Introduction to Quantum Probability for Social and Behavioral Scientists

Jerome R. Busemeyer, ZHENG WANG

Lee Rudolph

This chapter has two related purposes: to generate interest in a new and fascinating approach to understanding behavioral measures based on quantum probability principles, and to introduce and provide a tutorial of the basic ideas in a manner that is interesting and easy for social and behavioral scientists to understand.

M. Fannes

Andrew Gelman, Michael Betancourt

We are sympathetic to the general ideas presented in the article by Pothos & Busemeyer (P&B): Heisenberg's uncertainty principle seems naturally relevant in the social and behavioral sciences, in which measurements can affect the people being studied. We propose that the best approach for developing quantum probability models in the social and behavioral sciences is not by directly using the complex probability-amplitude formulation proposed in the article, but rather, more generally, to consider marginal probabilities that need not be averages over conditionals.

Luigi Accardi

CHANGKUI LI

Quantum probability theory, originally developed in the context of quantum mechanics, has increasingly been applied to social sciences to explain decision-making, cognition, and collective behavior. Unlike classical probability models, which assume pre-existing and well-defined preferences, quantum models incorporate superposition, interference, and non-commutativity to capture the contextual dependence and measurement effects observed in human decisions. This paper systematically explores the theoretical foundations of quantum probability in social sciences, discusses model construction techniques, and reviews empirical findings supporting its application. By examining case studies in quantum cognition, quantum game theory, and quantum voting, we illustrate how quantum approaches provide alternative explanations for phenomena such as order effects, measurement-induced attitude shifts, and collective polarization. The paper also discusses the methodological challenges and limitations of quantum social science, emphasizing the need for rigorous experimental validation and interdisciplinary collaboration. Finally, we highlight the potential for integrating quantum probability models with emerging technologies such as quantum computing and artificial intelligence, opening new avenues for social science research and policy applications.