futures-calibration

name	futures-calibration
description	Aggregate probability judgments across perspectives using Real-Time Delphi or prediction market mechanisms.
dependencies	{"tactics":["iterative-convergence-round","threshold-calibration"],"sops":["consensus-synthesis"]}

Futures Calibration

Purpose: Aggregate probabilistic forecasts from multiple perspectives into calibrated predictions. Uses Real-Time Delphi continuous updating or prediction market scoring to produce well-calibrated probability estimates for future events.

When to use:

• Technology timeline estimation
• Market forecasts requiring multiple expert inputs
• Risk probability assessment
• Any question framed as "what is the probability that X by time T?"

Budget

Parameter	Constraint
Rounds	2–3 (continuous updating preferred)
Perspectives	≥4 independent forecasters
Calibration target	Brier score improvement across rounds

State Ledger

Key	Type	Description
question	string	The forecasting question
time_horizon	string	When the event would resolve
perspectives	array	List of forecaster perspectives
rounds	array	History of probability estimates
current_aggregate	float	Current aggregated probability
calibration_metrics	object	Brier score, log score tracking

Available Tactics

• iterative-convergence-round — Adapted for probability estimates
• threshold-calibration — Determine confidence bands

Available SOPs

• judgment-collection
• feedback-distribution
• consensus-measurement
• round-decision
• threshold-sweep
• consensus-synthesis

Execution Guidance

1. Frame question with clear resolution criteria and time horizon
2. Collect initial probability estimates with reasoning
3. Share aggregate and reasoning (anonymized) each round
4. Track whether estimates are converging or polarizing
5. Report final calibrated estimate with confidence interval

Output Format

forecast_question: <question>
time_horizon: <date/period>
calibrated_probability: <float 0-1>
confidence_interval: [lower, upper]
rounds_completed: <int>
convergence_pattern: converging/stable/polarizing
key_considerations: [...]

Available Tactics

Optional, no fixed order; the final leaf is always a sop.

Tactic	When to use
iterative-convergence-round	Execute one full Delphi round — collect judgments, distribute anonymous feedback, measure consensus, decide whether to continue.
threshold-calibration	Systematically sweep consensus thresholds to observe which items achieve consensus at what level, producing a threshold-consensus curve.

Available SOPs

Optional, no fixed order; the final leaf is always a sop.

SOP	When to use
consensus-synthesis	Synthesize all rounds into a final consensus report documenting agreements, dissent, and process.