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Bacteria could shed light on how financial markets work

What do bankers and bacteria have in common? Finite resources, quick decision-making and an appreciation of trade-offs, according…

From Cholera to the collapse: bankers and bacteria have both been seen as monsters. Bankenstein

What do bankers and bacteria have in common? Finite resources, quick decision-making and an appreciation of trade-offs, according to a study in Ecology Letters. So could bacterial modelling ever help us avoid another banking crash?

Researchers from the Universities of Exeter and Sydney, used mathematical models and lab-based synthetic biology to predict bacterial investment crashes and boom-bust cycles. The study reveals how the diversity of life we see around us is maintained, and demonstrates that the outcomes of investment decisions can be precisely defined. It explains why a single-celled, fat cat investor or Darwinian demon (a hypothetical organism) didn’t win out long ago.

While humans invest in cash, bacteria trade in costly proteins to reduce their stress levels or to increase consumption and so grow faster. Each bacterium makes an investment decision; the bad investors fall by the wayside, the good ones survive.

Since the 1960s, it has been believed that trade-offs in the decision-making process give biological (and economic) investors different niches to exploit. This theory has pervaded all standard economics textbooks, where the importance of trade-offs and their shapes is featured under the concept of the Production Possibility Frontier (PPF). Until now, however, no one has been able to prove or disprove it. Ivana Gudelj at the University of Exeter explained that Levins’s theory was even starting to be doubted on the ecological side.

They believe their research, however, proves the theory and thus backs up decades of ecological, economic and evolutionary thought. “In my opinion our work provides a first experimental test confirming that optimal investment strategies can be predicted by knowing whether trade-offs carry increasing or decreasing - or a mixture of increasing and decreasing - opportunity costs,” Gudelj said.

Tom Ferenci of the University of Sydney, who was also involved in the research, explained how synthetic biology can explore problems in other disciplines where complex variable are at play. “Imagine, for example, if you could construct an artificial set of humans that would be identical except in their IQ, or fat levels, or longevity,” he said. “This of course is impossible with people, but is possible with bacteria.”

But could these bacterial trade-offs ever really influence how we think about economics? “There are multiple constraints in economics,” Gudelj conceded. “But thinking along the lines of boiling systems down to two trade components helps us consider how one finds the optimal investment.”

Harvey Rubin of the University of Pennsylvania, who was not involved in the study, has suggested bankers look to biology before. “There are formal similarities in the architectures of financial systems and biological systems,” he wrote in the New Scientist. “I believe they represent an excellent system on which to base a model financial system.”

The recent research was therefore of interest to Harvey. “The science is of the study is sound, it’s nice piece of work,“ he told The Conversation, "but there’s nothing explicit you can take away from it that could help economists.”

He explained that the study further demonstrates the possibility of a symbiotic relationship between biology and economics, a relationship that could be exploited in the future.

This contributes to a trend of biological models being used for more abstract sciences. Protection against cyber attacks has recently been modelled on the immune system - which similarly protects the body against invading organisms or viruses. Similarly, biological systems were used to bear on the problem of designing and building cyber systems that were both self-organising and robust.

“There is no question in my mind that this biological modelling could be expanded into economics,” Harvey said. “But we would need huge amounts of data to build these models. I am sure that the data is there, we just need access and collaborators.”