Integrating Response Time and Attention Duration in Bayesian Preference Learning for Multiple Criteria Decision Aiding
This work addresses the challenge of accurately modeling human decision-making processes for decision aiding, though it is incremental as it builds on existing Bayesian preference learning frameworks by adding behavioral data.
The authors tackled the problem of improving preference learning in multiple criteria decision aiding by integrating behavioral cues like response time and attention duration, resulting in a model that better reconstructs decision-makers' preferences compared to traditional methods, as validated in a lab experiment with 30 subjects.
We introduce a multiple criteria Bayesian preference learning framework incorporating behavioral cues for decision aiding. The framework integrates pairwise comparisons, response time, and attention duration to deepen insights into decision-making processes. The approach employs an additive value function model and utilizes a Bayesian framework to derive the posterior distribution of potential ranking models by defining the likelihood of observed preference data and specifying a prior on the preference structure. This distribution highlights each model's ability to reconstruct Decision-Makers' holistic pairwise comparisons. By leveraging both response time as a proxy for cognitive effort and alternative discriminability as well as attention duration as an indicator of criterion importance, the proposed model surpasses traditional methods by uncovering richer behavioral patterns. We report the results of a laboratory experiment on mobile phone contract selection involving 30 real subjects using a dedicated application with time-, eye-, and mouse-tracking components. We validate the novel method's ability to reconstruct complete preferences. The detailed ablation studies reveal time- and attention-related behavioral patterns, confirming that integrating comprehensive data leads to developing models that better align with the DM's actual preferences.