2026-09-19T06:19:30Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/619292022-01-13T07:55:03Zcom_1721.1_7582com_1721.1_7581col_1721.1_131022
Rosalind W. Picard.
Ahn, Hyungil, 1976-
Massachusetts Institute of Technology. Dept. of Architecture. Program in Media Arts and Sciences.
Program in Media Arts and Sciences (Massachusetts Institute of Technology)
2011-03-24T20:28:16Z
2011-03-24T20:28:16Z
2010
2010
http://hdl.handle.net/1721.1/61929
707402630
Thesis (Ph. D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2010.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 207-212).
Subjective and affective elements are well-known to influence human decision making. This dissertation presents a theoretical and empirical framework on how human decision makers' subjective experience and affective prediction influence their choice behavior under uncertainty, frames and emotions. The framework extends and integrates existing theories of prospect theory (PT) and reinforcement learning (RL), drawing on a growing literature offering the role of affect in decision making and the neural underpinnings of human decision behavior. The proposed Affective-Cognitive (AC) model extends Prospect Theory (PT)- based subjective value functions to model human experienced-utility and predicted-utility functions. The AC model assumes that the shapes (or parameters) of these subjective value functions dynamically vary with the decision makers affective states in sequential decision making. Human decision-making experiments were conducted to empirically infer how people adjust the parameters (i.e., shape and reference point) of their experienced-utility and predicted-utility functions in sequential decision-making situations involving incidental affective states (e.g., anger, fear, economic fear) and task-related confidence. I constructed a new model combining measures to evaluate risk preferences: behavioral choices, selfreported experience self-reported experience, self-reported predicted utility, self-reported confidence. The analysis results show how domain uncertainty, framing, and emotion state of decision makers influence their subjective experience and discriminability, affective prediction, optimal decisions and exploratory regulation. I found empirically that there were significant interaction effects of framing and emotion on risk preferences: negative emotions made people more risk-averse in face of gains. When it comes to losses, anger made people more risk-averse and fear more risk seeking. I also characterized how gender and emotion influence confidence and exploratory choice behavior. The theoretical analysis nicely supports empirical findings from human experiments. The new model provides a theory that better explain and simulate human behavior under uncertainty, frames and emotions.
by Hyung-il Ahn.
Ph.D.
212 p.
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by
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http://dspace.mit.edu/handle/1721.1/7582
Architecture. Program in Media Arts and Sciences.
Modeling and analysis of affective influences on human experience, prediction, decision making, and behavior
Thesis
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<Abstract>Subjective and affective elements are well-known to influence human decision making. This dissertation presents a theoretical and empirical framework on how human decision makers' subjective experience and affective prediction influence their choice behavior under uncertainty, frames and emotions. The framework extends and integrates existing theories of prospect theory (PT) and reinforcement learning (RL), drawing on a growing literature offering the role of affect in decision making and the neural underpinnings of human decision behavior. The proposed Affective-Cognitive (AC) model extends Prospect Theory (PT)- based subjective value functions to model human experienced-utility and predicted-utility functions. The AC model assumes that the shapes (or parameters) of these subjective value functions dynamically vary with the decision makers affective states in sequential decision making. Human decision-making experiments were conducted to empirically infer how people adjust the parameters (i.e., shape and reference point) of their experienced-utility and predicted-utility functions in sequential decision-making situations involving incidental affective states (e.g., anger, fear, economic fear) and task-related confidence. I constructed a new model combining measures to evaluate risk preferences: behavioral choices, selfreported experience self-reported experience, self-reported predicted utility, self-reported confidence. The analysis results show how domain uncertainty, framing, and emotion state of decision makers influence their subjective experience and discriminability, affective prediction, optimal decisions and exploratory regulation. I found empirically that there were significant interaction effects of framing and emotion on risk preferences: negative emotions made people more risk-averse in face of gains. When it comes to losses, anger made people more risk-averse and fear more risk seeking. I also characterized how gender and emotion influence confidence and exploratory choice behavior. The theoretical analysis nicely supports empirical findings from human experiments. The new model provides a theory that better explain and simulate human behavior under uncertainty, frames and emotions.</Abstract>
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