Our main contribution is how partial information is utilized. We don’t try to build any single full-fledged model. Instead, we use a mathematical trick to study all models in parallel that are not excluded by what we know.
The world can deliver sudden and nasty shocks. Economies can crash, fisheries can collapse, and climate can pass tipping points. Providing ample warning of such transitions presently requires the collection of enormous - and often prohibitive - amounts of data. A new method developed by Thilo Gross , Senior Lecturer in Engineering Mathematic's at the University of Bristol and Steven Lade from the Max-Planck-Institute for the Physics of Complex Systems in Germany promises to change this. In a paper published in PLoS Computational Biology , the researchers present a methodology that uses mathematics to exploit easily obtainable information to a greater effect and thus can reduce the amount of additional data that needs to be collected. The newly proposed method adds a new twist to an old idea. Predicting the behaviour of a system is easy if the system is well understood. For instance the behaviour of a simple pendulum can be well captured by a simple mathematical model that then predicts the dynamics of the pendulum for a long time. However, systems at risk of severe transitions, such as fisheries, are generally complex and not understood in great detail. To warn of critical transitions, scientists therefore use mostly model-free approaches that require close and continuous monitoring of the system under consideration. The present situation thus presents a fundamental dilemma: Predicting transitions without a credible mathematical model needs large amounts of data, but building such a model would entail gathering even larger amounts of information. "How can we improve our chances of seeing crashes coming?
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