Amazing work Elie! had fun with this one
Created this fork: https://github.com/swatfa/worldmonitor-bayesian
Utilizes X.com's open source Recommendation engine + Bayesian probability + Martingale & Nassim Taleb's Black Swan theorem to aggregate all the data and look for areas of developing confluence
Forked Woldmonitor-bayesian
Designed to detect systemic fragility and tail-risk events before they manifest as global crises. It transforms raw data into high-fidelity intelligence by employing a multi-stage analysis pipeline inspired by state-of-the-art recommendation and statistical theories.
1. Core Algorithms
X.com (The Algorithm) Integration:
Candidate Sourcing: Inspired by X's search ranking, the engine filters thousands of raw data points (from news, markets, and military transponders) into a "Heavy Ranking" pool of high-signal candidates.
SimClusters (Community Detection): Groups related signals into coherent Risk Narratives using Jaccard similarity, identifying how a news spike in one region might be "clustered" with a market move in another.
PageRank (Centrality): Uses a graph-based influence score (similar to TweepCred) to determine which specific signal is the "pivot point" for global instability.
Bayesian Inference: Calculates the Posterior Probability of a crisis. As new evidence arrives (e.g., a military flight cluster + a prediction market shift), the system updates its "belief" in a Black Swan event in real-time.
Martingale Risk Theory: Monitors Risk Accumulation. It detects when volatility is no longer "random walk" but is instead compounding exponentially—a signature of an impending "Dragon King" event (an extreme but non-random outlier).
Black Swan Theory (Nassim Taleb): Explicitly looks for Negative Convexity—states where the system is highly sensitive to small perturbations, making it vulnerable to "unknown unknowns."
2. What the System Does
The system acts as a Synthetic Intelligence Officer. It doesn't just show data; it generates a Correlated Hypothesis.
Detects "Gray Rhinos": Highly probable but ignored threats (e.g., inverted yield curves + rising social unrest).
Predicts Cascading Failures: Identifies how a cyber outage in one sector could trigger a "liquidity fracture" in the markets.
Visualizes the "Correlation Cube": A 3D interactive matrix where pulsating hotspots represent the PageRank Centrality of current global risks.
3. Confidence & Predictive Utility
Confidence Scores (0–95%): The "Bayesian Confidence" is derived from Triangulation. If only one source (e.g., News) flags a risk, confidence remains low (<40%). If three independent dimensions (e.g., Military, Commodities, and Prediction Markets) align, confidence scales to Critical (80%+).
Utility: The system is best at predicting Phase Transitions—the exact moment when a stable situation becomes "fragile." While it cannot predict the exact "unknown" trigger, it accurately identifies the Structural Fragility that allows that trigger to cause a global impact to become a Black Swan.
Amazing work Elie! had fun with this one
Created this fork: https://github.com/swatfa/worldmonitor-bayesian
Utilizes X.com's open source Recommendation engine + Bayesian probability + Martingale & Nassim Taleb's Black Swan theorem to aggregate all the data and look for areas of developing confluence
Forked Woldmonitor-bayesian
Designed to detect systemic fragility and tail-risk events before they manifest as global crises. It transforms raw data into high-fidelity intelligence by employing a multi-stage analysis pipeline inspired by state-of-the-art recommendation and statistical theories.
1. Core Algorithms
X.com (The Algorithm) Integration:
Candidate Sourcing: Inspired by X's search ranking, the engine filters thousands of raw data points (from news, markets, and military transponders) into a "Heavy Ranking" pool of high-signal candidates.
SimClusters (Community Detection): Groups related signals into coherent Risk Narratives using Jaccard similarity, identifying how a news spike in one region might be "clustered" with a market move in another.
PageRank (Centrality): Uses a graph-based influence score (similar to TweepCred) to determine which specific signal is the "pivot point" for global instability.
Bayesian Inference: Calculates the Posterior Probability of a crisis. As new evidence arrives (e.g., a military flight cluster + a prediction market shift), the system updates its "belief" in a Black Swan event in real-time.
Martingale Risk Theory: Monitors Risk Accumulation. It detects when volatility is no longer "random walk" but is instead compounding exponentially—a signature of an impending "Dragon King" event (an extreme but non-random outlier).
Black Swan Theory (Nassim Taleb): Explicitly looks for Negative Convexity—states where the system is highly sensitive to small perturbations, making it vulnerable to "unknown unknowns."
2. What the System Does
The system acts as a Synthetic Intelligence Officer. It doesn't just show data; it generates a Correlated Hypothesis.
Detects "Gray Rhinos": Highly probable but ignored threats (e.g., inverted yield curves + rising social unrest).
Predicts Cascading Failures: Identifies how a cyber outage in one sector could trigger a "liquidity fracture" in the markets.
Visualizes the "Correlation Cube": A 3D interactive matrix where pulsating hotspots represent the PageRank Centrality of current global risks.
3. Confidence & Predictive Utility
Confidence Scores (0–95%): The "Bayesian Confidence" is derived from Triangulation. If only one source (e.g., News) flags a risk, confidence remains low (<40%). If three independent dimensions (e.g., Military, Commodities, and Prediction Markets) align, confidence scales to Critical (80%+).
Utility: The system is best at predicting Phase Transitions—the exact moment when a stable situation becomes "fragile." While it cannot predict the exact "unknown" trigger, it accurately identifies the Structural Fragility that allows that trigger to cause a global impact to become a Black Swan.