A Double-Slit Experiment with Human Subjects

Decision theory postulates that human subjects have a well-defined “ranking” over different values of an attribute, irrespective of whether an observer is extracting information about this ranking. We study a sequence of “double-slit” experiments designed to perform repeated measurements of an attribute in a large pool of subjects using Amazon Mechanical Turk. Our findings contrast the prescriptions of decision theory in interesting ways. The response to an identical sequel measurement of the same attribute can be at significant variance with the initial measurement. Furthermore, the response to the sequel measurement depends on whether the initial measurement has taken place. In the absence of the initial measurement, the sequel measurement reveals additional variability, leading to a multimodal pattern which is largely absent if the first measurement has taken place.