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Risk Compensation and Bicycle Helmets

Found via de.rec.fahrrad:

This study investigated risk compensation by cyclists in response to bicycle helmet wearing by observing changes in cycling behavior, reported experience of risk, and a possible objective measure of experienced risk. The suitability of heart rate variability (HRV) as an objective measure of experienced risk was assessed beforehand by recording HRV measures in nine participants watching a thriller film. We observed a significant decrease in HRV in line with expected increases in psychological challenge presented by the film. HRV was then used along with cycling pace and self-reported risk in a field experiment in which 35 cyclist volunteers cycled 0.4 km downhill, once with and once without a helmet. Routine helmet users reported higher experienced risk and cycled slower when they did not wear their helmet in the experiment than when they did wear their helmet, although there was no corresponding change in HRV. For cyclists not accustomed to helmets, there were no changes in speed, perceived risk, or any other measures when cycling with versus without a helmet. The findings are consistent with the notion that those who use helmets routinely perceive reduced risk when wearing a helmet, and compensate by cycling faster. They thus give some support to those urging caution in the use of helmet laws.

(PubMed, Fulltext)

This video is totally unrelated.

Security as a classification problem

(video, via)

Security requires that one can tell the bad guys and the good guys¹ apart. Security is thus, at least in part, a classification problem. Different approaches to security use different typs of classifiers. The Israeli profiling described in the video above essentially implements one particular decision tree. There is nothing particularly good or bad about this particular tree compared to others, or to entirely different ways of doing the job. What matters in the first place is that the classifier is either correct—it never confuses good and bad—or that it is at least biased in the right direction—it may misclassify good guys as bad guys², but not bad guys as good guys. A secondary consideration is efficency. The Isreali approach to airport security optimizes efficiency for a particular threat model.


¹ Or other entities. Security classification may work on objects, actions, situations, or really any combination of features that might matter.

² Assuming the enforcement stage of the mechanism does not cause permanent damage to entities classified as bad.