New York city's skyline engulfed in smoke from the wildfires in Canada this June. (image: Ahmer Kalam/Unsplash)

When COVID-19 hit, SFI External Professor J. Doyne Farmer (University of Oxford) wanted to use his expertise to help predict how the economy would respond to the emerging pandemic. But the realities of COVID-19 — like so many of the concerns humanity currently faces — didn’t fit neatly into standard economic theory. It meant that he and his colleagues had to build new models based on “complexity economics” to make those predictions. 

Traditional economic models assume supply and demand are in equilibrium and that “rational” decision-makers “use all available information to make the best possible decision — the one that yields highest utility,” Farmer writes in his book, Making Sense of Chaos. Complexity economics, on the other hand, “assumes from the outset that agents are ‘boundedly rational,’ with limited ability to reason, who make imperfect decisions.”

In an upcoming SFI workshop, Farmer and others will apply approaches from complexity economics to make sense of some of society’s trickiest issues. The workshop, “Complex System Approaches to 21st Century Challenges: Inequality, Climate Change, and New Technologies,” runs July 31-August 2 and is part of SFI’s new Emergent Political Economies program.

The workshop will include some 60 representatives of multiple disciplines, countries, and organizations, as well as practitioners and funders “to make sure that the research that’s coming out of this project is getting into the hands of people who can actually use it,” says Travis Holmes, SFI’s EPE Program Manager.

That’s what SFI External Professor Jenna Bednar (University of Michigan), a workshop co-organizer, says gets her excited. “It’s very intentionally focused on concrete problems in the world.” Participants will include academics who are “bringing a lot of really good data for us. And they just don’t know how to make sense of those data,” she says. The hope is that complexity science can “start to give them some tools for analysis.”

On a local scale, Bednar offers the example of affordable housing as a “thorny, thorny problem that leads to so many other problems,” potentially affecting everything from mental health to employment, family relations, and the likelihood of experiencing violence or abuse. “Housing is not some sort of issue that you can isolate,” she says, yet the social sciences don’t often have the tools to develop models that take that sort of complexity into account. 

Climate change is another key example. “Traditional economics models have so far made terrible predictions about the climate transition,” Farmer says. Those models have overestimated the price and implementation times of new renewable technologies like solar, wind, and batteries. “This has led to overestimates of the cost of the transition, which has slowed the transition down,” he says.

In the long term, Farmer says, he hopes for another type of transition in which complexity economics “will surpass and largely take over traditional economics. My hope for the workshop is that it will help create a strong community of complexity economics modelers.” 

Support from the Omidyar Network Emergent Political Economies: Rules, Dynamics and Diversity Research.