Why is it so rarely noted that forecasting is actually crucial to informing all kinds of policy? Traditional social science and policy research is great for understanding what happened in the past, but decision-making requires predicting what is going to happen (if we do X)? 1/n

Make research (more) useful...
Prof: "I discovered X caused a 23% increase!"
Decision-maker: "Great, if I attempt X (or X') what % increase do you think I will see?" 2/n

I love the diversity of social science, but it drives me mad that so few social scientists see that dipping their toe in forecasting would complement the work they are already doing. This is especially true for scholars who believe their research is already "policy relevant." 3/n

Facing the #coronavirus pandemic, forecasts are (thankfully) getting a lot of play. How many cases will we have? What if we lockdown? How many ventilators will we need? These questions and decisions are more consequential than most, but... 4/n

Decision-makers in every field (education, peace-making, environmental regulation, business, etc.) would benefit from receiving more explicit forecasts. So #econtwitter #soctwitter, etc. I hope you'll help find a way to introduce more forecasting into your discipline. 5/n

For those new to this way of thinking, there are lots of people who have relevant forecasting expertise you may wish to learn from or collaborate with. Some of my great academic collaborators include: @PTetlock, @PavelDAtanasov, @donandrewmoore @ctwardy with @ReplicationMkts 6/n

but you could also learn a lot about forecasting from non-academics, e.g. current or former @superforecaster @GJ_Open participants, @foxyforecaster @Superforecastr @MWStory, writers like @dgardner. @slatestarcodex who publishes and scores his own forecasts. 7/n