An Uncertainty Principle for Social Science Experiments

Ravi Kashyap

Julius Sumner Miller

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.

Donald R. Matthys

Andrei Yu. Khrennikov, Emmanuel E. Haven

In this paper we discuss the rationale why sub(super)‐additive probabilities in a psychological setting could be explained via the use of quantum probability interference. We propose to measure the complementarity of two variables: i) time of processing (by experiment participants) of (non‐moving) images and ii) the ability (by experiment participants) of recognizing deformations of (non‐moving) pictures. We argue in the paper why we can not find this complementarity using the Heisenberg Uncertainty Principle. The paper provides for the details on the experimental set up to test the complementarity.