University of California, Berkeley

To optimize, scale, and study the Social Science Prediction Platform, an online resource for collecting and cataloguing expert forecasts about the results of social science experiments

  • Amount $600,000
  • City Berkeley, CA
  • Investigator Stefano DellaVigna
  • Initiative Behavioral and Regulatory Effects on Decision-making (BRED)
  • Year 2022
  • Program Research
  • Sub-program Economics

This grant provides ongoing support for Stefano DellaVigna at the University of California, Berkeley, and Eva Vivalt at the University of Toronto, who are scaling up their Social Science Prediction Platform (SSPP), an online platform for collecting and cataloguing forecasts about the results of social science experiments. Documenting such forecasts is an increasingly used and useful way to help evaluate the importance of social scientific studies. Among other reasons, it creates a baseline from which to measure the novelty or unexpectedness of a social scientific result or finding. It can also serve as a useful measure of scientific consensus around important or contested issues in a field. Grant funds will allow DellaVigna and Vivalt to include more research projects in the platform, to include more than 5,000 new forecasts, and to include new applications for predictions such as measuring the effectiveness of policy interventions. Funds will enable the team to run conferences and workshops; to produce training materials and outreach activities; to recruit a large and diverse sample of forecasters; and to develop new methodologies and platform capabilities.

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