Second-order induction in prediction problems

Rossella Argenziano and Itzhak Gilboa

Proceedings of the National Academy of Sciences, 2019, vol. 116, issue 21, 10323-10328

Abstract: Agents make predictions based on similar past cases, while also learning the relative importance of various attributes in judging similarity. We ask whether the resulting “empirically optimal similarity function” (EOSF) is unique and how easy it is to find it. We show that with many observations and few relevant variables, uniqueness holds. By contrast, when there are many variables relative to observations, nonuniqueness is the rule, and finding the EOSF is computationally hard. The results are interpreted as providing conditions under which rational agents who have access to the same observations are likely to converge on the same predictions and conditions under which they may entertain different probabilistic beliefs.

Keywords: belief formation; empirically optimal similarity function; learning; kernel estimation; generalized context model (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:nas:journl:v:116:y:2019:p:10323-10328

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