# RPS(1) Preferences

## TL;DR

This paper introduces a decision-making model based on adaptive learning with priors aligned with expected utility, revealing similarities to prospect theory and convergence to expected utility with longer learning periods.

## Contribution

It presents a novel adaptive learning model for preferences that aligns with expected utility in the long run and resembles prospect theory in finite cases.

## Key findings

- Preferences resemble Kahneman and Tversky's prospect theory
- Preferences converge to expected utility as learning period increases
- Model bridges prospect theory and expected utility in decision making

## Abstract

We consider a model for decision making based on an adaptive, k-period, learning process where the priors are selected according to Von Neumann-Morgenstern expected utility principle. A preference relation between two prospects is introduced, defined by the condition which prospect is selected more often. We show that the new preferences have similarities with the preferences obtained by Kahneman and Tversky (1979) in the context of the prospect theory. Additionally, we establish that in the limit of large learning period, the new preferences coincide with the expected utility principle.