Modeling Subjective Experience-based Learning under Uncertainty and Frames

In this paper we computationally examine how subjec-tive experience may help or harm the decision maker’s learning under uncertain outcomes, frames and their in-teractions. To model subjective experience, we propose the “experienced-utility function ” based on a prospect theory (PT)-based parameterized subjective value func-tion. 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 “subjec-tive discriminability ” of choices under uncertainty. Ex-periments 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 dis-criminability leads to more optimal decisions in learn-ing under uncertainty