1. Who can participate?
We’re looking for data-driven (e.g., models) or expertise-based (e.g., general intuition, theory-based), from any field. There are no restrictions, just report your education/areas of knowledge or expertise.
2. Do predictions need to be sole-authored?
Forecasts can be submitted by individuals or teams. There are no restrictions on the number of people in a team.
If there are multiple team members, the team leader should fill out the prediction survey and all other team members are required to fill out the team member survey (for demographic information).
3. Can all team members get authorship on the resulting publication, and if so how?
All team members are eligible for authorship. Please contact Igor Grossmann (igrossma@uwaterloo.ca) or Amanda Rotella (arotella@uwaterloo.ca) for authorship requirements.
Authorship order on the resulting publication will be first determined based on contributions to the project. For authors with similar contributions, authorship order will be determined alphabetically based on family name.
4. I am interested, how can I help?
Please spread the word. Advertise the call on social media (e.g. please retweet!), or send it to others who may be interested in participating.
5. Is it expected for forecasting to be conducted based solely on this data provided?
You can use additional data if you have it handy and want to include it. You are most welcome to add any datasets you may find useful for your estimates – just tell us what you used and how.
6. Is it expected for contributors to explore their own data collection?
You are most welcome to add your own data for the forecasts if you see it as desirable. Just tell us what you used and how.
7. What is the intended use of these contributions?
We aim to categorize contributions based on approaches (e.g., data vs. theory-driven forecasts), and unpack which strategies were more likely to lead to accurate forecasts. For statistical analyses we would not be focusing on single approaches, though we hope to learn from individual approaches that led to most successful forecasts.
8. What is the theory backing this work?
We draw on previous work on forecasting accuracy among experts by Meehl and Dawes, situating forecasts in the context of COVID-19 pandemic as a naturalistic experiment. One of the goals is to establish a benchmark of forecasting accuracy in behavioral and social sciences for societal issues over the course of COVID-19 pandemic. Another goal is to understand how behavioral and social scientists reason when making their forecasts and which reasoning strategies are more likely to be effective for forecasting accuracy.