intuition vs. analysis

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17 Terms

1
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3 key features of intuitive decision-making

  • process dominated by subconscious

  • see situation as a whole and not in parts

  • connected with your emotions; something does/doesn’t feel right

2
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intuitive processing can be

remarkably accurate

3
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thin slice judgements

tiny, quick judgments can be surprisingly accurate

4
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unconscious processing can be useful for making decisions

easier to pick jams

5
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unconscious thought theory

  • conscious perform well under simple conditions and poorly under demanding ones

  • unconscious performed consistently well

6
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our unconscious can

impact decisions in all sorts of ways (not only positively)

7
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priming effects

  • being exposed to a concept can impact

    • social judgment

    • evaluations

    • behavior

8
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“moneyball approach”

relying on a linear model so pitfalls of “human” judgment won’t get in the way of a good choice

9
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linear models are vastly superior

linear model dramatically outperforms admission committee ratings as a predictor of program success

10
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intuition vs. analysis

despite era of “big data” many major decisions still made based on instinct

11
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when is intuition useful?

when based on experience in seeing relevant patterns… not the same as just a gut instinct

12
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keeping intuition in check

  • self checking

    • don’t be fooled by intuition (confirmation bias, dissonance, self-serving biases, etc.)

    • continual feedback…willingness to adjust

  • “fact-based culture”

13
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data has to come from somewhere

source can be incorrect or misleading

14
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someone still has to interpret the data

data interpretation is more of an art than a science, even experts, given the same daaset, might reach opposing results

15
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lots of data doesn’t mean you’re “right”

biases in algorithm creation process + algorithms reflect existing biases

16
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is the goal always efficiency or “success”

yeah

17
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building “quantitative intuition”

  • what data was collected?

  • what data am i not seeing” (surveyor bias)

  • can i trust the data and analyses?

  • putting the data into context

  • pressure test the analyses - what happens when you change key numbers

    • if it looks wrong, it probably is

    • if it looks right, it may still be wrong