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You want to maximize the probability of selecting a high-quality candidate. High can pass 100% of tests; low can only pass 90%. You can give two (simultaneous) tests. Do you give them to a single candidate, or two different candidates? How do you choose the candidate(s) to test? @Ed_Van_Wesep/1228811770418139136
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SPOILER ALERT 🚨 Test one candidate twice. She gets picked 100% if she’s high type, and 81% even if she’s low! How do you pick which candidate to test? Randomly 🤷🏻♂️, unless you have even an epsilon bias for/against some candidates. 2/5
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.@Ed_Van_Wesep, Shaun, and Brian show that in a much broader class of models (eg, continuous learning, costly learning rather than a fixed budget), it’s NEVER optimal to learn equally about all candidates. Tiebreaking (statistical discrim OR animus) can get hugely amplified. 3/5
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Depending on parameters, favored candidates can be screened more intensively (looking for rare disqualifiers) or less intensively (looking for diamonds in the rough 💎). Implications not only for classic “discrimination” questions (eg, gender, race), but also… 4/5
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… why do organizations promote internally rather than hire externally? Why do we do in-person interviews at the ASSAs and flyouts rather than Skype? Monogamy? Loved chatting w @Ed_Van_Wesep about their idea last summer, and thrilled they wrote it! 5/5 @lukestein/1228346787519500290 https://t.co/LpQplHOo2B