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Introduction
Existing consensus systems are designed with implicit majoritarian assumptions. The commonly used term in consensus literature for a small group with dissenting opinions is adversary. This is particularly problematic when viewed through the lens of recent proposals to move governance structures to, for instance, blockchain-based systems which have majoritarian assumptions built into their design and operation.
There are some protections provided in social, political, and commercial governance structures for those with dissenting opinions via a system of minority rights, protected freedoms, and other checks and balances. If we are to design computational systems that meet the requirements of real-world institutions, we need to ensure that we preserve or improve upon the protections offered in existing systems. Majoritarian consensus gets in the way of achieving this goal: in their ideal instantiation, a set of nodes (the majority) have all the decision-making power while the remaining nodes have no ability to cause those decisions to be renegotiated. This fundamental problem is further exacerbated in other computational systems (e.g. centralised systems), where the issue is often obfuscated by wrapping the computational system in layers of rough human consensus.
Existing institutional protections are far from perfect, highlighting the difficulty of the problem, but this reveals more acutely the current lack of research into this area on the computational side.
This workshop - which will lead to a special collection of articles in Data & Policy (cambridge.org/dap), a peer-reviewed open access journal published by Cambridge University Press - aims to raise awareness of the issue and seeks answers to some fundamental questions about computational governance when viewed through this lens:
Questions
- What can technologists learn from other systems of governance?
- What advances in computer science would be required for computational systems to be useful in important affairs like human governance?
- Conversely, are there technologies that policy makers can use to improve existing systems?
The answers to these questions lie at the intersection of fundamental computer science, policy making, jurisprudence and human rights. We invite submissions on the theme of Challenging Majoritarian Assumptions in Computational Governance from all these domains.
Key dates
- Deadline for workshop papers: 24 September 2021
- Workshop: 28th October 2021
- Submission of full papers to Data & Policy: 15th March 2022
- Anticipated publication date in Data & Policy: July 2022
Submission and review process
Workshop
To participate in the workshop, contributors are asked to register via this link.
Submissions must be 6-10 pages in length and should be mailed to: mansoor@openorigins.com
All workshop papers will be assessed by two reviewers. A shepherd will be assigned to each paper to help authors incorporate feedback gathered during the workshop.
Data & Policy
Authors have the option of participating in a special collection on computational governance in Data & Policy, a peer-reviewed open access journal at the interface of data science and governance, published by Cambridge University Press.
The two reviews conducted for the workshop will be considered as part of the Data & Policy peer review process - expediting the process in comparison with a regular submission - with the journal reserving the right to seek an additional review to ensure that the paper has had input from data / methodological and policy perspectives.
Authors participating in the workshop interested in submitting to Data & Policy are encouraged to familiarise themselves with the Instructions for Authors in advance of submission to the journal. We anticipate that interested authors will develop their workshop papers into full research articles, but the Data & Policy team can be contacted dataandpolicy@cambridge.org if you have any questions.
Note that the journal provides LaTeX and Word templates to assist authors with the structure of papers. Data & Policy asks all authors to provide a Data Availability Statement with the submission and encourages, but does not require, authors to make underlying data and replication materials available via an open repository.
The final set of articles will be presented on a curated page on the Data & Policy website, with an introduction from the Guest Editors.
Guest Editors
- Dr. Mansoor Ahmed-Rengers (University of Cambridge)
- Dann Toliver (T.R.I.E. / TODAQ)
- Hazem Danny Al-Nakib (UCL)