Hazards, accidents, and data processing
We look at some fundamentals of system safety – everyone's favourite overlooked topic. Then we talk information theory because that's always a hoot.
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Hazardous States and Accidents
By popular demand, I have started trying to write about system safety/reliability again. To understand what goes on, we need some definitions, and we start here: accidents arise when the system is in a hazardous state and it encounters bad environmental conditions. To avoid accidents, we need to avoid hazardous states.
Full article (7–13 minute read): Hazardous States and Accidents
Flashcard of the week
Given an unknown value X, an observation Y, and a transformation f(Y), we have I(X; Y) ≥ I(X; f(Y)). What does this mean intuitively?
This is a principle in information theory called the data processing inequality. When we say I(X; Y) we are looking at the mutual information between X and Y, which is like correlation but for entropy – it tells us how much lower the entropy of X is when we know the value of Y. (Indeed it can be defined as I(X; Y) + H(X|Y) = H(X) where H is entropy.)
Now, the intuitive interpretation of the data processing inequality is that
We cannot gain information about the unknown value X by processing the observation Y in any way. Processing at best retains the information contained in Y, and has the potential of throwing it away.
(So how do things like Kalman filters improve our understanding of unknown values? They combine past observations, i.e. they aren't pure local transformations of the single observation.)
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