Many data mining approaches aim at modelling and predicting human behaviour. An important quantity of interest is the quality of model-based predictions, e.g. for finding a competition winner with best prediction performance. In real life, human beings meet their decisions with considerable uncertainty. Its assessment and resulting implications for statistically evident evaluation of predictive models are in the main focus of this contribution. We identify relevant sources of uncertainty as well as the limited ability of its accurate measurement, propose an uncertainty-aware methodology for more evident evaluations of data mining approaches, and discuss its implications for existing quality assessment strategies. Specifically, our approach switches from common point-paradigm to more appropriate distribution-paradigm. This is exemplified in the context of recommender systems and their established metrics of prediction quality. The discussion is substantiated by comprehensive experiments with real users, large-scale simulations, and discussion of prior evaluation campaigns (i.a. Netflix Prize) in the light of human uncertainty aspects.
In this article we introduce a probability distribution generated by a mixture of discrete random variables to capture uncertainty, feeling, and overdispersion, possibly present in ordinal data surveys. The choice of the components of the new model is motivated by a study on the data generating process. Inferential issues concerning the maximum likelihood estimates and the validation steps are presented; then, some empirical analyses are given to support the usefulness of the approach. Discussion on further extensions of the model ends the article. I describe a cognitive latent variable model, a combination of a cognitive model and a latent variable model that can be used to aggregate information regarding cognitive parameters across participants and tasks. The model is ideally suited for uncovering relationships between latent task abilities as they are expressed in experimental paradigms, but can also be used as data fusion tools to connect latent abilities with external covariates from entirely different data sources. An example application deals with the structure of cognitive abilities underlying an executive functioning task and its relation to personality traits. © 2014 Elsevier Inc.