Barkan R, Zohar D, Erev I
Technion-Israel Institute of Technology, Haifa, 32000, Israel
Organ Behav Hum Decis Process. 1998 May;74(2):118-44. doi: 10.1006/obhd.1998.2772.
Heinrich's (1931) classical study implies that most industrial accidents can be characterized as a probabilistic result of human error. The present research quantifies Heinrich's observation and compares four descriptive models of decision making in the abstracted setting. The suggested quantification utilizes signal detection theory (Green & Swets, 1966). It shows that Heinrich's observation can be described as a probabilistic signal detection task. In a controlled experiment, 90 decision makers participated in 600 trials of six safety games. Each safety game was a numerical example of the probabilistic SDT abstraction of Heinrich's proposition. Three games were designed under a frame of gain to represent perception of safe choice as costless, while the other three were designed under a frame of loss to represent perception of safe choice as costly. Probabilistic penalty for Miss was given at three different levels (1, .5, .1). The results showed that decisions tended initially to be risky and that experience led to safer behavior. As the probability of being penalized was lowered decisions became riskier and the learning process was impaired. The results support the cutoff reinforcement learning model suggested by Erev et al. (1995). The hill-climbing learning model (Busemeyer & Myung, 1992) was partially supported. Theoretical and practical implications are discussed. Copyright 1998 Academic Press.
海因里希（1931年）的经典研究表明，大多数工业事故可被视为人类错误导致的概率性结果。本研究对海因里希的观察进行了量化，并在抽象情境中比较了四种决策描述模型。所建议的量化方法运用了信号检测理论（格林和斯韦茨，1966年）。结果表明，海因里希的观察可被描述为一项概率性信号检测任务。在一项对照实验中，90名决策者参与了六项安全游戏的600次试验。每项安全游戏都是海因里希命题概率性信号检测理论抽象的一个数值示例。三项游戏在收益框架下设计，将安全选择视为无成本，而另外三项在损失框架下设计，将安全选择视为有成本。漏报的概率性惩罚设定为三个不同水平（1、0.5、0.1）。结果显示，决策最初往往具有风险性，而经验会导致更安全的行为。随着受罚概率降低，决策变得更具风险性，学习过程也受到损害。研究结果支持了埃雷夫等人（1995年）提出的截止强化学习模型。爬山学习模型（布塞迈耶和明，1992年）得到部分支持。本文还讨论了理论和实际意义。版权所有1998年学术出版社。
