If we knew the value of the feedback f, we could predict the response to perturbations just by multiplying them by 1/(1-f) — call this G for “gain”. What happens, Roe and Baker ask, if we do not know the feedback exactly? Suppose, for example, that our measurements are corrupted by noise — or even, with something like the climate, that f is itself stochastically fluctuating. The distribution of values for f might be symmetric and reasonably well-peaked around a typical value, but what about the distribution for G? Well, it’s nothing of the kind. Increasing f just a little increases G by a lot, so starting with a symmetric, not-too-spread distribution of f gives us a skewed distribution for G with a heavy right tail.

## Once again. Cosma Shalizi picks out something elegant and scary

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