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REVIEW 5 major objections 7 minor 22 references

Democracy for DAOs: An Empirical Study of Decentralized Governance and Dynamic (Case Study Internet Computer SNS Ecosystem)

T0 review · 5 major / 7 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read SNS DAOs on the Internet Computer sustain participation where other DAO platforms decline.

desk verdict Useful first SNS governance dataset, but the 'sustained engagement' claim overreaches what a voting-power metric can support. read the letter →

arxiv 2507.20234 v1 pith:P4IUNVC7 submitted 2025-07-27 cs.NI cs.ETcs.SI

Burak Arda Okutan , Stefan Schmid , Yvonne-Anne Pignolet This is my paper · ORCID
classification cs.NIcs.ETcs.SI
keywords DAOgovernanceInternetComputerSNSparticipationratesliquiddemocracyvotingdelegationon-chainempiricalstudy
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper asks whether a particular DAO governance design can avoid the voter fatigue and participation decay observed in other blockchain communities. Studying 14 Service Nervous System (SNS) DAOs on the Internet Computer over 20 months and more than 3,000 proposals, it reports an average participation rate of about 64% of voting power, an average approval rate of 96.8%, and an average decision time of about 1.14 days. Its headline finding is that, unlike Ethereum-based DAOs such as Compound, Uniswap, and ENS, these SNS DAOs show sustained or increasing engagement over time rather than decline. The authors attribute this to the SNS design: free voting under a reverse gas model, token-locked neurons with rewards, and per-topic vote delegation. If the result holds, it offers a concrete empirical counterexample to the pattern of decaying participation in DAO governance and a possible template for other ecosystems.

What carries the argument

The central object is the Service Nervous System (SNS), the Internet Computer's DAO framework: a shared governance canister codebase that each community parameterizes. The load-bearing mechanisms are the reverse gas model (developers prepay computation so voting costs users nothing), neuron-based voting power that grows with token lockup and age, per-topic delegation ('following') where a neuron's vote is cast automatically when a majority of its chosen followees agree, and token rewards proportional to voting participation. These mechanisms together are what the paper credits for high, sustained participation, near-universal approval, and fast decisions; it uses them to explain the contrast with gas-fee-burdened, delegation-poor Ethereum DAOs.

What would settle it

Recompute participation using distinct principals or human-linked accounts instead of voting power; if the number of distinct voters declines while voting-power participation stays high, the sustained-engagement claim fails. Also test whether the top 1% of neurons control a majority of voting power; if so, the 64% figure may reflect automated votes from a few large holders.

Watch

Extended reading notes

Core claim

The central discovery is that the Internet Computer's SNS governance framework produces persistently high community engagement, whereas other DAO platforms studied in the literature show participation decay. Across 14 SNS DAOs ranging from DeFi to gaming and meme coins, the authors measure an average participation rate of 64.34% of total voting power, an average approval rate of 96.8%, and an average decision-making duration of 1.14 days, with no monotonic decline over time. The paper argues this stems from mechanisms unique to SNS: a reverse gas model that makes voting free for users, rewards distributed in native tokens proportional to voting power, topic-specific delegation ('following') that lets neurons delegate to experts, and low proposal costs around $11 compared to thousands on Ethereum. The authors interpret these results as evidence that SNS-style governance—fully on-chain, low-cost, delegatable, and reward-backed—can sustain democratic involvement and serve as a replicable model for other blockchain ecosystems.

Load-bearing premise

The paper counts a vote as participation when voting power is cast, so automated votes from delegated neurons and a few large holders can keep participation high without broad human engagement.

Editorial extensions

If this is right

  • If the central claim is correct, SNS-style low-cost, reward-backed, delegatable voting is a workable antidote to voter fatigue in DAOs, sustaining engagement where fee-based on-chain voting declines.
  • The combination of fast decisions (1.14 days on average) and high participation suggests that cost and delegation design, not community size or proposal volume, are the main levers of governance agility.
  • The near-universal approval rates (96.8%) and low rejection of non-critical proposals indicate high alignment or potentially low scrutiny; the paper itself flags the need to study whether a few high-power neurons dominate small DAOs.
  • The proposal-activity comparison (OpenChat at about 48 proposals per month versus Uniswap at 1.7) implies SNS communities can sustain continuous development governance without overwhelming voters.
  • The paper's cost figures (roughly $11 per proposal versus $594 to $20,000 on Ethereum) imply that governance cost is a first-order determinant of participation, a direct design lesson for other ecosystems.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The participation metric—share of voting power that votes—can be inflated by delegation and by a few large neurons; testing participation by distinct neuron owners or human wallets could reveal that the 'sustained engagement' is largely automated voting, a possibility the paper acknowledges only qualitatively.
  • The same incentive structure that sustains participation (rewards proportional to voting power, voting power growing with lockup) may entrench early large holders, so the fast, high-approval governance could trade breadth for oligopoly; a power-law analysis of neuron sizes would test this.
  • The SNS finding suggests a testable design rule for other chains: eliminating per-vote transaction fees and adding per-topic delegation should raise participation; a natural experiment would be a fork or layer-2 DAO adopting the reverse gas model and measuring the participation slope over time.
  • The lower participation on critical proposals suggests that requiring more effort for high-stakes votes may backfire; a design that lowers the effort for critical votes, such as better delegation defaults, could raise engagement on the decisions that matter most.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

5 major / 7 minor

Summary. This paper presents an observational study of 14 SNS DAOs on the Internet Computer, based on over 3,000 proposals collected from launch until September 2024. The authors define participation, approval, rejection, and decision-duration metrics, report aggregate averages (participation about 64%, approval about 96.8%, decision duration about 1.14 days), and compare these with prior studies of Ethereum-based DAOs. The central claim, stated in the abstract and conclusion, is that SNS DAOs exhibit sustained or increasing engagement over time, in contrast to the participation decline reported for other DAO frameworks.

Significance. If the findings hold, the paper provides a practically important and policy-relevant counterexample: a low-cost, reward-backed, delegate-able voting framework that maintains high voting-power participation. The study's strengths include a substantial on-chain dataset (over 3,000 proposals across 14 DAOs), clear metric definitions in Section III.A, and a useful comparison to prior DAO governance studies. The on-chain data collection is, in principle, reproducible. However, the current evidence does not establish the headline claims: the participation metric conflates delegated voting power with human engagement, the time-trend claim rests on visual inspection without statistical tests, and the cross-ecosystem comparisons use different denominators. These issues are load-bearing because they directly affect the abstract's main sentence, the comparison in Section IV, and the conclusion.

major comments (5)
  1. [Section III.A and II.A] The participation metric is defined as the fraction of voting power engaged relative to total registered voting power. In the SNS model described in Section II.A, a neuron votes automatically when a majority of its followees agree, and otherwise abstains. Therefore, high and stable participation can be produced by a small number of active neurons followed by many passive token holders, without broad human deliberation. The paper acknowledges concentration only qualitatively in Section III.C ('dominance by a few neurons with high voting power'), but it never reports the fraction of votes cast via following, the distribution of voting power across neurons, or the number of distinct neurons whose voting power actually moved. This matters because the abstract and conclusion repeatedly interpret the participation rate as 'community engagement.' Please either refine the terminology and claims, or provide additional data on following behavior and voting-power concentration that would let the reader assess whether the rate reflects broad engagement.
  2. [Section III.F.1 and Figure 6] The claim that 'participation rates show an overall increase' and that 'as of September 2024, all of them are higher than at launch' is based on visual inspection of monthly average participation rates. The paper reports no confidence intervals, no trend test, and no correction for multiple DAO comparisons, so the central dynamic claim is not statistically established. In addition, Section III.F.1 says 'all of them are higher than at launch,' while Section IV says 'for most cases the participation rates are higher than the initial months,' an internal inconsistency. Please provide per-DAO trend statistics (e.g., a linear or monotonic trend test with standard errors), show all DAOs rather than a subset, and reconcile the 'all' versus 'most' statements.
  3. [Sections III.B, III.C, and III.E] The headline aggregate values (participation 64.34%, approval 96.8%, decision duration 1.14 days) are not explicitly defined as unweighted DAO averages or proposal-weighted aggregates. Because proposal counts vary widely across DAOs (OpenChat alone has 966 proposals, while others have far fewer, and Table I reports no proposal count per DAO), the aggregation choice can materially change these numbers. Please define the aggregation rule explicitly and report per-DAO and per-proposal distributions, including variances or standard errors.
  4. [Section IV] The comparison of SNS participation (about 64%) with Ethereum DAO participation figures is not apples-to-apples. The SNS rate is voting power relative to total registered voting power, whereas the cited studies use different denominators: Barbereau et al. report exercised voting rights relative to token-holders, Feichtinger et al. report percentages with yet another basis, and Messias et al. use votes or delegated tokens relative to total delegated tokens. These differences in denominator are part of what drives the apparent gap. Please either recompute comparable metrics from the raw data or clearly state the metric differences and discuss how they affect the cross-platform conclusion.
  5. [Section IV, governance cost comparison] The cost comparison is computed on different accounting bases: the SNS cost of 'around 11 USD' is the total cost of all SNS canister operations (including ledger transfers and upgrades) divided by the number of executed proposals, whereas the cited Ethereum costs are per-proposal or per-vote gas costs for governance transactions. This makes the 'lower costs' claim difficult to evaluate. Please provide a defined cost model with the same scope (e.g., proposal submission plus voting) and report costs per proposal and per vote for both ecosystems.
minor comments (7)
  1. [Throughout] There are several typos and infelicities: 'adn' (Section III.F.2), 'ICSwap' (Section III.D), 'suggestions suggestions' (Section III.D), 'government' instead of 'governance' (Section III.F.2), and 'see 6 for a plot' missing the word 'Figure' (Section III.F.1).
  2. [Figures 1-4] The x-axis labels with DAO names are very small and likely unreadable in print; consider rotating labels, using larger fonts, or abbreviating names consistently.
  3. [Table I] Table I lists age, treasury, and neuron count but not the number of proposals per DAO; adding a proposal count column would help interpret the weighted versus unweighted aggregation issue and the activity comparisons.
  4. [Section III.D] The 'harmless' proposal category is used in Figures 1-4 before its composition is explained; state explicitly in Section III.A or III.D that harmless proposals are primarily Motion and SNS-Upgrade proposals.
  5. [Section III.B] The sentence 'The oldest SNS DAO investigated, OpenChat and Yral, shows moderate rate of participation' is grammatically ambiguous; OpenChat and Yral are two separate DAOs and should be presented as such.
  6. [Reproducibility] No data or code availability statement is provided; since the study is empirical, sharing the query scripts and aggregated datasets would strengthen reproducibility and transparency.
  7. [Conflicts of interest] One co-author is affiliated with DFINITY, and reference [15] includes a co-author of this paper; the manuscript would benefit from an explicit conflict-of-interest or self-citation disclosure.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: all reported metrics are direct on-chain measurements and no fitted parameter or self-citation is load-bearing.

full rationale

The paper is an observational empirical study, not a derivation. In Section III.A it defines participation rate as "the fraction of voting power engaged relative to the total voting power registered", approval rate, decision duration, and proposal frequency, and in Sections III.B through III.F these are computed directly from governance-canister proposal data for 14 SNS DAOs. The central "sustained or increasing engagement" claim is an aggregate of monthly participation-rate measurements (e.g., Figure 6), not the output of a fitted model or of an equation whose output is an input. Cross-platform comparisons in Section IV use externally published rates (Feichtinger et al., Messias et al., Barbereau et al., Rhazoui et al., Wang et al.) with different denominators; while this raises a comparability/validity question, it is not circular because the SNS numbers are measured independently and no prior SNS result is baked into the comparison. The only overlapping-author citation, [15] (Schmid and Shestakov), appears in Section II.A as background on liquid democracy and voter fatigue; it is not used to define, fit, or justify any measured quantity or conclusion. No equation is fitted, no parameter is calibrated to a subset and then "predicted", and no uniqueness theorem is imported from prior work by the same authors. The skeptical concern that the voting-power participation metric may overstate human engagement because of automatic following/delegation is a construct-validity limitation, not a circular reduction: the paper explicitly defines the metric as voting-power participation, and the claim follows from that definition by direct observation rather than by hiding the original data as a result. Under the hard rule that circularity requires quoting a specific reduction, no such step exists.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

No free parameters or invented entities appear in this empirical study. The central claims rest on three domain assumptions: sample representativeness, the validity of voting-power participation as an engagement proxy, and comparability of metrics across different DAO studies. These are dataset and measurement assumptions, not fitted model parameters.

assumptions (3)
  • domain assumption The 14 selected SNS DAOs with at least six months of history are representative of all SNS DAOs.
    Section III.A selects 14 of 29 SNS DAOs by minimum age and diversity of categories. If this subset skews toward active or successful DAOs, the sustained-engagement conclusion does not generalize.
  • domain assumption Participation rate measured as voting power of participating neurons over total registered voting power is a valid proxy for community engagement.
    Section III.A and III.B define participation this way. It weights by voting power, not by individual humans, and automatic following means one neuron can represent many; the paper acknowledges related caveats only qualitatively for small neuron counts.
  • domain assumption Metrics from prior Ethereum DAO studies are comparable to SNS metrics despite different definitions.
    Section IV compares participation, approval, duration, and proposal frequency across studies with differing definitions (e.g., Messias et al. use ratio of cast votes to delegated tokens; Feichtinger et al. use token-holder participation). No normalization or reconciliation is provided.

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Cite this review

Pith. "Pith review of Democracy for DAOs: An Empirical Study of Decentralized Governance and Dynamic (Case Study Internet Computer SNS Ecosystem)." pith.science (2026). https://pith.science/paper/P4IUNVC7

@misc{pith2026250720234,
  author       = {Pith},
  title        = {Pith review of: Democracy for DAOs: An Empirical Study of Decentralized Governance and Dynamic (Case Study Internet Computer SNS Ecosystem)},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/P4IUNVC7}},
  note         = {Machine review of arXiv:2507.20234}
}
read the original abstract

Decentralized autonomous organizations (DAOs) rely on governance mechanism without centralized leadership. This paper presents an empirical study of user behavior in governance for a variety of DAOs, ranging from DeFi to gaming, using the Internet Computer Protocol DAO framework called SNS (Service Nervous System). To analyse user engagement, we measure participation rates and frequency of proposals submission and voter approval rates. We evaluate decision duration times to determine DAO agility. To investigate dynamic aspects, we also measure metric shifts in time. We evaluate over 3,000 proposals submitted in a time frame of 20 months from 14 SNS DAOs. The selected DAO have been existing between 6 and 20 months and cover a wide spectrum of use cases, treasury sizes, and number of participants. We also compare our results for SNS DAOs with DAOs from other blockchain platforms. While approval rates are generally high for all DAOs studied, SNS DAOs show slightly more alignment. We observe that the SNS governance mechanisms and processes in ICP lead to higher activity, lower costs and faster decisions. Most importantly, in contrast to studies which report a decline in participation over time for other frameworks, SNS DAOs exhibit sustained or increasing engagement levels over time.

Figures

Figures reproduced from arXiv: 2507.20234 by the authors.

Figure 1
Figure 1. Participation Rates: average proportion of the voting power partici [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Approval Rates: average percentage of voting power in favor of [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Percentage of rejected proposals, depending on their category. [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (3 more)
Figure 6
Figure 6. Figure 6: Monthly average participation rates for some SNS DAOs, showing [PITH_FULL_IMAGE:figures/full_fig_p006_6.png]
Figure 7
Figure 7. Figure 7: Average monthly proposal frequencies, rounded to integers. Some [PITH_FULL_IMAGE:figures/full_fig_p006_7.png]
Figure 8
Figure 8. Figure 8: Average monthly proposal frequencies of a subset of DAOs. [PITH_FULL_IMAGE:figures/full_fig_p007_8.png]

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Reference graph

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