Social Protocols

Introduction: Scalable Group Decision-Making

A deliberative consensus protocol is a process that online groups can use to make decisions. It’s designed to produce good decisions that are fair and manifest the collective intelligence of the group. And it’s designed to work at scale.

This is not easy. Once a group gets large enough, people will start trying to manipulate the results. And even if everyone acts in good faith, it is hard for a large group to agree even on basic facts, let alone optimal decisions. And even if people agree on the facts, they may have vastly different values and preferences.

UPDATE: See a discussion of this article on Hacker News.

Introduction

Bridging-Based Ranking is a way of scoring and ranking online content that bridges divides.

The term “Bridging-Based Ranking” was introduced in this essay by Aviv Ovadya of the Harvard Kennedy School Belfer Center. In his essay Ovadya explains how social media algorithms today tend to promote polarization and division. But it doesn’t have to be this way. Instead of promoting divisive comment that triggers people’s tribal instincts, couldn’t the algorithms help find and promote areas of common ground?

Introduction

In my article on Understanding Community Notes, I describe the basic Matrix Factorization algorithm used to identify notes that are helpful despite user polarization. In this article, I introduce a way to break this algorithm and describe an variation of the algorithm that uses 2-factor Matrix factorization.

Breaking the Algorithm

The algorithm uses Matrix Factorization to find a latent factor that best explains the variation among users’ votes. It assumes that this latent factor corresponds to some sort of polarization within the community. But what if the latent factor is due to diversity but not polarization? What if the factor that best explains variation in users’ votes corresponds to, for example, how informed or educated that user is? For example, suppose in some expert advice forum the regression for a post looks like this.

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 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? If content is distributed and no attention is paid to it, does it matter?”

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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 take for granted that our Twitter and Facebook feeds are free from videos of live-streamed suicides, violent executions, and child abuse. We don’t see the small army of tens of thousands of moderators (35,000 at Facebook as of Oct 2020) at work behind our feeds, spending all day viewing the most horrific content in the world so that we don’t have to.

A deliberative poll measures the informed opinion of a group of people who have participated in a discussion about the topic of the poll. This essay introduces a method for integrating deliberative polling into online discussions in social platforms, in order to discover the informed opinion of a group.

Many of us struggle to separate the information from the misinformation on social media, especially in the past weeks and months as we seek facts regarding COVID-19. The platforms themselves do not help much. It seems increasingly clear that the algorithms used to determine which posts and tweets show up at the top of your feed, having been optimized for engagement, are inadvertently optimized for misinformation.

But social media is still useful, because accurate information does spread too. And it spreads fast. But it’s up to us to judge for ourselves which information is true and which isn’t, and sometimes we get it wrong.

Today I have been thinking about Prediction Markets, and thought I would share some thoughts on how the Decision Engine could be designed to behave very much like a Prediction Market.

If you aren’t familiar with prediction markets, check out this explanation from Argon Group.

Verifiable Events

The Consensus Index as Market Price

The Consensus index will tend to change over time, especially as people introduce convincing arguments. It is this difference between the initial, pre-argument consensus index and the final consensus index (determined by some stopping condition such as time or stability) that makes for the possibility of an interesting prediction market.

In my last post, I introduced the general idea behind the decision engine. This post walks you through an example of what the decision engine might look like from a user’s point of view, and hopefully give you an idea of how it could result in more intelligent group conversations.

You see a screen that says:

My big project right now is something called a “decision engine”. Put simply, a decision engine is:

"a conversation-based process for group decision making"

The process uses game mechanics to create the right motivations, awarding players for introducing and defending reasons for supporting a decision, but also for acknowledging valid counter-arguments, and abandoning an argument if they see they will not be able to defend it.