Trusting information

Spinning swarm sensing from comment threads

A flip side to filter bubbles is the problem of verifying that the information you have is good. If we got to design all the agents in society we might be able to ensure the overall system acquires good information. But when we are dealing with the practicalities of real people, Do you do this by some kind of social trust graph? Some other mechanism? Perhaps social network consensus as with could do this?

How does this related to mora

Proof-of-identity systems

A simple case. Let’s just work out if we are receiving real information here. Is the message you for from me really from me?

Web of Trust is troublesome for all the usual reasons that encryption is troublesome.


Notary aims to make the internet more secure by making it easy for people to publish and verify content. We often rely on TLS to secure our communications with a web server, which is inherently flawed, as any compromise of the server enables malicious content to be substituted for the legitimate content.

With Notary, publishers can sign their content offline using keys kept highly secure. Once the publisher is ready to make the content available, they can push their signed trusted collection to a Notary Server.

Consumers, having acquired the publisher’s public key through a secure channel, can then communicate with any Notary server or (insecure) mirror, relying only on the publisher’s key to determine the validity and integrity of the received content.

Keybase has an interesting solution here. TBC.



Civil’s attempts to find blockchain-backed proof-of-truth for journalism Others?

Is a reputation system perhaps sufficient?

Robin Hanson once again has a framing I want, asking what info is verifiable. He would like it to be verifiable in the sense that we can make a contract about an outcome, with obvious application to blockchains, prediction markets and general mechanism design.

Farrell, Henry, and Bruce Schneier. 2018. “Common-Knowledge Attacks on Democracy.” SSRN Scholarly Paper ID 3273111. Rochester, NY: Social Science Research Network.

Teplitskiy, Misha, Eamon Duede, Michael Menietti, and Karim R. Lakhani. 2020. “Citations Systematically Misrepresent the Quality and Impact of Research Articles: Survey and Experimental Evidence from Thousands of Citers,” February.