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Journal of Mathematical Psychology

Empirical comparison of Markov and quantum models of decision making

Jerome R. Busemeyer, Zheng Wang, Ariane Lambert‐Mogiliansky

Published May 15, 2009• 186 citations
Publisher
DOI
10.1016/j.jmp.2009.03.002

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Markov and quantum information processing models are compared with respect to their capability of explaining two different puzzling findings from empirical research on human inference and decision making. Both findings involve a task that requires making an inference about one of two possible uncertain states, followed by decision about two possible courses of action. Two conditions are compared: under one condition, the decisions are obtained after discovering or measuring the uncertain state; under another condition, choices are obtained before resolving the uncertainty so that the state remains unknown or unmeasured. Systematic departures from the Markov model are observed, and these deviations are explained as interference effects using the quantum model. Quantum computing and information theory (Neilsen & Chuang, 2000) provides exciting new possibilities for computer science. But what importance does this new theory have for cognitive science? This is a question that is beginning to be asked by an increasing number researchers from a variety of fields including language (Gabora &
Markov processes, such as random walk models, have been successfully used by cognitive and neural scientists to model human choice behavior and decision time for over 50 years. Recently, quantum walk models have been introduced as an alternative way to model the dynamics of human choice and confidence across time. Empirical evidence points to the need for both types of processes, and open system models provide a way to incorporate them both into a single process. However, some of the constraints required by open system models present challenges for achieving this goal. The purpose of this article is to address these challenges and formulate open system models that have good potential to make important advancements in cognitive science.