Applications of Quantum Probability Theory to Human Causal Reasoning

理解人们如何推理因果关系是认知科学和决策学的中心课题。在过去，对人类因果推理的大部分研究都集中在学习单个因果之间的关系。然而，大多数现实世界的因果结构要复杂得多。例如，单个结果可能有多个原因，其中原因本身相互作用。当面对这样复杂的问题时，人们如何做出因果判断？有证据表明，人们对复杂因果系统的判断往往偏离经典概率论的规范规则。这个项目将探索量子概率在人类因果推理中的应用。量子概率论是经典概率论的非交换类比。特别是，量子概率可以解释顺序效应，这违反了经典概率论的交换规则。顺序效应发生在因果推理中，当判断结果的可能性取决于处理原因的顺序时。先前的工作已经证明，量子概率方法可以解释判断和决策研究中许多矛盾的发现。目前的项目将进一步发展量子概率帐户，将其应用于因果推理领域。本研究项目的长期目标是：(1)提高对人类对复杂因果系统判断的理解；(2)为基于量子概率论原理构建人类因果推理模型提供新的基础。人类因果推理是包括发展心理学、决策学和学习在内的许多领域的一个重要课题，因此拟议的研究有可能在一系列学科中推进知识。拟议的研究也有可能造福社会。许多研究人员目前正在研究使用贝叶斯因果网络来帮助决策者，如情报分析师，处理大量数据。虽然贝叶斯决策工具有很大的潜力，但需要研究来理解和改进与这些工具交互的分析师的因果判断。本研究计划通过实验来研究复杂的因果推理，并通过学习来提高因果判断。拟议的研究还增加了量子认知领域的发展，旨在为社会和行为科学发展量子概率动态系统。

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.