More Causes Less Effect: Destructive Interference in Decision Making

TL;DR

This paper demonstrates destructive interference effects in customer probability estimates, showing how quantum probability models can better explain complex decision-making phenomena involving multiple causes.

Contribution

It introduces the concept of destructive interference in consumer decision-making and applies quantum probability to model these effects, advancing understanding of cognitive processes.

Findings

Destructive interference observed in customer probability estimates.

Quantum models effectively explain non-classical response patterns.

Classical probability cannot account for the observed effects.

Abstract

We present a new experiment demonstrating destructive interference in customers' estimates of conditional probabilities of product failure. We take the perspective of a manufacturer of consumer products, and consider two situations of cause and effect. Whereas individually the effect of the causes is similar, it is observed that when combined, the two causes produce the opposite effect. Such negative interference of two or more reasons may be exploited for better modeling the cognitive processes taking place in the customers' mind. Doing so can enhance the likelihood that a manufacturer will be able to design a better product, or a feature within it. Quantum probability has been used to explain some commonly observed deviations such as question order and response replicability effects, as well as in explaining paradoxes such as violations of the sure-thing principle, and Machina and Ellsberg paradoxes. In this work, we present results from a survey conducted regarding the effect of multiple observed symptoms on the drivability of a vehicle. We demonstrate that the set of responses cannot be explained using classical probability, but quantum formulation easily models it, as it allows for both positive and negative "interference" between events. Since quantum formulism also accounts for classical probability's predictions, it serves as a richer paradigm for modeling decision making behavior in engineering design and behavioral economics.