Reflexivity & Irrationality Index

Overview

This pillar quantifies the influence of human psychology, social feedback loops, and irrational behavior on a market's outcome. It's essential for identifying markets where the act of predicting can itself alter the result, a concept known as reflexivity.

What It Does

The index analyzes market structure, media narratives, and social media chatter to detect self-reinforcing feedback loops. It scores how much a market's outcome depends on subjective human decisions versus objective, physical facts. This allows you to measure the 'irrationality premium' or discount present in the market's odds.

Why It Matters

In highly reflexive markets, traditional fundamental analysis is unreliable because sentiment can become a fundamental of its own. This pillar provides a crucial edge by flagging when to distrust objective data and instead focus on crowd psychology and momentum.

How It Works

First, it calculates a Human Agency Score, assessing if the outcome is based on physics or opinion. Next, it performs sentiment analysis on news and social data to measure emotional intensity. Finally, it detects feedback loops by correlating price velocity with media mentions, combining these into a single, actionable index.

Methodology

The final index is a weighted average of three components. 1. Human Agency Score (HAS): A 0-1 scale where 1 indicates an outcome is 100% human-decided (e.g., an Oscar vote). 2. Emotional Sentiment Weight (ESW): A score derived from NLP models analyzing the polarity and subjectivity of related news and social media content over a 7-day window. 3. Feedback Loop Strength (FLS): A correlation coefficient between the 24-hour price chart's rate-of-change and the volume of media mentions.

Edge & Advantage

It provides a clear signal for when to fade the 'smart money' and position on irrational momentum, a blind spot for purely quantitative models.

Key Indicators

• Human Agency Score

  high

  Measures the degree to which an outcome is determined by human opinion vs. objective reality.

• Feedback Loop Detector

  high

  Identifies if price movements are driving media coverage, which in turn drives more price movement.

• Emotional Sentiment Weight

  medium

  Quantifies the level of emotionally charged language in public discourse surrounding the market.

Data Sources

• Social Media APIs (e.g., Twitter, Reddit)

  Provides raw text data for sentiment analysis and chatter volume measurement.

• News APIs (e.g., Google News)

  Offers media articles to analyze narrative formation and sentiment from established sources.

• Prediction Market Price Feeds

  Supplies price and volume data needed to calculate velocity and correlation with media chatter.

Example Questions This Pillar Answers

• → Will Bitcoin reach $100k by the end of the year?
• → Who will win Time Magazine's Person of the Year?
• → Will GameStop's stock (GME) close above $50 on Friday?

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