One of the most important challenges in decision theory has been how to reconcile the normative expectations from Bayesian theory with the apparent fallacies that are common in probabilistic reasoning. Recently, Bayesian models have been driven by the insight that apparent fallacies are due to sampling errors or biases in estimating (Bayesian) probabilities. An alternative way to explain apparent fallacies is by invoking different probability rules, specifically the probability rules from quantum theory. Arguably, quantum cognitive models offer a more unified explanation for a large body of findings, problematic from a baseline classical perspective. This work addresses two major corresponding theoretical challenges: first, a framework is needed which incorporates both Bayesian and quantum influences, recognizing the fact that there is evidence for both in human behavior. Second, there is empirical evidence which goes beyond any current Bayesian and quantum model. We develop a model for probabilistic reasoning, seamlessly integrating both Bayesian and quantum models of reasoning and augmented by a sequential sampling process, which maps subjective probabilistic estimates to observable responses. Our model, called the Quantum Sequential Sampler, is compared to the currently leading Bayesian model, the Bayesian Sampler (J. Zhu et al., 2020) using a new experiment, producing one of the largest data sets in probabilistic reasoning to this day. The Quantum Sequential Sampler embodies several new components, which we argue offer a more theoretically accurate approach to probabilistic reasoning. Moreover, our empirical tests revealed a new, surprising systematic overestimation of probabilities. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

中文翻译：

---

弥合主观概率和概率判断之间的差距：量子顺序采样器。


决策理论中最重要的挑战之一是如何协调贝叶斯理论的规范期望与概率推理中常见的明显谬误。最近，贝叶斯模型的发展是基于这样的认识：明显的谬误是由于采样误差或估计（贝叶斯）概率的偏差造成的。解释明显谬误的另一种方法是调用不同的概率规则，特别是量子理论中的概率规则。可以说，量子认知模型为大量发现提供了更统一的解释，从基线经典角度来看，这些发现存在问题。这项工作解决了两个主要的相应理论挑战：首先，需要一个结合贝叶斯和量子影响的框架，认识到人类行为中存在这两种影响的证据。其次，有超越任何当前贝叶斯和量子模型的经验证据。我们开发了一个概率推理模型，无缝集成贝叶斯推理和量子推理模型，并通过顺序采样过程进行增强，将主观概率估计映射到可观察的响应。我们的模型称为量子顺序采样器，使用新实验与当前领先的贝叶斯模型贝叶斯采样器（J. Zhu 等人，2020）进行比较，产生了迄今为止概率推理中最大的数据集之一。量子顺序采样器包含几个新组件，我们认为它们为概率推理提供了理论上更准确的方法。此外，我们的实证测试揭示了一种新的、令人惊讶的系统性高估概率。 （PsycInfo 数据库记录 (c) 2024 APA，保留所有权利）。