We propose a novel approach to modelling time preferences, based on a cognitive shortcoming of human decision makers: the perception of future events becomes increasingly ?blurred? as the events are pushed further in time. We axiomatise a class of preference representations which can be...

A sequentially rationalizable choice function is a choice function which can be obtained by applying sequentially a fixed set of asymmetric binary relations (rationales). A Rational ShortlistMethod (RSM) is a choice function which is sequentially rationalizable by two rationales. These concepts...

We study a psychologically based foundation for choice errors. The decision maker applies a preference ranking after forming a 'consideration set' prior to choosing an alternative. Membership of the consideration set is determined both by the alternative specific salience and by the rationality...

If choices depend on the decision maker's mood, is the attempt to derive any consistency in choice doomed? In this paper we argue that, even with full unpredictability of mood, the way choices from a menu relate to choices from another menu exhibits some structure. We present two alternative...

We propose an extension of Tversky's lexicographic semiorder to a model of boundedly rational choice. We explore the connection with sequential rationalisability of choice, and we provide axiomatic characterisations of both models in terms of observable choice data.

We study two-stage choice procedures in which the decision maker first preselects the alternatives whose values according to a criterion pass a menu-dependent threshold, and then maximizes a second criterion to narrow the selection further. This framework overlaps with several existing models...

In Tversky's (1969) model of a lexicographic semiorder, preference is generated by the sequential application of numerical criteria, by declaring an alternative x better than an alternative y if the first criterion that distinguishes between x and y ranks x higher than y by an amount exceeding a...