4.4 KiB
| type | title | author | url | date | domain | secondary_domains | format | status | priority | tags | |||||||
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| source | Optimism Futarchy v1 Preliminary Findings | Optimism Collective (gov.optimism.io) | https://gov.optimism.io/t/futarchy-v1-preliminary-findings/10062 | 2025-06-12 | internet-finance |
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report | unprocessed | high |
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Content
Optimism ran a 21-day futarchy experiment (March-June 2025) parallel to their traditional Grants Council process. Each method selected 5 projects to receive 100K OP grants (~500K OP total) aimed at increasing Superchain TVL over 84 days.
Participation: 430 active forecasters after filtering 4,122 suspected bots. 5,898 total trades. 88.6% were first-time Optimism governance participants. Participants spanned 10 countries across 4 continents. Average 36 new users per day. Average 13.6 transactions per person.
Selection Overlap: Both methods selected the same 2 projects (Rocket Pool and SuperForm), but diverged on 3 others. Futarchy uniquely selected: Balancer & Beets, Avantis, Polynomial. Grants Council uniquely selected: Extra Finance, Gyroscope, Reservoir.
Selection Performance: Futarchy outperformed Grants Council by $32.5M TVL increase, primarily driven by Balancer & Beets ($27.8M). However, futarchy showed higher variance — selecting both top performers and the single worst-performing project.
Prediction Accuracy (CATASTROPHIC MISS): Markets predicted aggregate TVL increase of ~$239M. Actual: ~$31M. Overshot by approximately 8x. Specific misses: Rocket Pool predicted $59.4M, actual 0; SuperForm predicted $48.5M, actual -$1.2M; Balancer & Beets predicted $47.9M, actual -$13.7M.
Contributing Factors: Play money environment created no downside risk for inflated predictions. $50M initial liquidity anchor may have skewed price discovery. Strategic voting to influence grant allocations. TVL metric conflated ETH price with project quality.
Counterintuitive Finding: Badge Holders (recognized OP governance experts) had the LOWEST win rates. Trading skill determined outcomes, not domain expertise.
Behavioral Pattern: 41% of participants hedged bets in final days to avoid losses.
Agent Notes
Why this matters: This is the most detailed empirical test of futarchy governance outside MetaDAO. The selection-vs-prediction split is the key finding — futarchy was BETTER at picking winners but TERRIBLE at estimating magnitudes. This scopes the "markets beat votes" claim. What surprised me: Badge Holders losing to traders. If domain expertise doesn't help in futarchy markets, this challenges the claim that skin-in-the-game filters for INFORMED participants — it may filter for SKILLED traders instead. What I expected but didn't find: Real-money results. This was play money, which is the biggest confound. No data on whether v2 with real stakes is planned. KB connections: Directly challenges speculative markets aggregate information through incentive and selection effects not wisdom of crowds — the selection effect worked but only for ordinal ranking. Also relevant to MetaDAOs futarchy implementation shows limited trading volume in uncontested decisions — Optimism saw 88.6% first-time participants, suggesting futarchy CAN attract engagement. Extraction hints: Key claim candidate: "Futarchy excels at relative selection but fails at absolute prediction because the mechanism's strength is ordinal ranking weighted by conviction, not cardinal estimation." Also: "Play-money futarchy attracts participation but produces uncalibrated predictions because the absence of downside risk removes the selection pressure that makes markets accurate." Context: This was Optimism Season 7. The Uniswap Foundation co-sponsored. Butter operated the prediction markets. The experiment used conditional tokens (pass/reject) for 23 grant candidates, selecting the top 5 forecast to boost Superchain TVL most.
Curator Notes (structured handoff for extractor)
PRIMARY CONNECTION: speculative markets aggregate information through incentive and selection effects not wisdom of crowds WHY ARCHIVED: First large-scale futarchy experiment outside MetaDAO reveals critical selection-vs-prediction distinction not captured in existing KB EXTRACTION HINT: Focus on the selection-vs-prediction distinction and what it means for mechanism design — this is a scoping claim that refines existing beliefs