A Probabilistic Multi-User Preference Model

While information systems have revolutionized the provisioning of decision-relevant infor-mation and have improved human decisions in many domains, autonomous decision-making remains hampered by systems' inability to faithfully capture human preferences. We present a computational preference model that learns from limited data by pooling observations across like-minded users. Our model is capable of quantifying the certainty of its own predictions as input to autonomous decision-making tasks, and it infers probabilistic segments based on user choices in the process. We evaluate our model on real-world preference data collected on a commercial crowdsourcing platform, and we find that it outperforms both individual and population-level estimates in terms of predictive accuracy and the informativeness of its certainty estimates.