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@forecastingresearch

Forecasting Research Institute

Advancing the science of forecasting for the public good

We develop forecasting methods to improve decision-making on high-stakes issues. First-generation forecasting research—spearheaded by FRI Chief Scientist Philip Tetlock and coauthors—focused on establishing a rigorous standard for prediction accuracy. The next generation of work aims to channel this approach into real-world relevance. We work with policymakers and nonprofit organizations to design practical forecasting tools, and test them in large experiments.

Our research centers on:

  1. Producing high-quality forecasting questions about complex, long-run topics;
  2. Novel methods for resolving unresolvable questions;
  3. Testing the robustness of forecasting techniques across different domains and contexts; and
  4. Using forecasting tools to help organizations make better decisions.

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  1. xpt-lib xpt-lib Public

    Provides the data (anonymized) and code necessary to replicate the results of the Existential Risk Persuasion Tournament (XPT). The XPT was a forecasting tournament that explored potential threats …

    R 5 1

  2. aggutils aggutils Public

    Utilities for Aggregating Probabilistic Forecasts

    R 3 1

  3. forecastbench forecastbench Public

    A dynamic forecasting benchmark for LLMs

    Python 26 2

  4. forecastbench-datasets forecastbench-datasets Public

    Forecastbench Datasets, updated nightly

    JSON 12

  5. adversarial-collab adversarial-collab Public

    Provides the data (anonymized) and code necessary to replicate the results in FRI's report "Roots of Disagreement on AI Risk: Exploring the Potential and Pitfalls of Adversarial Collaboration."

  6. voivod voivod Public

    Provides a set of statistics for evaluating forecasting questions (not forecasts, but the questions themselves, with respect to another question), i.e. assessing the cruxiness of questions. Current…

    R

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