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Collective awareness: A vision of a new economics and how it could reduce risk w/Prof Doyne Farmer
May 8 @ 6:00 pm - 7:30 pm
Science gives us a collective awareness that turns unknown unknowns into probabilities and helps us deal with risks and avoid catastrophic scenarios. It is worth distinguishing three levels of collective awareness, that involve understanding the external environment, our effect on the environment, and our collective effect on ourselves. This lecture will focus on the hardest of these — our collective effect on ourselves — and on economics in particular. The economy underpins almost everything we do, and economic fluctuations cost the world many tens of trillions of dollars, yet the budget for polar research is greater than that for economics. Why is there no large-scale effort to better understand the economy? Prof Farmer will argue that our lack of making a serious effort and our lack of progress is due to fundamental problems with the current culture of economics, and macroeconomics in particular. He will present an alternative vision of the economics of the future, with a much stronger emphasis on our ability to simulate the world. This will give us a better day-to-day understanding of the economy, but most importantly, it will allow us to better use science to think about the big problems in our future, such as climate change, the digital economy, and the overarching changes to human existence that the bio, info, nano and cognitive technologies of the future will bring.
About the speaker
Prof J. Doyne Farmer is Director of the Complexity Economics program at the Institute for New Economic Thinking at the Oxford Martin School, Professor in the Mathematical Institute at the University of Oxford, and an External Professor at the Santa Fe Institute.
His current research is in economics, including agent-based modelling, financial instability and technological progress. He was a founder of Prediction Company, a quantitative automated trading firm that was sold to the United Bank of Switzerland in 2006. His past research includes complex systems, dynamical systems theory, time series analysis and theoretical biology.