Research

This post explores peer prediction — mechanisms that incentivize truth-telling without external verification. It defines key properties for decentralized moderation and reviews leading models, concluding that the Mutual Information Paradigm offers the most promising balance between truthfulness, simplicity, and openness.

This post introduces the Bayesian Truth Serum (BTS), a mechanism that rewards honest reporting in surveys by linking payouts to how “surprisingly common” an answer is. We explain how it works, explore its applications across fields like marketing and machine learning, and examine why it struggles with collusion and scalability—key challenges when applying it to decentralized moderation.

This third post in the “Quest for Social Sovereignty” series explores prediction markets as mechanisms for truth discovery and decentralized moderation. It examines how markets can aggregate beliefs, extend epistemic power (“omniscience”) to moderators, and inspire novel dispute-resolution schemes like Hanson’s Double-or-Nothing lawsuits and “moderation poker.” The piece concludes by pointing toward oracle-free systems for truth elicitation.

This is the #2 in our series "The quest for social sovereignty". It explores social epistemology—the study of how communities generate and judge knowledge—through the lens of Birdwatch/Community Notes, its strengths, vulnerabilities, and possible improvements for truth-seeking in social media.

Today’s social media platforms strip users of sovereignty over their content, data, and identity. As decentralized social networks emerge as a credible alternative, they offer new freedoms—but also face challenges like moderation and spam. In this blog series, we at ❜embed explore how to make decentralized social usable, transparent, and incentive-aligned, starting with recommender systems and moving toward credibly neutral moderation. Join us as we unpack the future of social algorithms and how we can build them in service of users, not platforms.