Choice Set Optimization Under Discrete Choice Models of Group Decisions

Kiran Tomlinson, Austin Benson
Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9514-9525, 2020.

Abstract

The way that people make choices or exhibit preferences can be strongly affected by the set of available alternatives, often called the choice set. Furthermore, there are usually heterogeneous preferences, either at an individual level within small groups or within sub-populations of large groups. Given the availability of choice data, there are now many models that capture this behavior in order to make effective predictions—however, there is little work in understanding how directly changing the choice set can be used to influence the preferences of a collection of decision-makers. Here, we use discrete choice modeling to develop an optimization framework of such interventions for several problems of group influence, namely maximizing agreement or disagreement and promoting a particular choice. We show that these problems are NP-hard in general, but imposing restrictions reveals a fundamental boundary: promoting a choice can be easier than encouraging consensus or sowing discord. We design approximation algorithms for the hard problems and show that they work well on real-world choice data.

Cite this Paper

BibTeX
@InProceedings{pmlr-v119-tomlinson20a, title = {Choice Set Optimization Under Discrete Choice Models of Group Decisions}, author = {Tomlinson, Kiran and Benson, Austin}, booktitle = {Proceedings of the 37th International Conference on Machine Learning}, pages = {9514--9525}, year = {2020}, editor = {III, Hal Daumé and Singh, Aarti}, volume = {119}, series = {Proceedings of Machine Learning Research}, month = {13--18 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v119/tomlinson20a/tomlinson20a.pdf}, url = {https://proceedings.mlr.press/v119/tomlinson20a.html}, abstract = {The way that people make choices or exhibit preferences can be strongly affected by the set of available alternatives, often called the choice set. Furthermore, there are usually heterogeneous preferences, either at an individual level within small groups or within sub-populations of large groups. Given the availability of choice data, there are now many models that capture this behavior in order to make effective predictions—however, there is little work in understanding how directly changing the choice set can be used to influence the preferences of a collection of decision-makers. Here, we use discrete choice modeling to develop an optimization framework of such interventions for several problems of group influence, namely maximizing agreement or disagreement and promoting a particular choice. We show that these problems are NP-hard in general, but imposing restrictions reveals a fundamental boundary: promoting a choice can be easier than encouraging consensus or sowing discord. We design approximation algorithms for the hard problems and show that they work well on real-world choice data.} }
Endnote
%0 Conference Paper %T Choice Set Optimization Under Discrete Choice Models of Group Decisions %A Kiran Tomlinson %A Austin Benson %B Proceedings of the 37th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2020 %E Hal Daumé III %E Aarti Singh %F pmlr-v119-tomlinson20a %I PMLR %P 9514--9525 %U https://proceedings.mlr.press/v119/tomlinson20a.html %V 119 %X The way that people make choices or exhibit preferences can be strongly affected by the set of available alternatives, often called the choice set. Furthermore, there are usually heterogeneous preferences, either at an individual level within small groups or within sub-populations of large groups. Given the availability of choice data, there are now many models that capture this behavior in order to make effective predictions—however, there is little work in understanding how directly changing the choice set can be used to influence the preferences of a collection of decision-makers. Here, we use discrete choice modeling to develop an optimization framework of such interventions for several problems of group influence, namely maximizing agreement or disagreement and promoting a particular choice. We show that these problems are NP-hard in general, but imposing restrictions reveals a fundamental boundary: promoting a choice can be easier than encouraging consensus or sowing discord. We design approximation algorithms for the hard problems and show that they work well on real-world choice data.
APA
Tomlinson, K. & Benson, A.. (2020). Choice Set Optimization Under Discrete Choice Models of Group Decisions. Proceedings of the 37th International Conference on Machine Learning, in Proceedings of Machine Learning Research 119:9514-9525 Available from https://proceedings.mlr.press/v119/tomlinson20a.html.

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