— by Thomas J. Sargent, New York University
Distinguished Research Fellow
Imagine you are lying in bed at 6:30 in the morning and you hear the newspaper land at your front door. You get up and look at the front page and see the headline: “Bank Run Today, see page 2.”
The chalkboards are empty. Bob Lucas starts at the upper left, writing out an economic model and explaining as he goes. Sometimes he boxes in a result. When all the boards are filled, he stands back. Points to each box. Shows where this is going. “Then, BOOM, BOOM. It’s like watching fireworks.
Richard Evans is a Senior Fellow in Computational Social Science at the University of Chicago, and Fellow here at the institute.
A recent wave of research has demonstrated the existence of generic Early Warning Signals (EWS’s) that help predict a large class of abrupt changes in the state of ecological systems -- e.g. “tipping points.” Examples range from experimental laboratory systems of living organisms at tiny scales such as microbes up to ecosystems at the scale of lakes, rangelands, marine systems, or coral reefs.
Machine learning may be neck and neck with “big data” in the race to being Hollywood’s favorite catch-all term for technology as magic.
Economic modeling is often driven by discovery or development of new data sets. For many substantive applications, the data does not simply “speak for itself,” making it important to build structural models to interpret the evidence in meaningful ways.
Macroeconomic policy—monetary and fiscal—has two primary goals to achieve: determining the aggregate price level and stabilizing government debt. Conventional assignments give monetary policy the task of controlling inflation and fiscal policy the job of stabilizing debt.