Master SWARM Consensus Forecasting Template — structured methodology for forecast framing, analysis, and audit. - captbullett65/MSCFT

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Over the past month, I’ve developed a forecasting system that expands GPT-4o’s reasoning capabilities using a structured multi-node swarm framework called **MSCFT 4.0A** (Master Swarm Consensus Forecasting Template). This system breaks forecasting into three coordinated stages: * **Node A** frames the question and identifies key sources * **Node B** analyzes developments and assigns structured probabilities * **Node C** applies internal consensus logic to generate a final structured forecast It’s designed for use in complex geopolitical, economic, or policy forecasting — and is structured entirely in plain text for reproducibility, transparency, and scoring compatibility. MSCFT also supports BIN logic (Bias, Information, Noise), inside-outside view structuring, and stepwise prompting to enhance LLM output quality. The framework now includes instructions, examples, and a clean forecasting template — all available on a public repository. If you’re interested in structured reasoning, decision support, or LLM-aided forecasting, I’d love to connect and show how it works.

Over the past month, I’ve developed a forecasting system that expands GPT-4o’s reasoning capabilities using a structured multi-node swarm framework called **MSCFT 4.0A** (Master Swarm Consensus Forecasting Template). This system breaks forecasting into three coordinated stages: * **Node A** frames the question and identifies key sources * **Node B** analyzes developments and assigns structured probabilities * **Node C** applies internal consensus logic to generate a final structured forecast It’s designed for use in complex geopolitical, economic, or policy forecasting — and is structured entirely in plain text for reproducibility, transparency, and scoring compatibility. MSCFT also supports BIN logic (Bias, Information, Noise), inside-outside view structuring, and stepwise prompting to enhance LLM output quality. The framework now includes instructions, examples, and a clean forecasting template — all available on a public repository. If you’re interested in structured reasoning, decision support, or LLM-aided forecasting, I’d love to connect and show how it works.

Real-World Forecasting with GPT-4o and MSCFT: 7 Resolved Cases Over a 12-year forecasting career with more than 1,600 structured predictions logged, I’ve implemented the MSCFT Template (v3.1C – BIN Integrated) to align with GPT-4o’s capabilities in structured reasoning, information filtering, and scenario-based forecasting. Results from 7 Resolved Forecasts: * All 7 forecasts correct or in the correct probability bucket * Average Brier Score: 0.0831 * Career Brier Score improved: 0.497 → 0.494 * Ranked 1st out of 66 in U.S. 10-Year Treasury yield forecast Other domains include S&P 500, Bitcoin, nuclear risk, geopolitical leadership MSCFT’s design includes: * A strict structure for Bias, Information, and Noise analysis (BIN) * Defined bucket logic or binary outcomes * Optional clause modules for yield curve asymmetries and conditional weighting These forecasts were produced using GPT-4o (ChatGPT Plus) with strict adherence to the MSCFT 3.1C forecasting template. GitHub repository (public): find the project at captbullett65 MSCFT If you’re working on structured forecasting with LLMs, this may offer a stable, reproducible approach.

Master SWARM Consensus Forecasting Template — structured methodology for forecast framing, analysis, and audit. - captbullett65/MSCFT

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captbullett65 has 3 repositories available. Follow their code on GitHub.