Every online community has rules that determine how the attention of the community is directed. For example in an online forum, the most up-voted posts may be shown on at the top of the page. This rule concentrates attention on popular content. But this is a terrible rule. It creates perverse incentives for people to share content that people will reflexively upvote based on first impressions. It encourages shallow conversation on lowbrow topics.

In the Introduction to Distributed Bayesian Reasoning, we argue that the rules of Bayesian inference can enable a form of distributed reasoning. In this article we introduce the idea of meta-reasoner, which is the hypothetical fully-informed average juror. The meta-reasoner resembles the average juror in that it holds prior beliefs equal to the average beliefs of the participants, but it is fully-informed because it holds beliefs for every relevant sub-jury.

“When you have eliminated the impossible, all that remains, no matter how improbable, must be the truth.” – Sherlock Holmes (Arthur Conan Doyle) For a long time Bayesian inference was something I understood without really understanding it. I only really got it it after reading Chapter 2 of John K. Kruschke’s textbook Doing Bayesian Data Analysis, where he describes Bayesian Inference as Reallocation of Credibility Across Possibilities I now understand Bayesian Inference to be essentially Sherlock Holmes’ pithy statement about eliminating the impossible quoted above, taken to its mathematical conclusion.

In this article we introduce an argument model: a set of terms for analyzing arguments by naming their parts. There are various argument models in the academic literature on argumentation theory and related fields but none provide us with precise definitions for all the concepts behind our algorithms for improving for online conversations. So we will define those concepts here. Our model incorporates the basic ideas from the influential Toulmin model of argumentation first introduced in 1948.

This essay explains how you can pay people to tell the truth, even if you can’t verify their answers.

In this article, I argue that we can apply game theory to explain and control the behaviors that dominate in an online community. Not only can game theory explain why misinformation and abuse are so common in social platforms, it can be used to design social platforms that will be filled with honest, informed, civil, and behavior. Attention Games “If a tree falls in a forest and no one is around to hear it, does it make a sound?

In this article, I will use game theory to explain why, under certain conditions, otherwise dishonest Internet people will behave with scrupulous honesty, and how social platforms can be intentionally engineered to create these conditions.

Everyone knows that the infamous social media algorithms promote the spread of false and misleading information on the Internet. But could they be engineered to do the opposite?

Distributed Bayesian Reasoning is a kind of hypothetical opinion poll. It tells us not what people actually believe, but what they would believe if they knew more.

In this article I argue that a decentralized community moderation system can be seen as is a kind of consensus protocol, similar to those used to secure blockchains; and that such a protocol can be designed to produce a Nash equilibrium where users reliably enforce a commonly-understood set of community standards of relevance and civility. The Fundamental Moderation Problem Most casual users of social media have no idea of the magnitude of the moderation problem.

We depend on other people for most of what we know about the world. I can observe for myself that the sun rises in the east, but I have never been to Cleveland; I believe it exists because other people do.

Why Accept the Premise? In the previous essay in this series, we defined the ideas of necessity and sufficiency from the perspective of a Bayesian rational agent. If an argument is necessary, then if the subject were to reject the premise, they would decrease their acceptance of the conclusion. And if an argument is sufficient, then if the subject were to accept the premise, they would increase their acceptance of the conclusion.

Argument and Information In the previous essay in this series, we introduced the idea of relevance, and said that a premise is relevant to the conclusion iff $P(A \vert B) > P(A \vert \bar{B})$. Consider the argument (𝐴) this is a good candidate for the job because (𝐵) he has a pulse. Having a pulse may not be a very persuasive reason to hire somebody, but it is probably quite relevant, because if the candidate did not have a pulse, the subject would probably be much less likely to want to hire him.

In this essay, we present an account of argumentation as the exchange of information between Bayesian rational agents. The basic idea of the Bayesian view of probability is that probabilities represent subjective degrees of belief. So if we know the beliefs of some rational “subject”, we can precisely define and measure various concepts relating to the quality of an argument in the mind of the subject. In other words we can objectively measure the subjective quality of an argument.