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Critical transitions are sudden, often irreversible, changes that can occur in a large variety of complex systems; signals that warn of critical transitions are therefore highly desirable. We propose a new method for early warning signals that integrates multiple sources of information and data about the system through the framework of a generalized model. We demonstrate our proposed approach through several examples, including a previously published fisheries model. We regard our method as complementary to existing early warning signals, taking an approach of intermediate complexity between model-free approaches and fully parameterized simulations. One potential advantage of our approach is that, under appropriate conditions, it may reduce the amount of time series data required for a robust early warning signal.
"OWS isn't a political entity. It doesn't need your support. It's sounding an alarm. Whether you heed it is up to you, and you ar the one who will gain or suffer as a result of your actions. Not them."
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"We have to make people understand the risks," he said.
It seems to me that his co-workers understand the risks better than he does. They know what the real risks are at work, and that they all revolve around not getting the job done. Those risks are real and tangible, and employees feel them all the time. The risks of not following security procedures are much less real. Maybe the employee will get caught, but probably not. And even if he does get caught, the penalties aren't serious.
Given this accurate risk analysis, any rational employee will regularly circumvent security to get his or her job done. That's what the company rewards, and that's what the company actually wants.
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