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REVIEW 3 major objections 6 minor 71 references

In DAO ballots, the option the proposal author picks gets about 59 points more voting-power share than similar options; approval stance and first-list position trail it.

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2026-07-13 03:00 UTC pith:SMR2FO6U

load-bearing objection Solid large-scale Snapshot decomposition: author-selected choices dominate voting-power share, residual after author-VP removal is real but not cleanly causal. the 3 major comments →

arxiv 2607.09435 v1 pith:SMR2FO6U submitted 2026-07-10 cs.CY cs.HCecon.GNq-fin.EC

Voting Biases in Decentralized Autonomous Organization (DAO) Governance

Stefano Balietti , Pietro Saggese , Markus Strohmaier This is my paper
classification cs.CY cs.HCecon.GNq-fin.EC
keywords Decentralized Autonomous OrganizationDAOGovernanceBiasBlockchainVotingCollective decision-makingDeFi
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

Decentralized Autonomous Organizations vote with tokens on Snapshot, yet support often piles onto one side. This paper asks what features of the ballot itself track that concentration. At the level of each choice on a proposal, it links voting-power share to three observables: whether the choice is approval-oriented, where it sits in the ordered list, and whether the proposal author selected it. In the main single-preference sample, author-selected choices show the largest conditional association—about 58.8 percentage points more share than otherwise similar non-author choices—followed by approval-oriented stance (about 27.1 points) and first-versus-second list position (about 7.7 points). The author-choice pattern remains large after many robustness checks, including recomputing shares after removing the author’s own voting power. The authors treat “bias” as a descriptive label for systematic association, not proven causal distortion, and argue that choice order, author signals, and vote visibility are institutional design choices rather than neutral interface details.

Core claim

On Snapshot single-preference proposals, conditional on stance, position, space fixed effects, and related covariates, an author-selected choice is associated with roughly 58.8 percentage points higher voting-power share than a comparable non-author choice; approving stance adds about 27.1 points and first-listed versus second-listed about 7.7 points. On the author-voted subsample the author-choice average marginal effect falls from about 48 to about 33 points after subtracting the author’s own voting power but does not disappear. The ordering of effects holds across a wide robustness grid.

What carries the argument

Choice-level fractional-logit models of voting-power share (with space-clustered errors and average marginal effects on the response scale), using three indicators—author selection, approval stance (from a multi-stage heuristic classifier validated against LLMs and crowdworkers), and list position—plus diagnostics that recompute shares after removing author voting power.

Load-bearing premise

The analysis only sees proposals that already reached a formal Snapshot ballot, so pre-vote screening and unmeasured proposal quality can still drive part of the author-choice association even after author votes are subtracted.

What would settle it

If, within proposals and after removing author voting power, randomizing choice order and masking which option the author selected erased the residual author-choice and first-position share advantages while leaving stance effects intact, the claimed interface and author-signal associations would not hold as design-relevant ballot features.

Watch this falsifier — get emailed when new claim-graph text bears on it.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 6 minor

Summary. The paper studies Snapshot DAO governance at the proposal-choice level, linking each choice’s voting-power share to three observable features: list position, approval-oriented stance, and whether the proposal author selected the choice. Using cleaned data on ~127k proposals and fractional-logit models with space clustering and extensive robustness checks, it reports average marginal effects of about 58.8 pp for author-selected choices, 27.1 pp for approving stance, and 7.7 pp for first-versus-second position in the main single-preference sample. A residual author-choice association remains after subtracting the author’s own voting power (AME falls from 47.92 to 32.56 pp on the author-voted subsample). Stance labels are validated against LLMs and crowdworkers. The authors treat “bias” as descriptive association, not proven causal distortion, and argue that ordering, author signals, and vote visibility should be treated as institutional design choices.

Significance. If the reported associations hold, the paper supplies large-scale, choice-level evidence that concentrated support in DAO voting is systematically related to ballot presentation and author selection, not only to token-weighted aggregation. That is a genuine contribution relative to prior work that documents high approval rates and token concentration without decomposing within-ballot features. Strengths include the proposal-choice unit of analysis, multi-method stance validation (heuristics vs Llama/GPT/humans, κ≈0.61–0.71), favorite-choice vs support-fraction mappings, nested fractional-logit specifications, space clustering, and the author-VP removal diagnostic. The design discussion is useful for platform and DAO practitioners even if causal channels remain open. The contribution is empirical and institutional rather than theoretical, but it is well placed in cs.CY / digital-governance research.

major comments (3)
  1. [§5.2 Table 6; §6 Implications] Table 6 and §5.2–§6: The residual author-choice AME after removing author voting power (47.92→32.56 pp) is the load-bearing result for treating author selection as more than mechanical self-voting. The residual can still reflect selection of already-favored options (proposal quality, pre-vote screening, coordination, expertise) that stance, rank, and space FE do not absorb (Limitations; §2 positive-outcome skew; Table 3 overlap). Please tighten the abstract and implications so design recommendations about “author signals” are explicitly conditional on this residual not being pure quality selection, and add a clearer mechanism discussion of what the residual can and cannot identify.
  2. [§5 Table 3; Table 5] Table 3 and Table 5: First-position choices are also disproportionately approving and author-selected (e.g., author-choice mean 0.73 at rank 1). Nested fractional logits separate conditional associations, but collinearity limits sharp attribution of distinct “channels.” Report variance-inflation or joint-exclusion diagnostics, and state more explicitly which contrasts remain identified when author×position and author×stance splits (Table 6) are the preferred evidence rather than the pooled three-way decomposition.
  3. [Figure 5; Table 17; §6] Figure 5 / Table 17 TVL-25/50/100 rows: On top DeFi protocols the author AME falls sharply (≈27–33 pp) and can sit below the approve-stance AME, with wide CIs. The text notes this but the headline “author strongest” claim is sample-dependent. Either qualify the main claim as holding primarily outside the high-TVL subsample, or provide a short analysis of why high-TVL spaces differ (participation, professionalization, visibility) so the ordering is not over-generalized.
minor comments (6)
  1. [Abstract] Abstract reports “58.8% increase” while the body reports 58.8 percentage-point AMEs; use consistent percentage-point language throughout.
  2. [§4 Fig. 1] Figure 1 pools proposals with different choice counts so shares need not sum to one; the caption notes this, but a brief reminder in the main text would help readers.
  3. [§1–§2] Clarify early that “bias” is used only descriptively (as in the abstract) whenever the triad is introduced in §1–§2, to avoid causal reading of “author bias.”
  4. [Table 4; Fig. 5] Table 4 lists “Compat full” / “Expanded” naming that does not fully match the short names used in Figure 5; align labels across table and figure.
  5. [§3.1; Appendix C] Appendix stance registries are thorough; a short main-text note on multilingual coverage and the Approve+Reject joint-label rule would help non-appendix readers assess measurement risk.
  6. [§6; Appendix] Minor typos: “a a 58.8” in §6; “ressults” in Appendix D.4; check “againist/aganist” only appear as intentional keyword variants.

Circularity Check

0 steps flagged

No circular derivation: headline AMEs are observational associations estimated from Snapshot ballots, not quantities forced by definition, fitted constants, or self-citation.

full rationale

The paper’s load-bearing claims are response-scale average marginal effects from fractional-logit models of choice-level voting-power share on three observed indicators (author selection, approval stance, list rank), with space clustering and extensive sample/specification robustness (Tables 5–6, Fig. 5, Table 17). Voting-power share is constructed from token-weighted votes; author choice, stance, and position are separately coded features of the ballot—not algebraic rewrites of the outcome. Stance labels come from a documented heuristic pipeline validated against LLMs and crowdworkers (Cohen’s κ ≈ 0.61–0.71), not from the vote totals being explained. The residual author-choice AME after subtracting the author’s own voting power (Table 6: 47.92 → 32.56 pp) is an empirical sensitivity, not a tautology: the indicator remains favorite-choice author selection while the outcome is recomputed without author mass. Self-citations (e.g., prior DAO/governance work by overlapping authors) supply background and data-cleaning conventions, not uniqueness theorems or ansatzes that force the 58.8/27.1/7.7 pp ordering. The authors explicitly treat “bias” as descriptive association, not causal distortion. Selection/endogeneity concerns (pre-vote screening, unmeasured quality) affect causal interpretation, not circularity of the reported associations. No step reduces a claimed prediction or first-principles result to its own inputs by construction.

Axiom & Free-Parameter Ledger

4 free parameters · 4 axioms · 1 invented entities

The paper is empirical social science: load-bearing content is measurement and modeling choices on observational Snapshot ballots, not free physical constants or new particles. The central claim rests on treating voting-power share as the outcome, on heuristic/LLM/human stance labels, on favorite-choice expansion of multi-preference ballots, and on fractional-logit conditional associations with space clustering—not on a closed-form derivation.

free parameters (4)
  • Stance keyword registry and tier rules (exact/prefix/suffix/contains)
    Curated multi-language keyword lists and conflict rules define Approve/Reject/Abstain/Other; validated but still a hand-built measurement system that can shift the approval indicator.
  • Favorite-choice vs support-fraction ballot-mass mapping
    How multi-preference votes are expanded into choice-level mass affects author-choice definition and shares; authors report small overall mass differences but fix favorite-choice for the author indicator.
  • Maturity and cleaning thresholds (e.g., ≥5 followers, >2 voters, >1 proposal; final/flagged filters)
    Sample construction rules determine which spaces/proposals enter the analytic set (~41–43% reduction from raw).
  • Author-choice visibility window of first six interface positions
    UI-constrained covariate for whether author choice is among the first six listed options; interacts with author choice in nested models.
axioms (4)
  • domain assumption Voting-power share (and excess vs uniform-within-proposal) is the appropriate outcome for comparing choices across heterogeneous DAO weighting rules.
    Stated in §4 Measures; justified by high correlation of votes and voting power but still a modeling choice.
  • domain assumption Fractional logit with quasi-binomial link and DAO-space clustered SEs (plus space or proposal FE in robustness) separates conditional associations of author, stance, and rank despite collinearity.
    §5.1 Model Design; standard for fractional outcomes but does not identify causal channels.
  • ad hoc to paper A proposal must contain both Approve and Reject for those labels to apply; one-sided proposals are recoded Other.
    Explicit classification rule in §3 and Appendix C; affects approval-bias measurement.
  • domain assumption “Bias” denotes systematic association with voting-power share, not proven causal distortion of preferences.
    Abstract and Introduction; scopes all headline percentages.
invented entities (1)
  • Descriptive triad of position / approval / author “bias” indicators at the proposal-choice level independent evidence
    purpose: Operationalize presentation, stance, and author-signal channels for Snapshot ballots
    Not new physical entities; constructed binary/rank features from list order, stance labels, and reconstructed author selection. Independent evidence is the external literature on order effects, approval skew, and reputation, not a new object.

pith-pipeline@v1.1.0-grok45 · 39812 in / 3077 out tokens · 41175 ms · 2026-07-13T03:00:25.776935+00:00 · methodology

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read the original abstract

Decentralized Autonomous Organizations (DAOs) use token-weighted voting to allocate resources, set protocol rules, and legitimate collective decisions. Yet, support in DAO voting is strikingly concentrated. What happens inside the ballot that produces this concentration? We study DAOs' governance at the proposal-choice level, linking each choice's voting-power share to three observable features: whether it expresses an approval-oriented stance, where it appears in the choice list, and whether it is selected by the proposal author. We find that (i) author-selected choices show the strongest and most robust association with voting-power share, with a 58.8% increase relative to non-author choices; (ii) approval-oriented choices retain a positive but slightly less consistent advantage (27.1%); and (iii) first-listed choices also attract systematically higher shares, consistent with position and order effects (7.7%). Results are robust across several specifications, which include subtracting an author's own voting power from computations. We use bias descriptively, to denote systematic associations rather than proven causal distortion. The results shift attention from proposal outcomes alone to the interface and social signals through which choices are presented. In DAO governance, ordering, author signals, and vote visibility should be treated as institutional design choices, not neutral implementation details.

Figures

Figures reproduced from arXiv: 2607.09435 by Markus Strohmaier, Pietro Saggese, Stefano Balietti.

Figure 1
Figure 1. Figure 1: Share and excess of choice-level voting power for the three indicators under study. A. Outcome: mean voting-power share for choices that are first-listed, approval-oriented, or selected by the proposal author. B. Outcome: mean excess voting power relative to a uniform￾within-proposal benchmark. Error bars show 95% confidence intervals for mean estimates. Across both measures, the choice associated with eac… view at source ↗
Figure 2
Figure 2. Figure 2: A shows how the share of voting power changes across proposals with different numbers of choices. The association is strongest on proposals with two and three choices; in the latter, the first choice captures around 80% of total voting power. These proposals often follow an approve/reject pattern, as in the Basic voting system, which always has exactly three choices. Fig. 2B shows the excess voting power f… view at source ↗
Figure 3
Figure 3. Figure 3: Choice stance, list position, and excess voting power. Excess voting power is plotted separately for each stance and by choice position. Excess is relative to a uniform-within-proposal benchmark. Error bars show 95% confidence intervals for mean estimates. All stances obtain a boost in voting power when placed in position 1, but the Approve stance obtains the largest gain and has limited deficit at other p… view at source ↗
Figure 4
Figure 4. Figure 4: B compares approval stance and author selection, highlighting two descriptive patterns. First, even when disaggregated by stance, author-selected choices show positive excess voting power across positions, although the size varies. Second, the author boost is stable across all choice positions only for the Approve stance, while it is decreasing for Reject and Other stances (see also Fig. S12); interestingl… view at source ↗
Figure 5
Figure 5. Figure 5: Three-bias average marginal effects (AMEs) across main sample and robustness specifications. Points show response-scale AMEs in percentage points; horizontal intervals are Bonferroni FWER-adjusted across the 36 shared AMEs. Main corresponds to the primary speci￾fications; others are robustness checks. Across all ten robustness specifications, the author-choice AME exceeds both the position effect (first vs… view at source ↗
Figure 6
Figure 6. Figure 6: Timeline of a DAO governance process. The decision-making process can be divided into three time windows: the pre-voting period, the voting period, and the post-voting period. A Decentralized Autonomous Organizations [PITH_FULL_IMAGE:figures/full_fig_p025_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Agreement across stance classification methods. Bars count source-specific non￾missing choice-level stance labels for the heuristic, Llama 3.1, Llama 3.3, OAI, and ChatGPT sources. Labels are counted within each source-specific denominator and are limited to Approve, Reject, Abstain, and Other. posal or as a no-suggestion decision. When an automated model does detect a positive suggested stance, the stance… view at source ↗
Figure 8
Figure 8. Figure 8: Suggested-stance labels by source. Bars show the share of proposals for which the source found no suggested voting direction or found a textual cue suggesting Approve, Reject, Abstain, or Other. Details about human labels are available in Sec. C.3, but anticipate here the relevant result [PITH_FULL_IMAGE:figures/full_fig_p037_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Choice stance counts by number of choices in a proposal (full sample) [PITH_FULL_IMAGE:figures/full_fig_p049_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Voting power and position by stance (full sample). A. Mean choice-level voting￾power share by stance. B. Share of choices with a given stance that are in position 1, by number of choices in the proposal. These shares are conditional on stance and therefore do not describe the overall distribution of stances. Confidence intervals are 95% confidence intervals of the means [PITH_FULL_IMAGE:figures/full_fig_… view at source ↗
Figure 11
Figure 11. Figure 11: Excess voting power by stance, choice position, and number of choices (full sample). Only stance-position-num-choice combinations with more than 10 observations are kept in the plot. Error bars are 95% confidence intervals of the means [PITH_FULL_IMAGE:figures/full_fig_p051_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Author boost by stance and choice position. Each dot in the plot is the difference between the point estimate of the average excess voting power of a choice of an author vs other choices. Error bars are 95% confidence intervals of the means [PITH_FULL_IMAGE:figures/full_fig_p052_12.png] view at source ↗

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