Predict choice: a comparison of 21 mathematical model

Schulz, E; Speekenbrink, M; Shanks, DR

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Predict choice: a comparison of 21 mathematical model

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https://ericschulz.github.io/publications/Schulz2014predict.pdf
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Schulz, E., Speekenbrink, M., & Shanks, D. (2014). Predict choice: a comparison of 21 mathematical model. In 36th Annual Meeting of the Cognitive Science Society (CogSci 2014): Cognitive Science Meets Artificial Intelligence: Human and Artificial Agents in Interactive Contexts (pp. 2889-2894). Austin, TX, USA: Cognitive Science Society.

Cite as: https://hdl.handle.net/21.11116/0000-0006-B450-4

Abstract

How should we choose a model that predicts human choices? Two important factors in this choice are a model's predictive power and a model's fexibility. In this paper, we compare these aspects of models in a large set of models applied to an experiment in which participants chose between brands of fictitious chocolate bars and a quasi-experiment predicting movies' gross revenue. We show that there is a trade-o ff between flexibility and predictive power, but that this trade-o ff appears to lie more towards the "flexible" side than what was previously thought.