2008
NIPS
NeurIPS 2008
A rational model of preference learning and choice prediction by children
Abstract
Young children demonstrate the ability to make inferences about the preferences of other agents based on their choices. However, there exists no overarching account of what children are doing when they learn about preferences or how they use that knowledge. We use a rational model of preference learning, drawing on ideas from economics and computer science, to explain the behavior of children in several recent experiments. Specifically, we show how a simple econometric model can be extended to capture two- to four-year-oldsâ use of statistical information in inferring preferences, and their generalization of these preferences.
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Interdisciplinary Bridge
— Interdisciplinary and Machine Learning
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Keyword Pioneer
— statistical inference
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio
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Trend Setter
— Imitation Learning
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Hot Topic Early Bird
— cognitive modeling
Authors
Topics
Machine Learning > Core Methods > Regression
Machine Learning > Core Methods > Representation Learning
Interdisciplinary > Cognitive Science > Cognitive Modeling
Machine Learning > Learning Types > Imitation Learning
Machine Learning > Learning Types > Preference Learning
Artificial Intelligence > Core AI > Decision Making