How Suitable Is Your Naturalistic Dataset for Theory-based User Modeling?

Abstract

Theory-based, or "white-box,"models come with a major benefit that makes them appealing for deployment in user modeling: their parameters are interpretable. However, most theory-based models have been developed in controlled settings, in which researchers determine the experimental design. In contrast, real-world application of these models demands setups that are beyond developer control. In non-experimental, naturalistic settings, the tasks with which users are presented may be very limited, and it is not clear that model parameters can be reliably inferred. This paper describes a technique for assessing whether a naturalistic dataset is suitable for use with a theory-based model. The proposed parameter recovery technique can warn against possible over-confidence in inferred model parameters. This technique also can be used to study conditions under which parameter inference is feasible. The method is demonstrated for two models of decision-making under risk with naturalistic data from a turn-based game.