Accidents and Decision Making under Uncertainty: A Comparison of Four Models

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.