAILS!
  72. 72. @bramwessel @factorfirm factorfirm.com
  73. 73. @bramwessel @factorfirm factorfirm.com
  74. 74. @bramwessel @factorfirm factorfirm.com The peril of adjustments
  75. 75. @bramwessel @factorfirm factorfirm.com Apples to Apples @bramwessel @factorfirm factorfirm.com
  76. 76. @bramwessel @factorfirm factorfirm.com
  77. 77. @bramwessel @factorfirm factorfirm.com Third parties are not created equal.
  78. 78. @bramwessel @factorfirm factorfirm.com Trend Line Adjustment
  79. 79. @bramwessel @factorfirm factorfirm.com House Effects Adjustment
  80. 80. @bramwessel @factorfirm factorfirm.com Undecideds?
  81. 81. @bramwessel @factorfirm factorfirm.com Even more stuff? W’ELL BE RIGHT BACK… WITH MORE STUFF
  82. 82. @bramwessel @factorfirm factorfirm.com This message was approved by…
  83. 83. @bramwessel @factorfirm factorfirm.com Modeling is done by humans.
  84. 84. @bramwessel @factorfirm factorfirm.comElection forecasting is grounded in classic information theory.
  85. 85. @bramwessel @factorfirm factorfirm.com “Information is the resolution of uncertainty.” — Claude Shannon
  86. 86. @bramwessel @factorfirm factorfirm.com “Findability is at the center of a quiet revolution in how we define authority, allocate trust, and make decisions.” — Peter Morville, Ambient Findability
  87. 87. @bramwessel @factorfirm factorfirm.com What does this all mean for Information Architects?
  88. 88. @bramwessel @factorfirm factorfirm.com What are our information supply chains?
  89. 89. @bramwessel @factorfirm factorfirm.com How do we know?
  90. 90. @bramwessel @factorfirm factorfirm.com Our jobs will keep getting harder.
  91. 91. @bramwessel @factorfirm factorfirm.com
  92. 92. @bramwessel @factorfirm factorfirm.com Side doors
  93. 93. @bramwessel @factorfirm factorfirm.com Re-orienting Users
  94. 94. @bramwessel @factorfirm factorfirm.com We are the definers.
  95. 95. @bramwessel @factorfirm factorfirm.com Changes in Page Rank align with us
  96. 96. @bramwessel @factorfirm factorfirm.com
  97. 97. @bramwessel @factorfirm factorfirm.com
  98. 98. @bramwessel @factorfirm factorfirm.com What can we do as information architects on the ground?
  99. 99. @bramwessel @factorfirm factorfirm.com As IA’s, what effect do we have on the broader information environment?
  100. 100. @bramwessel @factorfirm factorfirm.com How do we give information users the ability determine quality?
  101. 101. @bramwessel @factorfirm factorfirm.com What are the implications for organizations?
  102. 102. @bramwessel @factorfirm factorfirm.com
  103. 103. @bramwessel @factorfirm factorfirm.com What are an organization’s information assets?
  104. 104. @bramwessel @factorfirm factorfirm.com What is the aboutness of your organization?
  105. 105. @bramwessel @factorfirm factorfirm.com Bring the aboutness forward.
  106. 106. @bramwessel @factorfirm factorfirm.com Resist the impulse to automate for its own sake.
  107. 107. @bramwessel @factorfirm factorfirm.com Automate intelligently.
  108. 108. @bramwessel @factorfirm factorfirm.com Consider the question.
  109. 109. @bramwessel @factorfirm factorfirm.com Prioritization = values.
  110. 110. @bramwessel @factorfirm factorfirm.com What can we do as information architects as a community? What is our responsibility as a community?
  111. 111. @bramwessel @factorfirm factorfirm.com What are our goals as a society?
  112. 112. @bramwessel @factorfirm factorfirm.com #DTDT #DTDT?
  113. 113. @bramwessel @factorfirm factorfirm.com
  114. 114. @bramwessel @factorfirm factorfirm.com
  115. 115. @bramwessel @factorfirm factorfirm.com Information environmentalists.
  116. 116. @bramwessel @factorfirm factorfirm.com Elevating the signal to noise ratio Restoring a healthy signal to noise ratio.
  117. 117. @bramwessel @factorfirm factorfirm.com We need to be careful about giving up human control.
  118. 118. @bramwessel @factorfirm factorfirm.com What ambient entropy do automated processes generate?
  119. 119. @bramwessel @factorfirm factorfirm.com Good, Cheap, Fast? Pick two.
  120. 120. @bramwessel @factorfirm factorfirm.com Engaging the community of thought.
  121. 121. @bramwessel @factorfirm factorfirm.com Human solutions for human challenges.
  122. 122. What the triple bottom line? What is our triple bottom line?
  123. 123. @bramwessel @factorfirm factorfirm.com MODELING FOR TRUTH, JUSTICE, AND THE CANADIAN WAY
  124. 124. @bramwessel @factorfirm factorfirm.com Modeling from the outside in.
  125. 125. @bramwessel @factorfirm factorfirm.com Modeling from the inside out.
  126. 126. @bramwessel @factorfirm factorfirm.com
  127. 127. @bramwessel @factorfirm factorfirm.com Make information great againWhat kind of information environment do we want to live in?
  128. 128. @bramwessel @factorfirm factorfirm.com The curatorial imperative.
  129. 129. @bramwessel @factorfirm factorfirm.com
  130. 130. @bramwessel @factorfirm factorfirm.com Chance 538 gave Trump of winning the presidency in 2016: 28.6% Chance a major league baseball player would get a hit during any at-bat in 2016: 25.5%
  131. 131. @bramwessel @factorfirm factorfirm.com LEAST WRONG
  132. 132. THANK YOU! @bramwessel | @factorfirm | factorfirm.com

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