3 ms·

My favorite example: 1) Randomly divide people into two groups. 2) Ask everyone in the first group this question: "If someone sues you and loses, is it fair t

My favorite example: 1) Randomly divide people into two groups. 2) Ask everyone in the first group this question: "If someone sues you and loses, is it fair that they should pay your legal costs?" Most people will say yes. 3) Ask everyone in the second group this question: "If you sue someone and lose, is it fair that you should pay their legal costs?" Most people will say no. Even though it's the same fucking question. That's why we need shared enforced norms :-)

stillsut 11y ago
I like this example and might steal it. But I'd offer you a [bayesian] counter-argument: Say an HIV test is 99% accurate and responds positive on a randomly selected person. What's the chance they actually have HIV? About 5%! [0]. However, if you have prior reasons to believe you may have HIV, the chances are a lot higher than 5% given a positive test. To tie that back to lawsuits, your participants in the study have their priors set so that they don't expect to engage in frivolous lawsuits towards anyone. However they will likely recognize that they could fall victim to a frivolous lawsuit, and if they're normal, then the only type of lawsuits they could imagine being forced on them is a frivolous suit. Another way to say that is: probably 90% of the ridiculous lawsuits originate from 1% of the population. Then 99% of your participants will mentally model themselves as a sincere plaintiff (thus they shouldn't have to pay even if they lose), while a 1% frivolous rate is certainly high enough to make plausible the threat of being the defendant, and thus worthy of protection through legal fee reimbursement. [0]: http://cs.wellesley.edu/~cs199/lectures/34-bayes-rule.html http://cs.wellesley.edu/~cs199/lectures/34-bayes-rule.html
- cousin_it 11y ago
  Yes, fair enough.
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stillsut 11y ago

I like this example and might steal it. But I'd offer you a [bayesian] counter-argument: Say an HIV test is 99% accurate and responds positive on a randomly selected person. What's the chance they actually have HIV? About 5%! [0]. However, if you have prior reasons to believe you may have HIV, the chances are a lot higher than 5% given a positive test. To tie that back to lawsuits, your participants in the study have their priors set so that they don't expect to engage in frivolous lawsuits towards anyone. However they will likely recognize that they could fall victim to a frivolous lawsuit, and if they're normal, then the only type of lawsuits they could imagine being forced on them is a frivolous suit. Another way to say that is: probably 90% of the ridiculous lawsuits originate from 1% of the population. Then 99% of your participants will mentally model themselves as a sincere plaintiff (thus they shouldn't have to pay even if they lose), while a 1% frivolous rate is certainly high enough to make plausible the threat of being the defendant, and thus worthy of protection through legal fee reimbursement. [0]: http://cs.wellesley.edu/~cs199/lectures/34-bayes-rule.html http://cs.wellesley.edu/~cs199/lectures/34-bayes-rule.html

cousin_it 11y ago
Yes, fair enough.
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