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Tuesday, September 28, 2021

Problem Definition and Policy Estimation

 For Thursday, read Stone, ch. 14

Stories

Metaphors

Causal stories (p. 208) and COVID
  • Mechanical causes:  bureaucratic SOPs
  • Accidental causes:  force of nature
  • Intentional causes: conspiracy
  • Inadvertent causes:  lab leak

Stories and metaphors help determine the gathering and interpretation of data:  watch out for motivated reasoning.

The study, by Yale law professor Dan Kahan and his colleagues, has an ingenious design. At the outset, 1,111 study participants were asked about their political views and also asked a series of questions designed to gauge their "numeracy," that is, their mathematical reasoning ability. Participants were then asked to solve a fairly difficult problem that involved interpreting the results of a (fake) scientific study. But here was the trick: While the fake study data that they were supposed to assess remained the same, sometimes the study was described as measuring the effectiveness of a "new cream for treating skin rashes." But in other cases, the study was described as involving the effectiveness of "a law banning private citizens from carrying concealed handguns in public."
The result? Survey respondents performed wildly differently on what was in essence the same basic problem, simply depending upon whether they had been told that it involved guns or whether they had been told that it involved a new skin cream. What's more, it turns out that highly numerate liberals and conservatives were even more—not less—susceptible to letting politics skew their reasoning than were those with less mathematical ability.

 [/ 203

Aaron Sorkin and company explain:


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