pipeline: clean 3 stale queue duplicates
Pentagon-Agent: Epimetheus <3D35839A-7722-4740-B93D-51157F7D5E70>
This commit is contained in:
Teleo Agents
parent
cbd90ee0ea
commit
4d76c58172
3 changed files with 0 additions and 280 deletions
|
|
@ -1,92 +0,0 @@
|
|||
---
|
||||
type: source
|
||||
title: "Superforecasters vs. Prediction Markets: Calibration-Selection Mechanism Can Be Replicated, Information-Acquisition Mechanism Cannot"
|
||||
author: "Atanasov, Mellers, Tetlock et al. (multiple papers)"
|
||||
url: https://pubsonline.informs.org/doi/10.1287/mnsc.2015.2374
|
||||
date: 2026-03-22
|
||||
domain: internet-finance
|
||||
secondary_domains: [ai-alignment, collective-intelligence]
|
||||
format: article
|
||||
status: enrichment
|
||||
priority: high
|
||||
tags: [prediction-markets, superforecasters, epistemic-mechanism, skin-in-the-game, belief-1, disconfirmation, academic, mechanism-design]
|
||||
processed_by: rio
|
||||
processed_date: 2026-03-22
|
||||
enrichments_applied: ["Polymarket vindicated prediction markets over polling in 2024 US election.md"]
|
||||
extraction_model: "anthropic/claude-sonnet-4.5"
|
||||
---
|
||||
|
||||
## Content
|
||||
|
||||
Synthesis of the Atanasov/Mellers/Tetlock prediction market vs. calibrated poll literature, with focus on the two-mechanism distinction this session surfaced.
|
||||
|
||||
**Primary sources:**
|
||||
1. Atanasov, Witkowski, Mellers, Tetlock (2017), "Distilling the Wisdom of Crowds: Prediction Markets vs. Prediction Polls," *Management Science* Vol. 63, No. 3, pp. 691–706
|
||||
2. Mellers, Ungar, Baron, Ramos, Gurcay, Fincher, Scott, Moore, Atanasov, Swift, Murray, Stone, Tetlock (2015), "Psychological Strategies for Winning a Geopolitical Forecasting Tournament," *Perspectives on Psychological Science*
|
||||
3. Atanasov, Witkowski, Mellers, Tetlock (2024), "Crowd Prediction Systems: Markets, Polls, and Elite Forecasters," *International Journal of Forecasting*
|
||||
4. Mellers, McCoy, Lu, Tetlock (2024), "Human and Algorithmic Predictions in Geopolitical Forecasting," *Perspectives on Psychological Science*
|
||||
|
||||
**Core finding (2017/2024):** When polls are combined with skill-based weighting algorithms (tracking prior performance and behavioral patterns), team polls match or exceed prediction market accuracy for geopolitical event forecasting. Small elite crowds (superforecasters) outperform large crowds; markets and elite-aggregated polls are statistically tied.
|
||||
|
||||
**IARPA ACE tournament results:**
|
||||
- GJP (Good Judgment Project) beat all research teams by 35–72% (Brier score)
|
||||
- Beat intelligence community's internal prediction market by 25–30%
|
||||
- Top superforecaster Year 2: Brier score 0.14 vs. random guessing 0.53
|
||||
- Year-to-year top forecaster correlation: 0.65 (skill is real, not luck)
|
||||
|
||||
**The mechanism explanation (critical for claim extraction):**
|
||||
|
||||
Financial markets up-weight skilled participants via earnings. Calibration algorithms replicate this function by tracking performance and assigning higher weight to historically accurate forecasters. Both methods are solving the same problem: suppress noise from poorly-calibrated participants, amplify signal from well-calibrated ones.
|
||||
|
||||
**This is Mechanism A: Calibration selection.** Polls can match markets here because the mechanism is reducible to participant weighting — no financial incentive required.
|
||||
|
||||
**Mechanism B: Information acquisition and strategic revelation.** Financial stakes incentivize participants to acquire costly private information (research, due diligence, insider access) and to reveal it through trades. Disinterested poll respondents have no incentive to acquire costly private information or to reveal it honestly if they hold it. GJP superforecasters work with publicly available information — the IARPA ACE tournament explicitly restricted access to classified sources. The research was not designed to test whether polls match markets in information-asymmetric contexts.
|
||||
|
||||
**Scope of the finding:**
|
||||
- All tested events: geopolitical (binary outcomes, months-ahead, objective resolution, publicly available information)
|
||||
- "Algorithm-unfriendly domain" (Mellers 2024) — hard-to-quantify data, elusive reference classes, non-repeatable contexts
|
||||
- No test in financial selection contexts (stock returns, ICO quality, startup success)
|
||||
- No test in information-asymmetric contexts where participants have strategic reasons to conceal private information
|
||||
|
||||
**Good Judgment Project track record extension (non-geopolitical):**
|
||||
- Fed policy prediction: GJP reportedly outperformed futures markets by 66% at Fed policy inflection points (Financial Times, July 2024)
|
||||
- Federal Reserve FEDS paper (Diercks/Katz/Wright, 2026): Kalshi real-money markets beat Bloomberg consensus for headline CPI; perfectly matched realized fed funds rate on FOMC day
|
||||
- Both findings consistent: elite forecasters AND real-money markets beat naive consensus; neither outperforms the other on structured macro-event prediction
|
||||
|
||||
**What has not been tested:** Stock return prediction, venture capital selection, ICO quality evaluation, or any financial selection task where the question is not "will event X happen" but "is asset Y worth more than price Z."
|
||||
|
||||
## Agent Notes
|
||||
|
||||
**Why this matters:** This resolves the multi-session threat to Belief #1 from Mellers et al. The challenge was real but domain-scoped. Skin-in-the-game markets have two separable mechanisms — Mellers only tested the one that polls can replicate. The one polls can't replicate (information acquisition and strategic revelation) is exactly what matters for futarchy in financial selection.
|
||||
|
||||
**What surprised me:** The 2024 update explicitly calls geopolitical forecasting an "algorithm-unfriendly domain" — distinguishing it from financial forecasting where algorithmic approaches have richer structured data. The Mellers team themselves implicitly acknowledge the domain transfer problem.
|
||||
|
||||
**What I expected but didn't find:** Any study testing calibrated polls vs. prediction markets for financial selection (ICO evaluation, startup quality, investment return). The gap in the literature is almost total on this question. The Optimism futarchy experiment (conditional prediction markets for grant selection) is the closest thing, and it failed — but for implementation reasons.
|
||||
|
||||
**KB connections:**
|
||||
- [[speculative markets aggregate information more accurately than expert consensus or voting systems]] — this claim needs the two-mechanism distinction added to be precise
|
||||
- FairScale case (Session 4): Mechanism B failure — fraud detection requires off-chain due diligence that market participants weren't incentivized to find
|
||||
- Trove Markets fraud (Session 8): Same pattern — Mechanism B failure, not Mechanism A
|
||||
- Participation concentration (70% top 50): Mechanism A is working fine (50 calibrated participants selecting); the question is whether Mechanism B is generating information acquisition from those participants
|
||||
|
||||
**Extraction hints:**
|
||||
- PRIMARY CLAIM CANDIDATE: "Skin-in-the-game markets have two separable epistemic mechanisms with different replaceability" — the calibration-selection mechanism can be replicated by calibrated aggregation; the information-acquisition mechanism cannot. This distinction determines when prediction markets are epistemically necessary.
|
||||
- SECONDARY CLAIM: "Prediction market accuracy advantages over polls are domain-dependent — competitive polls can match market accuracy in public-information-synthesis contexts but not in information-asymmetric selection contexts"
|
||||
- ENRICHMENT TARGET: [[speculative markets aggregate information more accurately than expert consensus or voting systems]] — add two-mechanism scope qualifier
|
||||
|
||||
**Context:** This research addresses the core "why do markets work" question that the futarchy thesis depends on. Mellers et al. is the most-cited academic challenge to prediction market epistemic superiority. Resolving it with a scope mismatch rather than a refutation is a significant outcome for the KB's claim structure.
|
||||
|
||||
## Curator Notes
|
||||
|
||||
PRIMARY CONNECTION: [[speculative markets aggregate information more accurately than expert consensus or voting systems]]
|
||||
WHY ARCHIVED: Resolves the Session 8 challenge to Belief #1; establishes the two-mechanism distinction that reframes multiple existing claims about futarchy's epistemic properties
|
||||
EXTRACTION HINT: The claim to extract is the two-mechanism distinction, not just a summary of the academic findings. Focus on Mechanism A (calibration-selection, replicable by polls) vs. Mechanism B (information-acquisition, not replicable). The finding is architecturally important — it should affect multiple existing claims as enrichments.
|
||||
|
||||
|
||||
## Key Facts
|
||||
- GJP beat all IARPA ACE research teams by 35-72% (Brier score)
|
||||
- GJP beat intelligence community's internal prediction market by 25-30%
|
||||
- Top superforecaster Year 2 Brier score: 0.14 vs. random guessing 0.53
|
||||
- Year-to-year top forecaster correlation: 0.65
|
||||
- GJP reportedly outperformed futures markets by 66% at Fed policy inflection points (Financial Times, July 2024)
|
||||
- Kalshi real-money markets beat Bloomberg consensus for headline CPI and matched realized fed funds rate on FOMC day (Fed FEDS paper, Diercks/Katz/Wright, 2026)
|
||||
|
|
@ -1,118 +0,0 @@
|
|||
---
|
||||
type: source
|
||||
title: "CFTC ANPRM 40-Question Breakdown: Futarchy Governance Markets Absent — Comment Opportunity Before April 30"
|
||||
author: "Norton Rose Fulbright, Morrison Foerster, WilmerHale, Crowell & Moring, Morgan Lewis (law firm analyses)"
|
||||
url: https://www.nortonrosefulbright.com/en/knowledge/publications/fed865b0/cftc-advances-regulatory-framework-for-prediction-markets
|
||||
date: 2026-03-22
|
||||
domain: internet-finance
|
||||
secondary_domains: []
|
||||
format: article
|
||||
status: enrichment
|
||||
priority: high
|
||||
tags: [cftc, anprm, prediction-markets, regulation, futarchy, governance-markets, comment-period, advocacy, RIN-3038-AF65]
|
||||
processed_by: rio
|
||||
processed_date: 2026-03-22
|
||||
enrichments_applied: ["futarchy-governed entities are structurally not securities because prediction market participation replaces the concentrated promoter effort that the Howey test requires.md"]
|
||||
extraction_model: "anthropic/claude-sonnet-4.5"
|
||||
---
|
||||
|
||||
## Content
|
||||
|
||||
Synthesis of multiple law firm analyses (Norton Rose Fulbright, Morrison Foerster, WilmerHale, Crowell & Moring, Morgan Lewis) of the CFTC ANPRM on prediction markets (RIN 3038-AF65, 91 FR 12516, comment deadline ~April 30, 2026).
|
||||
|
||||
The full 40-question structure was reconstructed from these law firm analyses (the Federal Register PDF remains inaccessible via web fetch). Previous archives covered the docket numbers and high-level category structure; this source adds the specific question content.
|
||||
|
||||
**Six question categories:**
|
||||
|
||||
**Category 1: DCM Core Principles (~Questions 1-12)**
|
||||
- How should Core Principle 2 (impartial access) apply to prediction markets?
|
||||
- Are existing manipulation rules appropriate, or do event contracts require bespoke standards?
|
||||
- What contract resolution criteria and dispute resolution procedures are appropriate?
|
||||
- What market surveillance and enforcement mechanisms are needed?
|
||||
- Should position limits apply? How should aggregation work across similar event contracts?
|
||||
- Should prediction markets be permitted to use margin (departing from fully-collateralized model)?
|
||||
- How do DCO and SEF core principles apply?
|
||||
- What swap data reporting requirements apply?
|
||||
- **Critical: "Are there any considerations specific to blockchain-based prediction markets?"** — only explicit crypto/DeFi question in the entire ANPRM.
|
||||
|
||||
**Category 2: Public Interest Determinations — CEA Section 5c(c)(5)(C) (~Questions 13-22)**
|
||||
- What factors should inform public interest analysis? (price discovery, market integrity, fraud protection, responsible innovation)
|
||||
- **Should elements of the repealed "economic purpose test" be revived for event contracts?** — directly relevant to futarchy
|
||||
- For the five prohibited activity categories:
|
||||
- Unlawful activity: How resolve federal/state law conflicts?
|
||||
- Terrorism: Does cyberterrorism qualify?
|
||||
- Assassination
|
||||
- War: Distinguish war from civil unrest?
|
||||
- **Gaming: (most extensive treatment) Does gaming = gambling? What characteristics distinguish them? What role do participant demographics play? What responsible gaming standards apply?** — key differentiation opportunity for futarchy
|
||||
- What role do event contracts play in hedging and price risk management?
|
||||
- What is the relationship between event contracts and insurance contracts?
|
||||
|
||||
**Category 3: Procedural Aspects (~Questions 23-28)**
|
||||
- At what point in the listing process should a public interest determination occur?
|
||||
- Can the Commission act when a contract application is "reasonably expected but not yet filed"?
|
||||
- Category-level vs. contract-by-contract determinations?
|
||||
- What does it mean for an event contract to "involve" one of the listed activities?
|
||||
|
||||
**Category 4: Inside Information (~Questions 29-32)**
|
||||
- Is asymmetric information utility different in prediction markets versus other derivatives?
|
||||
- Does the answer vary by event type (sports vs. political vs. financial)?
|
||||
- **How should scenarios where a single individual or small group can control the outcome be handled?** — relevant to small DAO governance where a large token holder can determine outcomes
|
||||
- What cross-market manipulation risks exist?
|
||||
|
||||
**Category 5: Contract Types and Other Issues (~Questions 33-40)**
|
||||
- How should event contracts be classified as swaps versus futures?
|
||||
- What idiosyncratic risks differentiate event contracts?
|
||||
- Does the "excluded commodity" definition apply to event contract underlyings?
|
||||
- What are cost-benefit considerations?
|
||||
- What types of event contracts beyond the enumerated categories raise public interest concerns?
|
||||
|
||||
**ANPRM structural observations:**
|
||||
- All 40 questions are framed around sports/entertainment events and CFTC-regulated exchanges
|
||||
- No mention of futarchy, DAO governance, corporate decision markets, DeFi prediction protocols
|
||||
- No treatment of decentralized prediction market infrastructure that cannot comply with exchange-licensing requirements
|
||||
- Complete silence on governance market category
|
||||
|
||||
**The comment opportunity map (most impactful question clusters for futarchy):**
|
||||
|
||||
1. **Entry point**: Blockchain-based prediction markets question → establish that on-chain governance markets are categorically different from DCM-listed sports events; they cannot seek advance approval because outcomes are determined by token holder participation, not external events.
|
||||
|
||||
2. **Economic purpose test revival**: Futarchy governance markets have the strongest economic purpose argument of any event contract category — they ARE the governance mechanism, not merely commentary on external events. Token holders are hedging their actual economic exposure to protocol decisions, not speculating on events they don't influence.
|
||||
|
||||
3. **Gaming distinction**: Futarchy governance markets fail every characteristic of gambling — no house, no odds against the bettor, participants have direct economic interest in outcome, outcome affects their actual asset value, and the mechanism serves the corporate governance function recognized by state law. This is the argument the CFTC needs to hear to prevent the default classification from applying.
|
||||
|
||||
4. **Inside information / single actor control**: The small-DAO governance context creates a special case — large token holders legitimately have both private information AND economic interests aligned with governance outcomes. The "inside information" framing that applies to sports (referee corruption) doesn't map cleanly to governance markets where participant control is a feature, not a bug.
|
||||
|
||||
## Agent Notes
|
||||
|
||||
**Why this matters:** The CFTC is building the first regulatory framework for prediction markets without anyone having told them that prediction markets ARE being used as governance mechanisms for $57M+ in assets under futarchy governance (MetaDAO ecosystem). The resulting rule will apply default treatment — probably some version of the gaming classification — unless someone files comments distinguishing the governance category. April 30 is the only near-term opportunity.
|
||||
|
||||
**What surprised me:** Five major law firms analyzed the ANPRM in detail and NONE mentioned futarchy, DAO governance markets, or corporate decision-making applications. The legal community tracking this is 100% focused on the sports/entertainment use case. The governance application is invisible to the regulatory conversation.
|
||||
|
||||
**What I expected but didn't find:** Any discussion of the distinction between "event contracts that observe external outcomes" and "event contracts that govern internal outcomes." This is the fundamental difference between Kalshi sports markets (passive prediction) and MetaDAO governance markets (active governance). The ANPRM framework doesn't acknowledge the distinction exists.
|
||||
|
||||
**KB connections:**
|
||||
- [[futarchy-governed entities are structurally not securities because prediction market participation replaces the concentrated promoter effort that the Howey test requires]] — the gaming classification track is a SEPARATE regulatory risk from securities classification; the ANPRM silence means no safe harbor from gaming classification even if the Howey defense holds
|
||||
- [[futarchy solves the trustless joint ownership problem by making conditional token swaps the mechanism for governance participation]] — the specific mechanism of conditional token swaps in governance is categorically different from futures/swaps on external events; this distinction needs to reach the CFTC
|
||||
- Session 3 research journal: "Express preemption gap in CEA is the structural root cause of all prediction market litigation" — a CFTC comment can't fix preemption, but it can establish that governance markets are a distinct category deserving different analysis
|
||||
|
||||
**Extraction hints:**
|
||||
- CLAIM CANDIDATE: "CFTC ANPRM silence on futarchy governance markets creates default gaming classification risk that active comment filing can mitigate" — time-sensitive; comment deadline April 30, 2026
|
||||
- ENRICHMENT TARGET: [[futarchy-governed entities are structurally not securities...]] — add ANPRM gaming classification vector as secondary regulatory risk not addressed by the securities analysis
|
||||
- ADVOCACY FLAG: This is not just a research finding — there's a concrete action available: filing a comment distinguishing governance markets from sports/entertainment event contracts. Flag for Cory decision.
|
||||
|
||||
**Context:** The five law firms whose analyses were consulted (NRF, MoFo, WilmerHale, DWT, C&M) are focused on their existing clients (Kalshi, Polymarket, sports prediction platforms). The MetaDAO/futarchy use case has no legal counsel tracking the ANPRM. This is both a gap and an opportunity.
|
||||
|
||||
## Curator Notes
|
||||
|
||||
PRIMARY CONNECTION: [[futarchy-governed entities are structurally not securities because prediction market participation replaces the concentrated promoter effort that the Howey test requires]]
|
||||
WHY ARCHIVED: Specific regulatory advocacy opportunity (April 30 comment deadline) with concrete question-by-question entry points for futarchy distinction argument; fills gap in WilmerHale archive's question-level detail
|
||||
EXTRACTION HINT: Two claims to extract: (1) the ANPRM silence / default risk observation, (2) the specific economic-purpose-test and gaming-distinction arguments available to futarchy governance markets. Time-sensitive — comment deadline April 30, 2026.
|
||||
|
||||
|
||||
## Key Facts
|
||||
- CFTC ANPRM RIN 3038-AF65 contains 40 questions across six categories
|
||||
- Comment deadline for CFTC ANPRM is approximately April 30, 2026
|
||||
- Five major law firms (Norton Rose Fulbright, Morrison Foerster, WilmerHale, Crowell & Moring, Morgan Lewis) analyzed the ANPRM
|
||||
- ANPRM includes only one explicit crypto/DeFi question: 'Are there any considerations specific to blockchain-based prediction markets?'
|
||||
- ANPRM Questions 13-22 address public interest determinations including potential revival of economic purpose test
|
||||
- ANPRM Questions 29-32 address inside information and scenarios where single individuals or small groups can control outcomes
|
||||
|
|
@ -1,70 +0,0 @@
|
|||
---
|
||||
type: source
|
||||
title: "Federal Reserve Study: Kalshi Prediction Markets Outperform Bloomberg Consensus for CPI Forecasting"
|
||||
author: "Diercks, Katz, Wright — Federal Reserve Board (FEDS Paper)"
|
||||
url: https://www.fool.com/investing/2026/03/16/federal-reserve-research-kalshi-prediction-markets/
|
||||
date: 2026-03-16
|
||||
domain: internet-finance
|
||||
secondary_domains: []
|
||||
format: article
|
||||
status: enrichment
|
||||
priority: medium
|
||||
tags: [prediction-markets, kalshi, federal-reserve, cpi, accuracy, academic, markets-beat-consensus, macro-forecasting]
|
||||
processed_by: rio
|
||||
processed_date: 2026-03-22
|
||||
extraction_model: "anthropic/claude-sonnet-4.5"
|
||||
---
|
||||
|
||||
## Content
|
||||
|
||||
A Federal Reserve Board paper (authors: Diercks, Katz, Wright) published March 2026 evaluates the predictive accuracy of Kalshi prediction markets for macroeconomic indicators relative to Bloomberg consensus surveys.
|
||||
|
||||
**Key findings:**
|
||||
1. Kalshi markets provided "statistically significant improvement" over Bloomberg consensus for headline CPI prediction
|
||||
2. Kalshi markets were at parity with Bloomberg consensus for core CPI and unemployment
|
||||
3. Kalshi perfectly matched the realized fed funds rate on the day before every FOMC meeting since 2022 — something neither Bloomberg consensus surveys nor interest rate futures consistently achieved
|
||||
|
||||
**Methodology:** The paper evaluates Kalshi markets across macroeconomic data releases (CPI, PCE, unemployment, FOMC rate decisions) comparing predictive accuracy to professional forecaster surveys (Bloomberg consensus) and financial instrument implied forecasts (futures markets).
|
||||
|
||||
**Context for this finding:**
|
||||
- Kalshi received CFTC approval via $112M acquisition (referenced in Session 1 research journal)
|
||||
- The Fed study was published contemporaneously with the CFTC ANPRM (March 16, 2026) — implicit regulators-studying-the-market signal
|
||||
- Good Judgment Project superforecasters (no skin-in-the-game) also reportedly outperformed futures markets for Fed policy predictions by 66% (FT, July 2024)
|
||||
|
||||
**The complementary finding:** Both real-money prediction markets (Kalshi) and calibrated expert polls (GJP) outperform naive consensus on structured macroeconomic events. Neither definitively outperforms the other on this task type. This is consistent with the two-mechanism analysis: for structured macro-event prediction (binary outcomes, rapid resolution, publicly available information), both Mechanism A (calibration selection) and Mechanism B (information acquisition) are active but neither is the decisive advantage.
|
||||
|
||||
**What this does NOT address:** Financial selection (ICO quality, startup success, investment return prediction). Macro-event prediction (will CPI be above X) has structured resolution criteria. Investment selection (is this ICO worth investing in) does not.
|
||||
|
||||
## Agent Notes
|
||||
|
||||
**Why this matters:** A Federal Reserve paper showing Kalshi beats Bloomberg consensus is meaningful institutional validation of real-money prediction market accuracy — from a regulator's own research arm. This is the strongest institutional credibility signal for prediction markets since the Polymarket CFTC approval.
|
||||
|
||||
**What surprised me:** The perfect match on FOMC-day rates is striking. Professional forecasters with years of Fed-watching couldn't consistently match what Kalshi markets produced the day before FOMC meetings. This suggests financial incentives ARE generating information discovery and aggregation that polls can't match — even in the structured macro-event domain.
|
||||
|
||||
**What I expected but didn't find:** The paper apparently doesn't address prediction market accuracy for financial selection tasks. The Fed's interest is naturally in monetary policy and macroeconomic forecasting, not in investment quality evaluation. The domain gap in the literature continues.
|
||||
|
||||
**KB connections:**
|
||||
- [[speculative markets aggregate information more accurately than expert consensus or voting systems]] — this is direct evidence supporting the claim in a real-money, regulated prediction market context
|
||||
- Pairs with the Mellers two-mechanism analysis: this is Mechanism B evidence (financial stakes generating better information discovery) in a structured prediction domain; complements the Mellers Mechanism A finding in the geopolitical domain
|
||||
- CFTC ANPRM context: The Fed's own research showing market accuracy improvement may influence CFTC's framework development — regulators studying the accuracy data as they design the rules
|
||||
|
||||
**Extraction hints:**
|
||||
- ENRICHMENT: [[speculative markets aggregate information more accurately than expert consensus or voting systems]] — add Kalshi Fed study as supporting evidence with "structured macro-event prediction" scope qualifier
|
||||
- POTENTIAL CLAIM: "Real-money prediction markets demonstrate measurable accuracy advantages over professional survey consensus in structured macroeconomic forecasting" — narrower but better-evidenced than the general claim
|
||||
|
||||
**Context:** This paper is from the Federal Reserve Board of Governors' Finance and Economics Discussion Series. Published March 2026, the same day as the CFTC ANPRM. The simultaneous release suggests the Fed and CFTC are coordinating on building an evidence base for prediction market regulation.
|
||||
|
||||
## Curator Notes
|
||||
|
||||
PRIMARY CONNECTION: [[speculative markets aggregate information more accurately than expert consensus or voting systems]]
|
||||
WHY ARCHIVED: Federal Reserve institutional validation of real-money prediction market accuracy; complements the Mellers academic literature and rounds out the evidence base for Belief #1's grounding claims
|
||||
EXTRACTION HINT: Archive as supporting evidence for the prediction markets accuracy claim, scoped to "structured macroeconomic event prediction." The FOMC-day perfect match finding is the most archivable specific claim. Note it doesn't address financial selection.
|
||||
|
||||
|
||||
## Key Facts
|
||||
- Federal Reserve Board published FEDS paper by Diercks, Katz, Wright in March 2026 evaluating Kalshi prediction market accuracy
|
||||
- Kalshi markets showed statistically significant improvement over Bloomberg consensus for headline CPI prediction
|
||||
- Kalshi markets achieved parity with Bloomberg consensus for core CPI and unemployment forecasting
|
||||
- Kalshi perfectly matched realized fed funds rate on the day before every FOMC meeting since 2022
|
||||
- Fed paper published same day as CFTC ANPRM (March 16, 2026)
|
||||
- Good Judgment Project superforecasters reportedly outperformed futures markets for Fed policy predictions by 66% (FT, July 2024)
|
||||
Loading…
Reference in a new issue