Category: Experimental - Draft
Obsoletes: P/1
This document specifies an experimental, adaptive, and self-governing system for decentralized decision-making. It incorporates findings from systemic simulations and academic reviews on algorithmic governance. This version introduces critical mechanisms for explicability, accountability, and impact assessment to address previously identified vulnerabilities. Discussion and suggestions for improvements are encouraged. Distribution of this memo is unlimited.
The Dynamic Voting Protocol is a decentralized system designed to enhance collective decision-making. While prior versions introduced adaptive safeguards and meta-governance to prevent "meritocratic tyranny," a deeper analysis revealed vulnerabilities related to a lack of genuine accountability and explicability. This revised specification integrates three new core mechanisms: Governance Impact Statements for meta-governance proposals, Reputation Slashing as a consequence for poor governance, and a public explicability dashboard. The protocol now aims to create a system that is not only fair and adaptive but also transparently accountable and understandable to its participants.
- Background
- Protocol Overview
- Main Components
- 3.1. Voter Profiles
- 3.2. Reputation Assessments
- 3.3. Vote Delegation
- 3.4. Bridging Strategies
- 3.5. Verifications
- 3.6. Connections
- 3.7. Decentralized Network
- 3.8. Participants
- 3.9. Incentives
- 3.10. Governance and Meta-Governance
- 3.11. [NEW] Accountability Lifecycle: Harm Index, Reversal, and Slashing
- Vote Weight and Reputation Dynamics
- Explicability and Transparency
- Data Structures
- Protocol Sketch
- Rewards for Nodes (Miners)
- Blockchain Technology
- Protection Against Abuse and Manipulation
- Applications
- Open Questions and Implementation Challenges
- Related Work
- Acknowledgments
- References
The design of this protocol is a direct response to the vulnerabilities observed in liquid democracy. Experiments from the W. Allen Wallis Institute of Political Economy have shown that "over-delegation" can lead to suboptimal outcomes (Guo & McMurray, 2019). This phenomenon, which we term "meritocratic tyranny," is the central problem that the DVP's adaptive safeguards are designed to solve.
Furthermore, the governance of DAOs faces voter apathy and power concentration, as documented in Frontiers in Blockchain (Fan et al., 2024). This study criticizes delegation models that lack accountability mechanisms, a weakness this version of the protocol directly addresses through the introduction of reputation "slashing."
The general framework operates within the principles of algorithmic governance. A report from the European Parliament underscores the need for not only transparency but also explicability and correction mechanisms (European Parliament, 2019). In response, the DVP now requires Governance Impact Statements for meta-governance proposals, a process inspired by the "Algorithmic Impact Assessments" recommended in that report.
The Dynamic Voting Protocol is a decentralized, blockchain-based framework designed to revolutionize collective decision-making. While initial versions focused on three pillars—thematic reputation, vote delegation, and bridging strategies—simulations revealed a critical vulnerability: a tendency toward meritocratic tyranny, where the self-reinforcing success of a majority can systematically suppress the influence of minority or new participants, regardless of the quality of their contributions. This finding necessitates the introduction of a fourth, crucial pillar: Adaptive Safeguards.
The protocol's core function remains to weight votes based on demonstrated expertise. However, it now incorporates a dynamic "equity thermostat" that continuously monitors the health of the system. Using on-chain metrics like the Gini coefficient of reputation distribution, the protocol automatically adjusts its own rules to prevent both systemic suppression and "equity over-correction"—a state where merit is diluted in the name of fairness.
Crucially, the parameters of this adaptive system are not set in stone. They are themselves governed by the community through a meta-reputation layer. This creates a resilient, self-regulating, and homeostatic ecosystem capable of evolving its own rules to maintain a long-term balance between merit and inclusion.
Voter profiles form the foundational data structure of the protocol, encapsulating a participant's identity and multiple dimensions of their reputation. Each profile contains:
- Thematic Reputation Scores: A set of domain-specific reputation values (e.g., technology, policy) that determine a voter's influence on specific topics.
- Meta-Reputation Score: A distinct reputation value earned by participating constructively in the governance of the protocol itself. This score determines a voter's influence on proposals to change the protocol's rules.
- Voting History: A record of votes cast or delegated.
- Delegation Preferences: A list of delegates for both thematic and meta-governance votes.
- Connections: Reciprocal relationships with other voters.
Reputation is earned through a combination of peer evaluations, verified contributions, and a history of accurate voting.
To counteract meritocratic tyranny, the protocol implements autonomous, adaptive safeguards that moderate the thematic reputation system.
- Dynamic Reputation Floor (R_min): A minimum reputation floor that is algorithmically tied to the network's Gini coefficient. It rises to protect participants when inequality grows and lowers to allow for meritocratic differentiation when the system becomes too flat.
- Decaying Contribution Bonus (B_velocity): A temporary reputation multiplier for new participants that decays over their first several contributions, helping them overcome the initial barrier to entry without devaluing long-term merit.
Participants can delegate their voting power—both thematic and meta-reputation—to trusted experts, allowing for a fluid blend of direct and representative democracy.
Algorithms reward decisions that achieve consensus across diverse groups, promoting inclusivity over polarization.
Crowdsourced verifications ensure participant authenticity.
Voters can form reciprocal connections to build a network of trust.
The protocol operates on a blockchain for integrity and transparency.
- Voters (End-Users): Participate in both thematic and meta-governance decisions.
- Storers, Nodes, Retrievers: Technical actors who maintain the network infrastructure.
Incentives include reputation gains for constructive participation and token rewards for network maintenance.
The protocol features a two-tiered governance structure:
- Thematic Governance: Day-to-day decision-making on specific topics, weighted by Thematic Reputation.
- Meta-Governance: Decision-making about the protocol's rules, such as adjusting the Gini threshold or the B_velocity decay rate. These votes are weighted by Meta-Reputation.
To combat apathy and ensure informed decisions in meta-governance, every proposal to change the protocol's rules must be accompanied by a Governance Impact Statement. This document, drafted by the proposer, must include:
- Statement of Purpose: What specific problem does this rule change aim to solve?
- Projected Impact Analysis: How is this change expected to affect key system metrics (reputation gap, Gini, participation)?
- Risk Assessment: What are the worst-case scenarios and unintended consequences?
- Mitigation Plan: If risks are identified, what measures are proposed to mitigate them?
To ensure that the protocol is not only adaptive but also self-correcting, it implements a formal accountability lifecycle. This process is triggered when a meta-governance decision is suspected of causing systemic harm.
The Harm Index: "Harm" is defined not by a single metric but by a composite Harm Index, calculated continuously. The index is a weighted score of key health indicators:
- Inequality Component: The current Gini coefficient of the thematic reputation distribution.
- Gridlock Component: The failure rate of "good" (non-malicious) meta-governance proposals over a rolling window.
The weights of these components (e.g., 70% Gini, 30% Gridlock) are themselves meta-governance parameters.
The Reversal Vote: If a meta-governance decision is implemented and the Harm Index subsequently crosses a critical, pre-defined Harm Threshold, a Reversal Vote is automatically triggered.
This is a simple majority vote (weighted by Meta-Reputation) to nullify the harmful rule change and revert the parameter to a safe, default state.
Reputation Slashing and Shared Risk: If the Reversal Vote passes, the accountability mechanism is activated:
- Slashing: The key participants who voted for the original harmful proposal will have a significant percentage of their Meta-Reputation permanently destroyed (slashed).
- Shared Risk: Participants who delegated their Meta-Reputation to a slashed participant will also lose a smaller percentage of their Meta-Reputation. This incentivizes careful delegation.
This entire lifecycle creates a systemic immune response, providing a strong disincentive against poorly conceived or malicious governance proposals and ensuring the long-term health of the protocol.
The weight of a vote (W_v) is calculated as:
W_v = R * D * B * Q
Where R is the relevant reputation (Thematic or Meta), D is the Delegation factor, B is the Bridging factor, and Q is a Quality score.
A participant's thematic reputation is moderated by the Adaptive Safeguards. Meta-Reputation is earned separately through constructive participation in meta-governance.
Transparency without understanding is insufficient. The protocol must maintain a public explicability dashboard that translates raw data into comprehensible information. This dashboard will include:
- Decision Narratives: Natural language explanations for automated actions (e.g., "Inequality (Gini) has increased... the safety net (R_min) has been activated.").
- Governance Impact Tracking: A visual tracker showing the real-world impact of a rule change compared to the projections made in its Governance Impact Statement.
(This section would be expanded in a technical paper to detail the on-chain structures for reputation, delegation with slashing risk, proposals with impact statements, and the historical data for the Harm Index.)
(This section would be expanded to describe the lifecycle of a meta-governance proposal, from submission with an impact statement, to voting, implementation, harm monitoring, and potential reversal with slashing.)
The primary defense against systemic bias is the integrated system of Adaptive Safeguards and Meta-Governance. The addition of a formal Accountability Lifecycle provides a strong disincentive against malicious or poorly conceived governance proposals.
- DAO Governance: Fairer, more resilient, and accountable governance.
- Public Consultations: Representative outcomes with clear mechanisms for redress.
- Community Debates: Highlighting informed consensus with understandable system dynamics.
- Policy Development: A self-regulating and self-correcting system for bridging stakeholder input.
The focus now shifts to the robust implementation of these new accountability and explicability mechanisms.
- Slashing Parameters: What are the optimal percentages for reputation slashing to deter bad behavior without overly discouraging participation?
- Explicability Engine: What natural language generation models and data visualization techniques are best suited for the public dashboard?
- Harm Index Calibration: What are the optimal initial weights and thresholds for the Harm Index?
- Aragon, DAOstack: Blockchain voting systems.
- Bloom, uPort: Decentralized reputation frameworks.
Thanks to contributors for valuable feedback and to the SEI 2.3 framework for its ethical guidance in identifying, simulating, and mitigating systemic risks.
- Buterin, V., 2013. Ethereum Whitepaper.
- Conger, K., 2016. LinkedIn Sues Mantheos For Scraping User Data. TechCrunch.
- European Parliament, 2019. A governance framework for algorithmic accountability and transparency.
- Fan, C., et al., 2024. Delegated voting in decentralized autonomous organizations: a scoping review. Frontiers in Blockchain.
- Guo, M., & McMurray, N., 2019. Liquid Democracy: Two Experiments on Delegation in Voting. W. Allen Wallis Institute of Political Economy.
- Hardt, S. & Lopes, L., 2015. Google Votes: A Liquid Democracy Experiment on a Corporate Social Network.
- Jiang, J., & Li, T., 2024. Agent-based modeling for decentralized autonomous organizations and decentralized finance. Risk.net.
- Liddell, H. G., & Scott, R., 1889. An Intermediate Greek-English Lexicon.
- Nakamoto, S., 2008. Bitcoin: A Peer-to-Peer Electronic Cash System.
- Ovadya, A., 2022. How Platform Recommendation Systems Might Reduce Division and Strengthen Democracy.
- Sørensen, E., & Torfing, J., 2009. Making governance networks effective and democratic through meta-governance. Public Administration.
- Torrenegra, A., 2018. Torre Protocol: The People Network for Work.
This work has not been validated or claimed as a novelty. All of the Intellectual Property Rights or other rights that might be claimed to pertain to the implementation or use of the technology described in this document or the extent to which any license under such rights might or might not be available; nor does it represent that it has made any independent effort to identify any such rights. Information on the procedures with respect to rights in RFC documents can be found in BCP 78 and BCP 79 of the Internet Research Task Force.
The writers invite any interested party to bring to its attention any copyrights, patents or patent applications, or other proprietary rights that may cover technology that may be required to implement this standard. Please address the information to jalban.arq@gmail.com.