Mathematical Biology Seminar: Jennifer Trueblood (Indiana University)

Order of information plays a crucial role in the process of updating beliefs across time. In fact, the presence of order effects makes a classical or Bayesian approach to inference difficult. As a result, the existing models of inference, such as the belief-adjustment model, merely provide an ad hoc explanation for these effects. We postulate a quantum inference model for order effects based on the axiomatic principles of quantum probability theory. The quantum inference model explains order effects by transforming a state vector with different sequences of operators for different orderings of information. We demonstrate this process by fitting the quantum model to data collected in a medical diagnostic task and a jury decision-making task. To further test the quantum inference model, new jury decision-making experiments are developed. The results of these experiments are used to compare the quantum model to the belief-adjustment model and suggest that the belief-adjustment model faces limitations whereas the quantum inference model does not.