Prediction Science: How Well Can We Predict the Future? Jon Puleston, Lightspeed GMI #NetGain2015 #MRX


Netgain 2015Live blogging from the Net Gain 2015 conference in Toronto, Canada. Any errors or bad jokes are my own.

Prediction Science: How Well Can We Predict the Future?

Jon Puleston, VP of Innovation at Lightspeed GMI

  • [Jon had the room take a quiz on a random series of questions]

  • What has he learned about predictions? Better at predicting certain things, behaviors. Not so good at predicting prices.
  • You can isolate people who are good at predicting, perhaps find them and use their super power🙂
  • Prediction isn’t dependent on sample size. One person can be sufficient. It’s more about sample diversity and the intelligence of that sample.
  • 16 is a crowd if they are all well-informed. That’s all you need.
  • You could ask 1000 people or 5 people about the weather next week, but you really only need to ask the 1 person who saw a weather forecast.
  • How do you aggregate crowd wisdom – mean/median/mode
  • Crowds mean that errors get distilled out
  • A crowd of people predicted the price of the ipad within 1%
  • But without any knowledge, the crowd is ignorant. People NOW can’t predict the weight of a cow.
  • Prediction is littered with cognitive biases – 68% of people will say that a coin will toss heads because we always hear ‘heads’ first
  • People’s preferences for wine depend on whether you ask “Who prefers red wine?” vs “Who prefers white wine?”
  • People who check their emails before breakfast are more likely to say people check their email before breakfast
  • Emotions get in the way of making valid predictions. We are more positive about our own teams vs other teams when predicting score counts.
  • Do you you clean up after a meeting vs Do people clean up after meetings. People say they do but they don’t. [I do. Even when I wasn’t in the meeting.]
  • Read: http://www.amazon.com/Expert-Political-Judgment-Good-Know/dp/0691128715
  • only 48% of stock market gurus stock market predictions were correct
  •  People who ‘bet’ 1 unit not as good as 2, but betting 3 is a little better. People who bet against are really good.
  • After 15 people predict and other people see that, they start to predict the same way, the answers don’t move.
  • But in an independent voting method, larger surveys are better than 15 people
  • Best predictive market situations allow sharing of information, let people discuss and debate
  • e.g., in guess which mug is most popular, someone will suggest mugs are good gifts, someone will suggest lots of people garden, people decide that the gardening mug will be most popular
  • Think of board room meetings where they didn’t discuss things before they vote on a decision.  Stray comments are problematic.
  • Try dividing up the herd and then recombine the three groups back into one. Helps improve accuracy just like how we run 3 focus groups not 1.
  • Let people change their opinions in surveys [We NEVER let people do that!]
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