The conditions that set up an economy for a crisis are often subtle systemic processes that gradually reduce the network's resilience, but clues often can be seen in financial data leading up to the crash.
Deploying ideas and tools from complexity science would go a long way toward foreseeing market interdependencies that cause instabilities in global financial markets, according to a group of scientists writing today in Science, including SFI External Professor Doyne Farmer and SFI Science Board member Robert May.
Since the financial crisis of 2008, many economists have adopted language from complexity science -- “tipping point,” “networks,” “feedback,” and “resilience”-- but that has not translated to widespread use of the field's emerging quantitative and modeling tools.
One challenge is that individual banks are reticent to share information about their own networks, even though, on a collective level, their credit-debit interdependencies suggest that sharing would be both stabilizing and morally sound. “Estimating systemic risk relies on granular data on the financial network,” write the study’s authors. “Unfortunately, business interactions between banks are often hidden because of confidentiality issues.”
"It is an opportune time for academic economists, complexity scientists, social scientists, ecologists, epidemiologists, and researchers at financial institutions to join forces to develop tools from complexity theory, as a complement to existing economic modeling approaches," they write. "One ambitious option would be an online, financial-economic dashboard that integrates data, methods, and indicators. This might monitor and stress-test the global socioeconomic and financial system in something close to real time, in a way similar to what is done with other complex systems, such as weather systems and social networks."
Read the article in Science (February 18, 2016)
Read the article in International Business Times (February 19, 2016)
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