Prior research in judgment and decision making (JDM) has investigated the effect of problem framing on human preferences. Furthermore, research in JDM documented the absence of such reversal of preferences when making decisions from experience. However, little is known about the effect of context on preferences under the combined influence of problem framing and problem format. Also, little is known about how cognitive models would account for human choices in different problem frames and types (general/specific) in the experience format. One of the primary objectives of this research is to investigate the presence of preference reversals under the influence of problem framing (gain/loss), problem format (experience/description), and problem type (general/specific). Another objective of this research is to develop cognitive models to account for human choices across different problem frames and types in the experience format. A total of 320 participants from India were randomly assigned to one of eight between-subjects conditions that differed in problem frame, format, and type. Results revealed preference reversals in the description condition; however, they were absent in the experience condition. Moreover, preference reversals were less pronounced in the general problem framing compared to the specific problem framing. Furthermore, specific problems influenced risk-seeking behavior among participants. We developed cognitive and heuristics models using instance-based learning theory and natural mean heuristic. Results reveal models’ dependency on recent and frequent observations during information sampling. These experience-based cognitive models could help build artificial intelligence models with fewer preference reversals.

中文翻译：

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生死决定和 COVID-19：调查和建模框架、经验和背景对亚洲疾病问题偏好逆转的影响


先前的判断与决策（JDM）研究调查了问题框架对人类偏好的影响。此外，JDM 的研究表明，根据经验做出决策时不存在这种偏好逆转。然而，在问题框架和问题格式的共同影响下，背景对偏好的影响却知之甚少。此外，人们对认知模型如何解释人类在不同问题框架和经验格式（一般/特定）类型（一般/特定）中的选择知之甚少。本研究的主要目标之一是调查在问题框架（增益/损失）、问题格式（经验/描述）和问题类型（一般/特定）的影响下偏好逆转的存在。这项研究的另一个目标是开发认知模型，以解释人类在体验格式中不同问题框架和类型中的选择。来自印度的总共 320 名参与者被随机分配到八个受试者间条件之一，这些条件在问题框架、格式和类型上有所不同。结果揭示了描述条件下的偏好逆转；然而，他们在体验条件下缺席。此外，与特定问题框架相比，一般问题框架中的偏好逆转不太明显。此外，具体问题影响了参与者的风险寻求行为。我们使用基于实例的学习理论和自然平均启发式开发了认知和启发式模型。结果揭示了模型对信息采样过程中最近和频繁观察的依赖。这些基于经验的认知模型可以帮助构建偏好逆转较少的人工智能模型。