动态系统累积变量判断中的关联启发式

关联启发式是指在累积变量判断中, 将存量与流量建立正相关关系, 认为存量变化特征与流量变化特征具有相似性而导致判断偏差的思维模式。在多特征交互及简单动态系统任务判断中, 关联启发式偏差以不同形式的S-F错误表现出来。作为一种内生性的、顽固的思维模式, 对于关联启发式的心理机制提出了客体偏差、特征替代、不充分调整等多种观点解释。影响关联启发式的个体因素和情境因素的研究结果不尽一致。由于关联启发式偏差直接影响人对复杂系统的可持续性决策和行动, 减少偏差策略的研究也已实现起步。本文系统总结和分析了关联启发式研究的发展脉络, 针对澄清关联启发式的发生机制、系统考察关联启发式的影响因素及其相互作用、丰富和深化关联启发式的研究范式、开发减少关联启发式的有效途径等方面, 提出了深化研究的方向和思路。

Correlation heuristic of accumulation judgment in dynamic systems

Correlation heuristic is a mental model in which judgment bias in understanding accumulation is aroused when people correlate accumulations and flows and assume that the patterns of accumulations and flows are similar. Correlation heuristic bias, termed as “stock-flow failure”, can be found in understanding interactive dynamic systems. Due to lack of appreciation of the basic principles of accumulation, individuals tend to use this heuristic, where they intuitively assume that the pattern of stock highly correlates with the pattern of inflow or net flow. They attempted to match the stock’s trajectory along with the inflow rate. Research shows that correlation heuristic bias is common in decision making for simple as well as multi-faceted decision making tasks. The heuristic has significant implications in business management decision making and is not conducive for people’s sustainable decision-making. Results of research on the individual factors such as object bias, attribution substitution, and insufficient adjustment and contextual factors that may influence correlation heuristics are inconsistent. Empirical studies that explore factors impacting correlation heuristics have emerged. Effort to explore ways of reducing such bias has started. This article systematically reviewed the correlation heuristic literature and recommended areas to be further explored in future studies, including clarifying its inner mechanism, digging into potential influencing factors and their interactions, enhancing research paradigms, and developing methods that can reduce correlation heuristic.