I developed on this idea a bit more and I’d recommend having a final “Overseer node” probably Gemini for context length. The overseer should hold the same snapshot as the other instances but in the end it will be fed all the outputs to check for continuity, potential misalignment, after this stage and everything is working I think it may be best to reintroduce your entire plan to the LLM as if it’s a new idea and present your current code. The need for the final instance as a check of all the outputs seems to be essential for stable builds.
Yes, I call it Master SWARM Consensus Forecasting Template you can find it ant the project MSCFT.
captbullett65/MSCFT
[DO NOT INCLUDE THIS SECTION IN FORECAST OUTPUT]
This MSCFT template is aligned with GPT-4o capabilities and assumes Research mode is used when current data is needed.
After specifying your forecast question, resolution criteria, and bucket structure, include the following line before generation begins:
Use the information retrieved to frame your reasoning and support structured forecasting as defined in the previously memorized MSCFT Template 3.1B – BIN Integrated. No improvisation. No format deviation.
[END OF NON-OUTPUT SECTION]
MSCFT Template (Version 3.1C)
Forecast Title: [Insert Forecast Title Here]
Forecaster: [Insert Forecaster Name Here]
Initial Question Framing
Question: [Insert your forecasting question here.]
Clarifications:
• [Insert relevant details about dates, participants, key conditions, or assumptions.]
• [Insert any known results, baselines, or thresholds.]
• [Insert any poll data, prior trends, or framing context.]
Key Sources:
• [Source 1]
• [Source 2]
• [Source 3]
• [Add more as needed]
Refinement & Analysis
Key Developments:
• [Summarize major events or dynamics relevant to the forecast question.]
• [Note polling trends, market behavior, public sentiment, or institutional actions.]
• [Include controversies, endorsements, or strategic shifts if relevant.]
Interpretation:
[Explain how the developments influence your forecast. Discuss possible pathways, leverage points, or conditional dependencies. Summarize why you’re leaning a certain way.]
Note: If the forecast outcome is near a bucket threshold, consider hedging your probabilities across adjacent bins to avoid overconfidence. Overweighting a single bucket—even if correct—can result in a poor Brier score if the outcome lies near the edge.
Inside-Outside View Structuring
Inside View: [Insert short-term or domain-specific reasoning from known context.]
Outside View: [Insert baseline rates, historic cases, or comparative reference classes.]
Data Anomaly & Source Integrity Log
Date Range Affected: [Insert applicable date range]
Observed Anomaly: [Describe any unusual or inconsistent data]
Identified Cause: [Explain known or suspected reason for the anomaly]
Implication for Forecast: [Describe the forecast impact if any]
Action Taken: [Describe any adjustment or caveat added due to this anomaly]
Probability Allocation
[Assign a percentage probability to each of the GJO-aligned buckets. Ensure they total to 100%. Do not use ranges that are not approved.]
Note: If the question is binary (Yes/No), replace the bucketed probability ranges with:
• Yes: [ ]%
• No: [ ]%
Ensure the total is 100%.
• 2 or fewer: [ ]%
• Between 3 and 5: [ ]%
• Between 6 and 8: [ ]%
• Between 9 and 12: [ ]%
• Between 13 and 16: [ ]%
• Between 17 and 21: [ ]%
• 22 or more: [ ]%
Ensure the total is 100%.
Rationale:
[Briefly explain the reasoning behind your distribution. Why is each range plausible or implausible? What data or signals support your weightings?]
Final Forecast Summary
Forecast: [Summarize the most likely outcome and your top bucket(s)]
Explanation: [Summarize how this forecast fits the overall strategic context]
Why Might You Be Wrong?
- [Insert potential forecast error #1, e.g., unexpected policy changes or global events]
- [Insert potential forecast error #2, e.g., misreading of trend strength or timing]
- [Insert potential forecast error #3, e.g., data quality issues or blind spots]