Towards autonomous decision-making: A probabilistic model for learning multi-user preferences
AbstractInformation 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 systemsâ€™ 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 usersâ€™ behalf.
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Bibliographic InfoPaper provided by Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam. in its series Research Paper with number ERS-2013-007-LIS.
Date of creation: 22 May 2013
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preferences; software agents; assistive technologies; multi-task learning; autonomous decision-making;
This paper has been announced in the following NEP Reports:
- NEP-ALL-2013-06-04 (All new papers)
- NEP-CDM-2013-06-04 (Collective Decision-Making)
- NEP-CMP-2013-06-04 (Computational Economics)
- NEP-HRM-2013-06-04 (Human Capital & Human Resource Management)
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