Previous work proposes that heuristics, such as Take-The-Best, may succeed because of deliberate ignorance of covari-ance in their cue weight estimates as opposed to full-information models (logistic regression). Other studies find thatTake-The-Best performs particularly well compared to full-information models in high covariance as opposed to low co-variance environments. This poses the question of whether heuristics perform well when there is a mismatch between theircovariance prior and the covariance in the environment? We test this by gradually manipulating solely the level of covari-ance among cues. Indeed, Take-The-Best performs better as average covariance increases, while tallying, nave Bayes andlogistic regression worsen. Since both nave Bayes and tallying also disregard covariance but integrate across cues, thisindicates the competitive advantage of Take-The-Best stems from relying on a single cue when redundancy is high. Weextend previous work by Rieskamp and Dieckmann (2012) and imply a reinterpretation of past Take-The-Bests successes.

Travel behavior research shows that the disutility of waiting times looms larger than the disutility of in-vehicle times.However, little has been said about the plausibility of the assumption of compensatory behavior in the preferences forwaiting and traveling. Another open question is whether the variability in waiting and in-vehicle times affects transportdecisions in the same way. To answer these research questions, we conducted a lab experiment with university studentsfrom London, UK and Santiago, Chile. Participants were presented with 14 decisions scenarios that manipulated theaverage and the variability of waiting and in-vehicle times in two bus routes under the choice paradigms of decisions fromdescription and from experience. We found that participants did not compensate waiting and in-vehicle times; rather, theysought to minimize overall journey times. In addition, participants disliked more variability while waiting than traveling.Interestingly, both behaviors were only observed in the experiential choices.

A long-standing debate in decision making has been whether people rely on very little information for making choices, or weigh and add all available information. We propose a new method to determine whether a non-compensatory (Take-The- Best) or compensatory strategy (Logistic Regression) is more psychologically plausible: by looking at peoples active learning queries. This method goes beyond traditional model selection techniques as it reveals the information people choose to learn early on, which subsequently drives their decisions. We developed active learning algorithms for both Take-The-Best and Logistic Regression, and designed an active learning experiment to distinguish between these models. By letting both models and humans actively learn, we could compare their queries, and found that people follow a rank-based learning strategy in non-compensatory environments, but prefer more certainty-based queries in compensatory environments. We argue that active learning studies provide a promising new methodology to distinguish among decision models