If you see something, say something — and we’ll ignore it

I arrived on a late evening flight at a Canadian airport that will remain nameless, and I was the second person into an otherwise deserted Customs Hall. On a chair was a cloth shoulder bag and a 10″ by 10″ by 4″ opaque plastic container. Being a good citizen, I went over to the distant Customs officers on duty and told them about it. They did absolutely nothing.

There are lessons here about predictive modelling in adversarial settings. The Customs officers were using, in their minds, a Bayesion predictor, which is the way that we, as humans, make many of our predictions. In this Bayesian predictor, the prior that the ownerless items contained explosives was very small, so the overall probability that they should act was also very small — and so they didn’t act.

Compare this to the predictive model used by firefighters. When a fire alarm goes off, they don’t consider a prior at all. That is, they don’t consider factors such as: a lot of new students just arrived in town, we just answered a hoax call to this location an hour ago, or anything else of the same kind. They respond regardless of whether they consider it a ‘real’ fire or not.

The challenge is how to train front-line defenders against acts of terror to use the firefighter predictive model rather than the Bayesian one. Clearly, there’s still some distance to go.

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