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 the existing theories, such as 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 employs Prospect Theory (PT)-based parameterized subjective value functions to model human experienced-utility and predicted-utility functions. We assume that the shapes (or parameters) of these subjective value functions dynamically vary with the decision-maker’s affective states in sequential decision making. Ahn has conducted human decision-making experiments to empirically infer how people adjust the parameters (i.e., risk attitude 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. Theoretically he has has proved that the reference point (decision framing) selection and emotion state of decision makers may affect their risk attitudes and exploratory regulation (i.e., trade-offs between exploration and exploitation). The theoretical analysis nicely supports empirical findings from human experiments.
Rosalind W. PicardJennifer LernerAndrew BartoKelly Hewett