In many settings people must give numerical scores to entities from a small discrete set. For instance, rating physical attractiveness from 1-5 on dating sites, or papers from 1-10 for conference reviewing. We study the problem of understanding when using a different number of options is optimal. For concreteness we assume the true underlying scores are integers from 1-100. We consider the case when scores are uniform random and Gaussian. We study when using 2, 3, 4, 5, and 10 options is optimal in these models. One may expect that using more options would always improve performance in this model, but we show that this is not necessarily the case, and that using fewer choices -- even just two -- can surprisingly be optimal in certain situations. While in theory for this setting it would be optimal to use all 100 options, in practice this is prohibitive, and it is preferable to utilize a smaller number of options due to humans' limited computational resources. Our results suggest that using a smaller number of options than is typical could be optimal in certain situations. This would have many potential applications, as settings requiring entities to be ranked by humans are ubiquitous.
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