Hypothesis
  • The Hypothesis White Paper
  • Introduction
    • Thesis
  • The Hypothesis Vision
    • Defining "Trust" in a "Post-Truth" World
    • A New Way To Interact With Media
    • Interweaving Incentives With Foresight and Usefulness
    • A New Model of Accountability
    • Section Summary
  • Building a platform based on openness & transparency
    • Redefining “Community-Powered”
      • Formulating Questions
      • Context
      • Question Resolution
      • Groups
    • Data-Driven Gamification
      • Foresight
      • Usefulness
      • Quality of Research
      • Knowledge Metric
    • Protecting User Privacy
  • Monetization
    • A Note on Monetization
    • Subscriptions
    • Foresight API & the future of Hypothesis
      • Cultivating Oracles & User-Earned Revenue
      • The Media’s Monetization Problem
  • Concerns
    • Platform Limitations
    • Dangers of Bias
  • Conclusion
    • Closing Remarks
    • Roadmap
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  1. Building a platform based on openness & transparency
  2. Data-Driven Gamification

Usefulness

For creators, Usefulness serves the purpose of indicating a source’s relative value to users in making an accurate prediction over time. Usefulness can be more technically described as content’s value in terms of the extent to which it was interacted with by both correctly-predicting and incorrectly-predicting users in the process of making their predictions. Mathematically, Usefulness also makes use of a k-value and constant that serve the same purpose as they do for Foresight.

While Hypothesis intends to be transparent about as much of its technology as possible, the processes describing Usefulness are currently under development and cannot be made public. Relevant stakeholders may contact miles@hypothesis.fyi for more information regarding Hypothesis algorithms.

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Last updated 3 years ago

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