Comonotonic Independence: The Critical Test between Classical and Rank-Dependent Utility Theories

This article compares classical expected utility (EU) with the more general rank-dependent utility (RDU) models. The difference between the independence condition for preferences of EU and its comonotonic generalization in RDU provides the exact demarcation between EU and rank-dependent models. Other axiomatic differences are not essential. An experimental design is described that tests this difference between independence and comonotonic independence in its most basic form and is robust against violations of other assumptions that may confound the results, in particular the reduction principle and transitivity. It is well known that in the classical counterexamples to EU, comonotonic independence performs better than full-force independence. For our more general choice pairs, however, we find that comonotonic independence does not perform better. This is contrary to our prior expectation and suggests that rank-dependent models, in full generality, do not provide a descriptive improvement over EU. For rank-dependent models to have a future, submodels and choice situations need to be identified for which rank-dependence does contribute descriptively

Easy first steps and their implication to the use of a mouse-based and a script-based strategy

The present study evaluated the convention to design training environments by giving access to easy strategies first and progressing to more difficult and efficient strategies thereafter. An experiment was conducted focusing on training in a simplified editing task. This task could be performed with an easy (mouse-based) strategy as well as with a more efficient (script-based) strategy. Two learning environments were compared, based on the order of the introduction of the following 2 strategies to participants: an easy-first program and a difficult-first program. The results highlight 2 interesting patterns. First, initial training in an easy strategy impaired the acquisition of a more efficient strategy. Second, learning the easy strategy first reduced between-subjects variability. It helped poor performers but resulted in a lower proportion of high-level performers

Degradation in spatial knowledge acquisition when using automatic navigation systems

Over-reliance on automated navigation systems may cause users to be "mindless" of the environment and not develop the spatial knowledge that maybe required when automation fails. This research focused on the potential degradation in spatial knowledge acquisition due to the reliance on automatic wayfinding systems. In addition, the impact of "keeping the user in the loop" strategies on spatial knowledge was examined. Participants performed wayfindings tasks in a virtual building with continuous or by-request position indication, in addition to responding to occasional orientation quizzes. Findings indicate that having position indication by request and orientation quizzes resulted in better acquired spatial knowledge. The findings are discussed in terms of keeping the user actively investing mental effort in the wayfinding task as a strategy to reduce the possible negative impact of automated navigation systems

Foregone with the Wind: Indirect Payoff Information and its Implications for Choice

Foregone payoff, Adaptive behavior, Reinforcement learning, Fictitious Play, Directional learning, Bandit problems,