# Collective Intelligence的所有文章

在 HTX 新闻中心浏览与「Collective Intelligence」相关的最新资讯与深度分析。潘盖市场趋势、项目动态、技术进展及监管政策，提供权威的加密行业洞察。

Is Polymarket's Pricing Wrong? 200 AI Agent Simulation of Crisis Yields Unexpected Answer

An experiment used MiroFish, an open-source multi-agent simulation platform, to model the geopolitical crisis in the Strait of Hormuz and compare the results with Polymarket's prediction market. The system generated 200 AI agents—including government officials, media, energy firms, financial traders, and civilians—and simulated 7 days of social media interaction (Twitter-like environment) based on a 5,800-character background brief. Key findings: - Organic, free-form discussions among agents produced an average probability of 47.9% for the strait reopening by April 2026, significantly higher than Polymarket's market-derived probability of 31%. - When agents were individually questioned in a formal "interview" setting, they converged to overly optimistic responses (60–75% across categories), reflecting a cooperation bias. - The most accurate predictions came from a minority of pessimistic agents (e.g., Iranian officials, financial analysts, academics) who organically expressed probabilities near 22%—aligning closely with market pricing. - The simulation revealed a structural divide: public/official statements tend toward optimism, while genuine risk assessments emerge from unstructured, adversarial discourse. The study suggests that natural interaction among specialized agents can generate valuable signals, but LLM bias and limited context remain constraints. Future work will expand data scope, use stronger models, and increase agent diversity.

marsbit03/18 06:16

Is Polymarket's Pricing Wrong? 200 AI Agent Simulation of Crisis Yields Unexpected Answer

marsbit03/18 06:16

When Polymarket Enters the Dow Jones, Prediction Markets Are Becoming Part of Serious News

Polymarket, a prediction market platform, has entered into an exclusive partnership with Dow Jones Media Group. Under the agreement, Polymarket’s real-time prediction probabilities will become the sole source of prediction market data across all Dow Jones consumer platforms, including dedicated data modules, event pages, and customized earnings calendars. This integration will reach audiences of major financial publications such as The Wall Street Journal, Barron’s, and MarketWatch. The collaboration signals a significant shift in how news is presented, moving beyond traditional expert analysis and polls to incorporate crowd-sourced, money-backed probabilistic forecasts on elections, economic trends, and cultural events. This endorsement from a highly credible financial news organization suggests prediction markets are increasingly viewed as serious informational tools rather than mere gambling platforms. 2025 has been a breakthrough year for prediction markets, with Polymarket and competitor Kalshi recording nearly $40 billion in trading volume and achieving multibillion-dollar valuations. Polymarket’s notably accurate predictions during the 2024 U.S. elections—where it consistently projected a Trump victory with high certainty—demonstrated the effectiveness of incentive-driven crowd wisdom. However, regulatory challenges remain. While Kalshi holds a CFTC license, it faces legal scrutiny in states like Nevada, where prediction markets are still considered unlicensed gambling. Polymarket has also encountered criticism around potential insider trading, highlighting the lack of clear regulatory frameworks. Despite these issues, the Dow Jones partnership marks a major step toward the mainstream acceptance of prediction markets as a credible supplement to traditional news.

Odaily星球日报01/13 07:27

When Polymarket Enters the Dow Jones, Prediction Markets Are Becoming Part of Serious News

Odaily星球日报01/13 07:27

What Should the New Financial Infrastructure of the AI Era Look Like?

The article explores the limitations of current prediction markets, which, despite their success in aggregating information through risk-sharing (e.g., accurately predicting election outcomes), suffer from a flawed economic model: their most valuable output—information—becomes a free public good once generated. This restricts their viability to entertainment-driven domains like elections and sports, while critical areas (geopolitical risk, regulatory outcomes, etc.) remain unaddressed. The author proposes "Cognitive Finance," a new infrastructure designed from first principles for the AI and crypto era. Key components include: - **Private Markets**: Using trusted execution environments (TEEs) to keep prices confidential, enabling entities (e.g., hedge funds, corporations) to pay for exclusive signals without leakage to competitors. - **Combinatorial Markets**: Moving beyond isolated events to maintain a joint probability distribution, where trades update correlated outcomes simultaneously, akin to a neural network. - **Agent Ecosystems**: AI-native markets where specialized agents (trading, evaluation, information acquisition) operate with strict isolation between price access and information sourcing to prevent self-cannibalization. - **Human Intelligence**: Interfaces allowing humans to contribute knowledge via natural language without seeing prices, compensated based on predictive accuracy. The vision is a decentralized, composable infrastructure where AI systems and humans collaboratively build a continuously updated, probabilistic world model. This transcends today’s prediction markets, aiming to transform decision-making in finance, supply chains, geopolitics, and beyond by making uncertainty tradable and knowledge liquid.

marsbit12/26 11:06

What Should the New Financial Infrastructure of the AI Era Look Like?

marsbit12/26 11:06

Kalshi's First Research Report Released: How Collective Intelligence Outperforms Wall Street Think Tanks in Predicting CPI

Kalshi Research's inaugural report demonstrates that prediction markets significantly outperform Wall Street consensus forecasts in predicting year-over-year CPI data, particularly during economic shocks. The study, analyzing data from February 2023 to mid-2025, shows Kalshi’s predictions had a 40.1% lower mean absolute error (MAE) than consensus forecasts overall. During major shocks (deviations >0.2 percentage points), Kalshi’s MAE was 50–60% lower, and for moderate shocks (0.1–0.2 points), it was 50–56.2% lower. When market predictions diverged from consensus by over 0.1 points, they correctly signaled a shock about 81% of the time and were more accurate in 75% of cases. The outperformance is attributed to diverse participant insights, direct financial incentives for accuracy, and efficient aggregation of dispersed information. These findings suggest prediction markets provide a valuable complementary signal, especially in volatile periods where traditional models often fail.

marsbit12/24 04:21

Kalshi's First Research Report Released: How Collective Intelligence Outperforms Wall Street Think Tanks in Predicting CPI

marsbit12/24 04:21

Kalshi's First Research Report Released: How Collective Intelligence Outperforms Wall Street Think Tanks in Predicting CPI

Kalshi Research's inaugural report demonstrates that prediction markets consistently outperform Wall Street consensus forecasts in predicting the U.S. year-over-year CPI inflation rate. The study, covering over 25 monthly CPI releases from February 2023 to mid-2025, shows Kalshi’s market-implied forecasts had a 40.1% lower mean absolute error (MAE) than consensus predictions across all environments. The advantage was most pronounced during economic "shocks." For large surprises (over 0.2 percentage points), Kalshi's forecasts were 50% more accurate a week before the data release, improving to 60% more accurate the day before. For medium surprises (0.1-0.2 percentage points), the advantage was similarly 50%, rising to 56.2% closer to the release. Crucially, a divergence of over 0.1 percentage points between the market forecast and consensus served as a strong signal, with an 81.2% probability that a shock would occur. When the two forecasts disagreed, the market prediction was more accurate 75% of the time. The report attributes this "Shock Alpha" to three factors: the "wisdom of crowds" aggregating diverse information, superior incentive structures that reward accuracy over conformity, and more efficient information synthesis, even with the same public data. This suggests prediction markets provide a valuable, differentiated signal for investors and policymakers, especially during periods of high uncertainty.

Odaily星球日报12/24 04:00

Kalshi's First Research Report Released: How Collective Intelligence Outperforms Wall Street Think Tanks in Predicting CPI

Odaily星球日报12/24 04:00

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