Discovering Context Effects from Raw Choice Data
This work addresses the issue of context effects in preference learning for applications relying on stable utility assumptions, though it is incremental as it builds on existing models.
The paper tackles the problem of discovering choice set effects from raw choice data, introducing the context dependent random utility model (CDM) as an extension of the Multinomial Logit model, which is shown to be interpretable and applicable to real and simulated data for exploratory analysis.
Many applications in preference learning assume that decisions come from the maximization of a stable utility function. Yet a large experimental literature shows that individual choices and judgements can be affected by "irrelevant" aspects of the context in which they are made. An important class of such contexts is the composition of the choice set. In this work, our goal is to discover such choice set effects from raw choice data. We introduce an extension of the Multinomial Logit (MNL) model, called the context dependent random utility model (CDM), which allows for a particular class of choice set effects. We show that the CDM can be thought of as a second-order approximation to a general choice system, can be inferred optimally using maximum likelihood and, importantly, is easily interpretable. We apply the CDM to both real and simulated choice data to perform principled exploratory analyses for the presence of choice set effects.