Modeling Subjective Experience-Based Learning under Uncertainty and Frames
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Picard_Modeling subjective.pdf
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813.89 KB
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729395a7e273a2bb15ecbd5b23c03a5c
Author(s)
Ahn, Hyung-il
Picard, Rosalind W.
Date Issued
July 2014
Journal
Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence
Publisher
AAAI
Citation
Ahn, Hyung-il, and Rosalind W. Picard. “Modeling Subjective Experience-Based Learning under Uncertainty and Frames.” Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence 2014: 329–335.
Version
Author's final manuscript
Abstract
In this paper we computationally examine how subjective experience may help or harm the decision maker's learning under uncertain outcomes, frames and their interactions. To model subjective experience, we propose the "experienced-utility function" based on a prospect theory (PT)-based parameterized subjective value function. Our analysis and simulations of two-armed bandit tasks present that the task domain (underlying outcome distributions) and framing (reference point selection) influence experienced utilities and in turn, the "subjective discriminability" of choices under uncertainty. Experiments demonstrate that subjective discriminability improves on objective discriminability by the use of the experienced-utility function with appropriate framing for a given task domain, and that bigger subjective discriminability leads to more optimal decisions in learning under uncertainty.
MIT Department
Massachusetts Institute of Technology. Media Laboratory
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
http://dl.acm.org/citation.cfm?id=2893873.2893925
