Information systems have revolutionized the provisioning of decision-relevant information, and decision support tools have improved human decisions in many domains. Autonomous decisionmaking, on the other hand, remains hampered by systems’ inability to faithfully capture human preferences. We present a computational preference model that learns unobtrusively from limited data by pooling observations across like-minded users. Our model quantifies 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. Our work takes an important step toward systems that act autonomously on their users’ behalf.

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Keywords Assistive technologies, Autonomous decision-making, Multi-task learning, Preferences, Software agents
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Ketter, W, & Peters, M. (2013). Towards Autonomous Decision-Making. Retrieved from