IBM Research - Ireland Internship Project: Probabilistic preference model to account for incomparability - overview


Abstract: When comparing options that that are judged on several attributes (e.g. apartments or jobs) some comparisons are more difficult than others. For instance, it is difficult to choose between two apartments if one is well located but very expensive and the other is affordable but poorly located. When posed with such comparisons, that involve a significant trade-off across attributes, it is more likely that decision-makers will express incomparability or indifference. We propose to use a random utility model to represent this effect. In this model, attribute weights and marginal utility functions parameters are drawn from probability distributions whose parameters represent the DM's preferences.

The intern will contribute to the development of the model and test its accuracy on real experimental data. The internship provides an excellent opportunity to learn about decision analytics, and how to improve preference elicitation by taking behavioral results under consideration.

 

Required skills: Machine learning, Bayesian inference, Multi-attribute preference models, familiarity with Matlab.