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.