We are increasingly better at predicting things about our environment. Modern weather forecasts are a lot better than they used to be, and our ability to predict climate change illustrates our better understanding of our effect on our environment. But what about predicting our collective effect on ourselves?  We now use tools like Google maps to predict how long it will take us to drive to work, and other small things, but we fail miserably when it comes to many of the big things. For example, the recent financial crisis cost the world tens of trillions of pounds, yet our ability to forecast, understand and mitigate the next economic crisis is very low. Is this inherently impossible? Or perhaps we are just not going about it the right way? The complex systems approach to economics, which brings in insights from the physical and natural sciences, presents an alternative to standard methods. Doyne will explain what this new approach is and give a few examples of its successes so far. He will then present a vision of the economics of the future which will need to confront the serious problems that the world will soon face.
 

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This is an introduction to the article 

A. Beilinson, p-adic periods and derived de Rham cohomology, J. Amer. Math. Soc. 25 (2012), no. 3, 715--738.

Over the years, nonlinear and nonparametric models have attracted a great deal of attention. This is mainly due to the fact that most time series arising from the real-world exhibit nonlinear behavior, whereas nonparametric models, in principle, do not make strong prior assumptions about the true functional form of the underlying data generating process.

In this workshop, we will focus on the use of nonlinear and nonparametric modelling approaches for time series forecasting, and discuss the need and implications of accurate forecasts for informed policy and decision-making. Crucially, we will discuss some of the major challenges (and potential solutions) in probabilistic time series forecasting, with emphasis on: (1) Modelling in the presence of regime shifts, (2) Effect of model over-fitting on out-of-sample forecast accuracy, and, (3) Importance of using naïve benchmarks and different performance scores for model comparison. We will discuss the applications of different modelling approaches for: Macroeconomics (US GNP), Energy (electricity consumption recorded via smart meters), and Healthcare (remote detection of disease symptoms).

This talk will be a geophysicist's view on the emerging properties of a numerical model representing the Earth's climate and volcanic activity over the past million years.

The model contains a 2D ice sheet (Glen's Law solved with a semi-implicit scheme), an energy balance for the atmosphere and planet surface (explicit), and an ODE for the time evolution of CO2 (explicit).

The dependencies between these models generate behaviour similar to weakly coupled nonlinear oscillators.