Laws of Accident Causation (Rune’s rules)

This paper argues that the influence of a number of important risk factors on road accidents can be modelled statistically in terms of a few scientific laws, which represent more or less invariant causal relationships.

It is postulated that if humans were perfectly rational, and always in perfect control of hazards that are subject to human control, there would be very few serious accidents.

It is proposed that both external and internal factors limit the exercise of human rationality in the control of hazards, resulting in errors that are associated with accidents. The following “laws of accident causation”, all of which are empirically testable, are proposed:

1. The universal law of learning, stating that the rate of accidents per unit of exposure is inversely related to the amount of exposure.

2. The law of the unpredictable, stating that the more rarely a certain risk factor is
encountered the larger is its effect on accident rate.

3. The law of complexity, stating that the more units of information a road user must
attend to, the higher becomes the accident rate.

4. The law of cognitive capacity, stating that the more reduced cognitive capacity
becomes, the higher the accident rate.

Instances of all these laws, as well as guidelines as to how the laws can be tested empirically, are given. It is hoped that proposing a few basic mechanisms describing the impact of risk factors will serve as a building block for a more general theory of accident causation.