Prudent case-based prediction when experience is lacking
Patrick H. O'Callaghan
Papers from arXiv.org
An inexperienced predictor is asked to qualitatively rank eventualities according to their plausibility, given past cases. Inexperience means that, resampling past cases (with replacement) fails to generate a suitably diverse set of rankings. (4-diversity requires that each of the 4! strict rankings of four eventualities arises for some sample.) Along with other essential consistency requirements, 4-diversity yields a matrix representation that may be viewed as an empirical likelihood function (Gilboa and Schmeidler, 2003). We impose 2-diversity and derive a similar representation: provided the predictor is prudent enough to ensure that the arrival of novel cases will not force her into being dogmatic, intransitive or into revising her existing rankings. We build on this to establish a formal tradeoff between inexperience and the cognitive or computational cost of more abstract resampling.
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