… To do so, we define interpretability as the ability of the model to have both a quantifiable
uncertainty of its parameters as well as an understandable or meaningful association with its …

… , there is uncertainty associated with them. Taking this uncertainty into account in numerical
… the one we propose will be virtually identical and the risk of a draw from the Krinsky-Robb …

… behaviours relating to this opt-out alternative when modelling discrete choice experiments…
choice models. We aim to contribute to a better understanding of how to model opt-out choices …

… Our test can allow for risk-loving and elation-seeking attitudes, or it can require risk aversion.
It can … from a particular model. We evaluate the performance of various models under risk …

… uncertainty in mine planning and design; (2) accounting for the social risk and uncertainty is
the main … to account for uncertainty due to other variables once their distributions are known. …

… choice model for the choice whether to evacuate or not. We find two latent segments
distinguished by demographics and risk … in the choice-set, and model uncertainty. These “second-…

… We used discrete choice experiments (DCEs) among … , uncertainty around how the epidemic
may develop contributed to the risk evaluation: 60% of HCWs in the study were uncertain …

… the gap between discrete choice models and behavioral … a “black-box” discrete choice model
whose utility functions depend … accounting for the effect of risk aversion on preferences. The …

… : the role of uncertainty as a barrier to energy retrofit decisions. We develop a discrete choice
… efficiency investment related to the risk and uncertainty barriers. Although some theoretical …

… discrete choice models. For more in-depth discussion, interested readers may refer to
Karlstrom and Morey (2003), Batley and Ibáñez Rivas (2013), Dekker (2014), Dekker and Chorus (…