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Samotsvety Forecasting - GitHub Organization

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Credibility Rating

3/5
Good(3)

Good quality. Reputable source with community review or editorial standards, but less rigorous than peer-reviewed venues.

Rating inherited from publication venue: GitHub

GitHub organization page for Samotsvety Forecasting, a group focused on improving forecasting methods and scoring rules, which is relevant to AI safety through better prediction and evaluation of AI-related risks and outcomes.

Metadata

Importance: 35/100homepage

Summary

This is the GitHub organization page for Samotsvety Forecasting, a forecasting group with two public repositories: one for their website and one proposing improved scoring rules for forecasting platforms. The optimal-scoring repository proposes three alternative scoring rules to address limitations of current methods, including critiques of the Keynesian Beauty Contest approach used in some forecasting research.

Key Points

  • Samotsvety Forecasting maintains two public repositories: their website and an optimal-scoring research project.
  • The optimal-scoring repo proposes three scoring rules as improvements over current forecasting platform alternatives.
  • Critiques the Keynesian Beauty Contest method used in Karger et al. for resolving otherwise unresolvable forecasting questions.
  • Samotsvety is a well-regarded forecasting group whose work informs probabilistic reasoning about AI safety and other risks.

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 This git repository outlines three scoring rules that I believe might serve current forecasting platforms better than current alternatives. It stems from my frustrations with current scoring rules …
 

 
 
 
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 This git repository outlines three scoring rules that I believe might serve current forecasting platforms better than current alternatives. It stems from my frustrations with current scoring rules and with the Keynesian Beauty Contest method used in Karger et al. to resolve questions which may otherwise seem unresolvable

 
 
 
 

 
 
 
 
 

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Resource ID: 13ed69fe34432472 | Stable ID: sid_BISVsScyDA