Towards analyzing the impact of diversity and cardinality on the quality of collective prediction using interval estimates

Abstract

Recently, many research results have indicated that diversity is the most important characteristic of crowd-based applications. However, it is a point estimates-based finding in which single values are used as the representation of individual predictions on a real-life cognition task. This paper presents a study on how cardinality and diversity influence the quality of collective prediction using interval estimates. By means of computational experiments, we have found that these factors positively influence the quality of collective prediction. Besides, the results also indicate that the hypothesis “the higher the diversity, the better the quality of collective prediction” is true. Furthermore, the findings also reveal a cardinality threshold in which its increase does not significantly influence the quality of collective prediction.