Applications of Quantum Probability Theory to Human Causal Reasoning

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Understanding how people reason about causes and effects is a central topic in cognitive science and decision-making. In the past, much of the research on human causal reasoning has focused upon learning the relationship between a single cause and effect. However, most real world causal structures are more complex. For example, there can be multiple causes for a single effect where the causes themselves interact. When faced with complex problems such as this, how do people make causal judgments? There is evidence that people's judgments about complex causal systems often deviate from the normative rules of classic probability theory. This project will explore the application of quantum probability to human causal reasoning. Quantum probability theory is the noncommutative analog of classical probability theory. In particular, quantum probability can account for order effects, which are violations of the commutative rule of classic probability theory. Order effects occur in causal reasoning when the judged likelihood of an effect depends on the order in which the causes are processed. Previous work has demonstrated that the quantum probability approach can account for many paradoxical findings in judgment and decision research. The current project will further develop the quantum probability account by applying it to the area of causal reasoning.The broad and long term goals of this research program are (1) to increase the understanding of human judgments about complex causal systems and (2) to provide a new foundation for constructing models of human causal reasoning from the principles of quantum probability theory. Human causal reasoning is an important topic in numerous fields including developmental psychology, decision-making, and learning, thus the proposed research has the potential to advance knowledge across a range of disciplines. The proposed research also has the potential to benefit society. Many researchers are currently investigating the use of Bayesian causal networks to help decision-makers, such as intelligence analysts, process large amounts of data. While Bayesian decision tools have great potential, research is needed to understand and improve the causal judgments of analysts who interact with these tools. This research program addresses this topic through experiments designed to investigate complex causal reasoning and to improve causal judgments through learning. The proposed research also adds to the growing field of quantum cognition aimed at developing quantum probabilistic-dynamic systems for social and behavioral sciences.