A Computational Model for Constructing Preferences for Multiple Choice Options

Abstract

When choosing between multiple alternatives, people usually do not have ready-made preferences in their mind but rather construct them on the go. The 2N-ary Choice Tree Model (Wollschlaeger & Diederich, 2012) proposes a preference construction process for N choice options from description, which is based on attribute weights, differences between attribute values, and noise. It is able to produce similarity, attraction, and compromise effects, which have become a benchmark for multi-alternative choice models, but also several other context and reference point effects. Here, we present a new and mathematically tractable version of the model – the Simple Choice Tree Model – which also explains the above mentioned effects and additionally accounts for the positive correlation between the attraction and compromise effect, and the negative correlation between these two and the similarity effect as observed by Berkowitsch, Scheibehenne, and Rieskamp (2014).


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