Modeling Risky Choices in Unknown Environments

Ville Tanskanen, Chang Rajani, Homayun Afrabandpey, Aini Putkonen, Aurélien Nioche, Arto Klami
Proceedings of The 13th Asian Conference on Machine Learning, PMLR 157:1081-1096, 2021.

Abstract

Decision-theoretic models explain human behavior in choice problems involving uncertainty, in terms of individual tendencies such as risk aversion. However, many classical models of risk require knowing the distribution of possible outcomes (rewards) for all options, limiting their applicability outside of controlled experiments. We study the task of learning such models in contexts where the modeler does not know the distributions but instead can only observe the choices and their outcomes for a user familiar with the decision problems, for example a skilled player playing a digital game. We propose a framework combining two separate components, one for modeling the unknown decision-making environment and another for the risk behavior. By using environment models capable of learning distributions we are able to infer classical models of decision-making under risk from observations of the user’s choices and outcomes alone, and we also demonstrate alternative models for predictive purposes. We validate the approach on artificial data and demonstrate a practical use case in modeling risk attitudes of professional esports teams.

Cite this Paper

BibTeX
@InProceedings{pmlr-v157-tanskanen21a, title = {Modeling Risky Choices in Unknown Environments}, author = {Tanskanen, Ville and Rajani, Chang and Afrabandpey, Homayun and Putkonen, Aini and Nioche, Aur\'elien and Klami, Arto}, booktitle = {Proceedings of The 13th Asian Conference on Machine Learning}, pages = {1081--1096}, year = {2021}, editor = {Balasubramanian, Vineeth N. and Tsang, Ivor}, volume = {157}, series = {Proceedings of Machine Learning Research}, month = {17--19 Nov}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v157/tanskanen21a/tanskanen21a.pdf}, url = {https://proceedings.mlr.press/v157/tanskanen21a.html}, abstract = {Decision-theoretic models explain human behavior in choice problems involving uncertainty, in terms of individual tendencies such as risk aversion. However, many classical models of risk require knowing the distribution of possible outcomes (rewards) for all options, limiting their applicability outside of controlled experiments. We study the task of learning such models in contexts where the modeler does not know the distributions but instead can only observe the choices and their outcomes for a user familiar with the decision problems, for example a skilled player playing a digital game. We propose a framework combining two separate components, one for modeling the unknown decision-making environment and another for the risk behavior. By using environment models capable of learning distributions we are able to infer classical models of decision-making under risk from observations of the user’s choices and outcomes alone, and we also demonstrate alternative models for predictive purposes. We validate the approach on artificial data and demonstrate a practical use case in modeling risk attitudes of professional esports teams.} }
Endnote
%0 Conference Paper %T Modeling Risky Choices in Unknown Environments %A Ville Tanskanen %A Chang Rajani %A Homayun Afrabandpey %A Aini Putkonen %A Aurélien Nioche %A Arto Klami %B Proceedings of The 13th Asian Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2021 %E Vineeth N. Balasubramanian %E Ivor Tsang %F pmlr-v157-tanskanen21a %I PMLR %P 1081--1096 %U https://proceedings.mlr.press/v157/tanskanen21a.html %V 157 %X Decision-theoretic models explain human behavior in choice problems involving uncertainty, in terms of individual tendencies such as risk aversion. However, many classical models of risk require knowing the distribution of possible outcomes (rewards) for all options, limiting their applicability outside of controlled experiments. We study the task of learning such models in contexts where the modeler does not know the distributions but instead can only observe the choices and their outcomes for a user familiar with the decision problems, for example a skilled player playing a digital game. We propose a framework combining two separate components, one for modeling the unknown decision-making environment and another for the risk behavior. By using environment models capable of learning distributions we are able to infer classical models of decision-making under risk from observations of the user’s choices and outcomes alone, and we also demonstrate alternative models for predictive purposes. We validate the approach on artificial data and demonstrate a practical use case in modeling risk attitudes of professional esports teams.
APA
Tanskanen, V., Rajani, C., Afrabandpey, H., Putkonen, A., Nioche, A. & Klami, A.. (2021). Modeling Risky Choices in Unknown Environments. Proceedings of The 13th Asian Conference on Machine Learning, in Proceedings of Machine Learning Research 157:1081-1096 Available from https://proceedings.mlr.press/v157/tanskanen21a.html.

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