The IETF has had a long tradition of doing its technical work through a consensus process, taking into account the different views among IETF participants and coming to (at least rough) consensus on technical matters. In particular, the IETF is supposed not to be run by a "majority rule" philosophy. This is why we engage in rituals like "humming" instead of voting. However, more and more of our actions are now indistinguishable from voting, and quite often we are letting the majority win the day without consideration of minority concerns. This document explains some features of rough consensus, what is not rough consensus, how we have gotten away from it, how we might think about it differently, and the things we can do in order to really achieve rough consensus. Note: This document is quite consciously being put forward as Informational. It does not propose to change any IETF processes and is therefore not a BCP. It is simply a collection of principles, hopefully around which the IETF can come to (at least rough) consensus.
Role differentiation as ignition of a collective information...
Informational active matter shows how measurement-informed decisions produce collective order, so far in systems that reach consensus. We design collective information engines structured by...
Gaming Consensus: Coordinated Manipulation in Crowdsourced Fact-Checking
In this work, we develop the first rigorous analysis of the resistance of the core matrix factorization component of Community Notes to coordinated manipulation. Bridging-based algorithms (Ovadya & Thorburn, 2023; Small et al., 2021) offer some inherent resistance to manipulation: unlike traditional voting systems where adversaries can fabricate volume through fake upvotes, bridging requires diverse agreement across users with opposing viewpoints. Forging bridging consensus is harder as it requires an understanding of users and rating patterns on the platform. However, we demonstrate that it is possible to create synthetic consensus through a carefully designed voting strategy. Our approach does not rely on exploiting any software/security vulnerability or implementation bug, instead leveraging a fundamental property of the system’s core matrix factorization algorithm.
Taking AT to the IETF - AT Protocol
We recently posted two drafts to the IETF Data Tracker. This is the first major step towards standardizing parts of AT in an effort to establish long-term governance for the protocol.
Bridging Voting and Deliberation with Algorithms: Field Insights from vTaiwan and Kultur Komitee
Democratic processes increasingly aim to integrate large-scale voting with face-to-face deliberation, addressing the challenge of reconciling individual preferences with collective decision-making. This work introduces new methods that use algorithms and computational tools to bridge online voting with face-to-face deliberation, tested in two real-world scenarios: Kultur Komitee 2024 (KK24) and vTaiwan. These case studies highlight the practical applications and impacts of the proposed methods. We present three key contributions: (1) Preference-based Clustering for Deliberation (PCD), which enables both in-depth and broad discussions in deliberative settings by computing homogeneous and heterogeneous group compositions with balanced and adjustable group sizes; (2) Human-in-the-loop MES, a practical method that enhances the Method of Equal Shares (MES) algorithm with real-time digital feedback. This builds algorithmic trust by giving participants full control over how much decision-making is delegated to the voting aggregation algorithm as compared to deliberation; and (3) the ReadTheRoom deliberation method, which uses opinion space mapping to identify agreement and divergence, along with spectrum-based preference visualisation to track opinion shifts during deliberation. This approach enhances transparency by clarifying collective sentiment and fosters collaboration by encouraging participants to engage constructively with differing perspectives. By introducing these actionable frameworks, this research extends in-person deliberation with scalable digital methods that address the complexities of modern decision-making in participatory processes.
The Internet is for End Users
This document explains why the IAB believes that, when there is a conflict between the interests of end users of the Internet and other parties, IETF decisions should favor end users. It also explores how the IETF can more effectively achieve this.
The Internet is for End Users
This document explains why the IAB believes that, when there is a conflict between the interests of end users of the Internet and other parties, IETF decisions should favor end users. It also explores how the IETF can more effectively achieve this.
The Internet is for End Users
This document explains why the IAB believes that, when there is a conflict between the interests of end users of the Internet and other parties, IETF decisions should favor end users. It also explores how the IETF can more effectively achieve this.
'Generative CI' through Collective Response Systems
How can many people (who may disagree) come together to answer a question or make a decision? "Collective response systems" are a type of generative collective intelligence (CI) facilitation...
Turing-Complete Governance — The Collective Intelligence Project
It is fiendishly difficult to get lots of people to make decisions and work together both non-hierarchically and effectively. Because blockchain technology enables decentralized consensus at scale, the implications for human decision-making and coordination are immensely promising; if we can use it
Against Modesty’s Bailey
Modesty arguments often say that you should mostly or entirely bow to ‘expert consensus’ or the views of particular others, and who are you to disagree.
The emergence of consensus: a primer
The origin of population-scale coordination has puzzled philosophers and scientists for centuries. Recently, game theory, evolutionary approaches and complex systems science have provided quantitative insights on the mechanisms of social consensus. However, the literature is vast and widely scattered across fields, making it hard for the single researcher to navigate it. This short review aims to provide a compact overview of the main dimensions over which the debate has unfolded and to discuss some representative examples. It focuses on those situations in which consensus emerges ‘spontaneously’ in the absence of centralized institutions and covers topics that include the macroscopic consequences of the different microscopic rules of behavioural contagion, the role of social networks and the mechanisms that prevent the formation of a consensus or alter it after it has emerged. Special attention is devoted to the recent wave of experiments on the emergence of consensus in social systems.
Consensus Democracy | The Paper Pilot
The Paper Pilot's digital garden of thoughts on philosophy, politics, and sociology
'Generative CI' through Collective Response Systems
How can many people (who may disagree) come together to answer a question or make a decision? "Collective response systems" are a type of generative collective intelligence (CI) facilitation process meant to address this challenge. They enable a form of "generative voting", where both the votes, and the choices of what to vote on, are provided by the group. Such systems overcome the traditional limitations of polling, town halls, standard voting, referendums, etc. The generative CI outputs of collective response systems can also be chained together into iterative "collective dialogues", analogously to some kinds of generative AI. Technical advances across domains including recommender systems, language models, and human-computer interaction have led to the development of innovative and scalable collective response systems. For example, Polis has been used around the world to support policy-making at different levels of government, and Remesh has been used by the UN to understand the challenges and needs of ordinary people across war-torn countries. This paper aims to develop a shared language by defining the structure, processes, properties, and principles of such systems. Collective response systems allow non-confrontational exploration of divisive issues, help identify common ground, and elicit insights from those closest to the issues. As a result, they can help overcome gridlock around conflict and governance challenges, increase trust, and develop mandates. Continued progress toward their development and adoption could help revitalize democracies, reimagine corporate governance, transform conflict, and govern powerful AI systems -- both as a complement to deeper deliberative democratic processes and as an option where deeper processes are not applicable or possible.
interfluidity » Consensus not censorship
We’ve become obsessed over the past few years with the problem of misinformation. And for good reason. “Flood the zone with shit” is now standard operating procedure for a variety of interests and factions. Groups who pretend to be above that kind of thing let confirmation bias do the same work, elevating conjectures they find convenient to believe far beyond the evidentiary basis for believing them, and transmuting concurrence among prestigious groups whose biases are aligned into “authority” to which they demand deferrence. Casual information consumers become divided into two camps, the “do your own research” types who imagine, mistakenly, that they are capable of seeing through all this (and so succumb to their own confirmation bias), and those who more accurately understand that they cannot reliably distinguish truth from bullshit (and so opt out of democratic deliberation with a shrug, other than perhaps to vote for the candidates whose political party they distrust less).
Something I keep thinking about is whether/how it would be possible to construct something like "trusted reviewing circles" without (1) destroying peer review's egalitarian goals, (2) accidentally enabling collusion rings, (3) recreating the same system with same issues over time.
Maria Antoniak
We are caught in such a trap. Asking good-faith community members to volunteer more when we can plainly see so much bad-faith behavior without consequences... IDK where it ends. Probably not central source of the problem, but NO ONE should be listed as an "author" on 20, let alone 40, submissions.