Predicting Preference Reversals via Gaussian Process Uncertainty Aversion


Rikiya Takahashi, Tetsuro Morimura ;
Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, PMLR 38:958-967, 2015.


Modeling of a product or service’s attractiveness as a function of its own attributes (e.g., price and quality) is one of the foundations in econometric forecasts, which have been provided with an assumption that each human rationally has a consistent preference order among his choice decisions. Yet the preference orders by real humans become irrationally reversed, when the choice set of available options is manipulated. In order to accurately predict choice decisions involving preference reversals, which existing econometric methods have failed to incorporate, the authors introduce a new cognitive choice model whose parameters are efficiently fitted with a global convex optimization algorithm. The proposed model captures each human as a Bayesian decision maker facing a mental conflict between objective evaluation samples and a subjective prior, where the underlying objective evaluation function is rationally independent from contexts while the subjective prior is irrationally determined by each choice set. As the key idea to analytically handle the irrationality and to yield the convex optimization, the Bayesian decision mechanism is implemented as a closed-form Gaussian process regression using similarities among the available options in each context. By explaining the irrational decisions as a consequence of averting uncertainty, the proposed model outperformed the existing econometric models in predicting the irrational choice decisions recorded in real-world datasets.

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