… important implications for discrete choice modeling. For the … in the experimental literature on
choice under uncertainty. … data of this type, it is common to estimate a structural model of risk …

… We explore how threat and uncertainty are … uncertainty. Finally, we propose a clinically,
theoretically and empirically informed model for uncertainty distress, and outline how this model …

… A discrete choice model was estimated to investigate tourists’ choices. The estimation … them
conservative and keep away from risks and uncertainties. The significant standard deviation …

… Distinguishing between risk and uncertainty, this article … for modeling and regulating the
financial system. We find that: simple methods can sometimes dominate more complex modeling …

… Before proceeding to trilevel risk-averse modeling, we first develop a cardinality set that
represents all the possible locations for individual RES owners (eg, the prosumers) to make …

… BN models developed from uncertain sources and big data. … tools in risk analysis, risk
management, and decision science … may make model validation difficult, and may question model …

… stated preference survey, and a discrete choice model was estimated to analyze the 185 …
associated with novel options may be attributed to higher levels of uncertainty regarding a …

… model may decide that a person is at a high risk for … Broadly, uncertainty in datadriven
decision-making systems may … to this as model specification uncertainty or architecture …

… focus primarily on uncertainty in the … uncertainty or dynamic reservoir modelling as this lies
beyond the scope of the present study. We do acknowledge that quantification of uncertainty …

… , bundles, decisions under uncertainty, and matching. Under … of a moral hazard model
despite this unobservability.5 … These results also apply to more general discrete choice models …