A Generalized Probability Framework to Model Economic Agents' Decisions Under Uncertainty

TL;DR

This paper explores how quantum probability models can better represent human decision-making under uncertainty in economics and finance, especially where classical probability fails.

Contribution

It advances the application of quantum probability formalism to model decision making, explaining its effectiveness beyond microscopic quantum phenomena.

Findings

Quantum probability models fit experimental decision data better than classical models.

Application of Hilbert space formalism captures non-classical decision behaviors.

Provides a theoretical foundation for using quantum mechanics in behavioral economics.

Abstract

The applications of techniques from statistical (and classical) mechanics to model interesting problems in economics and finance has produced valuable results. The principal movement which has steered this research direction is known under the name of `econophysics'. In this paper, we illustrate and advance some of the findings that have been obtained by applying the mathematical formalism of quantum mechanics to model human decision making under `uncertainty' in behavioral economics and finance. Starting from Ellsberg's seminal article, decision making situations have been experimentally verified where the application of Kolmogorovian probability in the formulation of expected utility is problematic. Those probability measures which by necessity must situate themselves in Hilbert space (such as `quantum probability') enable a faithful representation of experimental data. We thus provide an explanation for the effectiveness of the mathematical framework of quantum mechanics in the modeling of human decision making. We want to be explicit though that we are not claiming that decision making has microscopic quantum mechanical features.