What happens when the instruments we use to make rigorous scientific predictions operate in ways that we cannot comprehend with natural cognition? In a recent essay published in Aeon, SFI President David Krakauer takes a philosophical deep dive into this fascinating and pressing question.
The U.S. is likely to see a near-term 24% drop in employment, 17% percent drop in wages, and 22% drop in economic activity as a result of the COVID-19 crisis according to a new study co-authored by SFI External Professor Doyne Farmer at the University of Oxford. These impacts will be very unevenly distributed, with the bottom quarter of earners at risk of a 42% loss in employment and bearing a 30% share of total wage losses. In contrast, the study estimates the top quarter of earners only risk a 7% drop in employment and an 18% share of wage losses.
There’s no free lunch when it comes to making predictions about the COVID-19 pandemic.
Common-sense estimates provide quantitative ways to think about the economic impact of COVID-19 in Italy.
Complexity science and computer algorithms can help us address privacy concerns that arise with the pandemic.
American higher education must think outside the academy in a post-pandemic world.
It is important to keep in mind that as agents we maintain bottom-up control, even if we lack decisive power.
Despite the near-universal assumption of individuality in biology, there is little agreement about what individuals are and few rigorous quantitative methods for their identification. A new approach may solve the problem by defining individuals in terms of informational processes.
Beyond our response to the pandemic itself lie the longer-term effects, including new opportunities — social, political, economic, and otherwise.
The current spike in public trust in science gives science communicators an opportunity to reach new audiences.
This time of disruption is also one of opportunity.
A complex systems perspective of viruses offers insight for controlling SARS-CoV-2 and future emerging viruses.
Typical recession and recovery economic behavior offers great stock market buying opportunities.
In their op-ed for STAT, former SFI postdoctoral fellow Laurent Hébert-Dufresne (University of Vermont) and current postdoc Vicky Chuqiao Yang, Complexity Postdoctoral Fellow and Peters Hurst Scholar, argue that if scientists hope to develop better epidemiological models, they must grasp the complex interplay between social behavior and disease.
The analogies we live by are shaping our thoughts about our current situation.
Physical distancing is necessary for reducing infections, but the timing of restrictive confinement makes all the difference.
Transmission T-007 Danielle Allen, E. Glen Weyl, and Rajiv Sethi on How to Reduce COVID-19 Mortality While Easing Economic Decline
A “mobilize and transition” strategy could reduce COVID-19 mortality while cushioning the economic decline.
We can use social media data to detect signatures of global crises, including early warning signs.
Transmission T-005: Andrew Dobson on the Need for Disease Models which Capture Key Complexities of Transmission
The disease models used to guide policy for the COVID-19 pandemic must capture key complexities of transmission.
On March 31, five speakers from epidemiology and economics discussed strategies for both public health and economic recovery, and answered questions from the SFI community.