Discovering Context Effects from Raw Choice Data

Arjun Seshadri, Alex Peysakhovich, Johan Ugander
Proceedings of the 36th International Conference on Machine Learning, PMLR 97:5660-5669, 2019.

Abstract

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v97-seshadri19a, title = {Discovering Context Effects from Raw Choice Data}, author = {Seshadri, Arjun and Peysakhovich, Alex and Ugander, Johan}, booktitle = {Proceedings of the 36th International Conference on Machine Learning}, pages = {5660--5669}, year = {2019}, editor = {Chaudhuri, Kamalika and Salakhutdinov, Ruslan}, volume = {97}, series = {Proceedings of Machine Learning Research}, month = {09--15 Jun}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v97/seshadri19a/seshadri19a.pdf}, url = {https://proceedings.mlr.press/v97/seshadri19a.html}, abstract = {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.} }
Endnote
%0 Conference Paper %T Discovering Context Effects from Raw Choice Data %A Arjun Seshadri %A Alex Peysakhovich %A Johan Ugander %B Proceedings of the 36th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2019 %E Kamalika Chaudhuri %E Ruslan Salakhutdinov %F pmlr-v97-seshadri19a %I PMLR %P 5660--5669 %U https://proceedings.mlr.press/v97/seshadri19a.html %V 97 %X 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.
APA
Seshadri, A., Peysakhovich, A. & Ugander, J.. (2019). Discovering Context Effects from Raw Choice Data. Proceedings of the 36th International Conference on Machine Learning, in Proceedings of Machine Learning Research 97:5660-5669 Available from https://proceedings.mlr.press/v97/seshadri19a.html.

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