Template-Type: ReDIF-Paper 1.0
Author-Name: Peters, M.
Author-Name-Last: Peters
Author-Name-First: Markus
Author-Name: Ketter, W.
Author-Name-Last: Ketter
Author-Name-First: Wolfgang
Title: Towards autonomous decision-making: A probabilistic model for learning multi-user preferences
Abstract: Information systems have revolutionized the provisioning of decision-relevant information, and decision support tools have improved human decisions in many domains. Autonomous decision- making, on the other hand, remains hampered by systems’ inability to faithfully capture human preferences. We present a computational preference model that learns unobtrusively from lim- ited 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 informative- ness of its certainty estimates. Our work takes an important step toward systems that act autonomously on their users’ behalf.
Creation-Date: 2013-05-22
File-URL: https://repub.eur.nl/pub/40144/ERS-2013-007-LIS.pdf
File-Format: application/pdf
Series: RePEc:ems:eureri
Number: ERS-2013-007-LIS
Classification-JEL: C63, L15, O32
Keywords: assistive technologies, autonomous decision-making, multi-task learning, preferences, software agents
Handle: RePEc:ems:eureri:40144