Subjective and a ective elements are well-known to in uence human decision making. This dissertation presents a theoretical and empirical framework on how human decision makers' subjective experience and a ective prediction in uence their choice behavior under uncer- tainty, frames and emotions. The framework extends and integrates existing theories of prospect theory (PT) and reinforcement learning (RL), drawing on a growing literature illuminating the role of a ect in decision making and the neural underpinnings of human decision behavior. The proposed A ective-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 subjec- tive value functions dynamically vary with the decision maker's a ective states in sequential decision making. Human decision-making experiments were conducted to infer how peo- ple adjust the parameters (i.e., shape and reference point) of their experienced-utility and predicted-utility functions in sequential decision-making situations involving incidental af- fective states (e.g., anger, fear, economic fear) and task-related con dence. I constructed a new model combining measures to evaluate risk preferences: behavioral choices, self- reported experience, self-reported predicted utility, self-reported con dence. The analysis results show how domain uncertainty, framing, and emotional state of decision makers in u- ence their subjective experience and discriminability, a ective prediction, optimal decisions and exploratory regulation. I found empirically that there were signi cant interaction e ects of framing and emotion on risk preferences: negative emotions made people more risk-averse in the 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 in uence con dence and ex- ploratory choice behavior. The theoretical analysis nicely supports empirical ndings from human experiments. The new model provides a theory that better explains and simulates human behavior under uncertainty, frames and emotions.
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