Brian Nosek, a University of Virginia psychology professor who has devoted his career to making scientific data more reliable and trustworthy, is frustrated. Like everyone else, he's trying to understand the pandemic, particularly in his own community of Charlottesville, and in California, where he has family.
So he wonders: Where is the virus spreading? Where is it suppressed? Where are people social distancing as they should, and where are they not? Where will he and his family be safe?
In this pandemic, we're swimming in statistics, trends, models, projections, infection rates, death tolls. Nosek has professional expertise in interpreting data, but even he is struggling to make sense of the numbers.
“What's crazy is, we're three months in, and we're still not able to calibrate our risk management. It's a mess,” said Nosek, who runs the Center for Open Science, which advocates for transparency in research. “Tell me what to do! Please!”
Decision Intelligence (DI) lets us decide what to do. DI manages data overload and lets us focus on a specific decision. Covid-19 safety decisions do not require holding all the global, aggregated statistics and projections in your head. Most critical decisions address risks at point in time for a specific facility or activity; they require not mountains of data, but understanding how, in this time and place, actions lead to outcomes. As individuals, business owners, executives, or government officials, once we look at the causal links in a coronavirus decision, we can identify the subset of data that is actually relevant. And we can build AI and other models to render that data understandable and actionable and to let us update our decisions as the pandemic changes over time. DI gives us tools and a process to answer the Covid-19 question, “what should I do?” For an example of applying DI to covid-19 decision-making, look here.