Optimal Number of Choices in Rating Contexts

TL;DR

This paper investigates the optimal number of discrete choices humans should use when rating entities, revealing that fewer options can sometimes outperform more, especially considering human cognitive limitations.

Contribution

It provides a theoretical and computational analysis of the optimal number of rating choices, showing that fewer options can be optimal in certain models despite the temptation to use all available choices.

Findings

Using fewer choices can be optimal in some scenarios.

More options do not always lead to better performance.

Practical considerations favor smaller choice sets due to human limitations.

Abstract

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. We consider the case when scores are uniform random and Gaussian. We study computationally when using 2, 3, 4, 5, and 10 options out of a total of 100 is optimal in these models (though our theoretical analysis is for a more general setting with $k$ choices from $n$ total options as well as a continuous underlying space). 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 could have many potential applications, as settings requiring entities to be ranked by humans are ubiquitous. There could also be applications to other fields such as signal or image processing where input values from a large set must be mapped to output values in a smaller set.