copyleftdev

forecasting-bayesian-updates

Updates a probability using Bayes' rule and explicitly supported likelihoods. Applies when new evidence changes an existing forecast, when base rates must be combined with a signal, or when duplicate reports and dependent evidence risk overcounting.

forecasting-brier-scoring

Calculates and audits Brier scores for resolved binary and nominal categorical forecasts. Applies to forecast scorecards, probability accuracy comparisons and tournament scoring; declares score normalization, aligns questions and timestamps, and excludes unresolved outcomes.

forecasting-calibration

Diagnoses forecast calibration using reliability groups and Brier decomposition while separating calibration from resolution. Applies to resolved forecast histories, overconfidence audits, reliability tables and recalibration proposals; reports sample sizes and binning artifacts.

forecasting-conditional-trees

Generates near-term indicator questions for a complex ultimate outcome using FRI conditional-tree elicitation. Applies to finding forecast cruxes, ranking early-warning indicators and measuring expected information gain; distinguishes informational value from decision utility.

forecasting-counterevidence

Challenges a provisional forecast through consider-the-opposite reasoning and diagnostic counterevidence. Applies to confirmation bias, overconfident narratives, forecast red-teaming, or requests to identify what evidence would change a conclusion.

forecasting-decomposition

Breaks compound forecasts into conditional probability models, event trees, or dimensional estimates. Applies to conjunctions, alternative pathways, scenario mixtures, Fermi-style estimation, and sensitivity analysis; checks dependencies before combining components.

forecasting-delphi-elicitation

Structures independent forecast elicitation followed by controlled feedback and private revision using Delphi-inspired methods. Applies when collecting estimates from actual participants or assessing panel design; distinguishes independent respondents from correlated AI samples.

forecasting-logit-aggregation

Combines comparable binary probability forecasts using the Satopaa logit-pooling method and validated extremization. Applies to forecast ensembles, pooling expert probabilities, or choosing aggregation parameters; checks evidence overlap and does not assume AI samples are independent.

forecasting-question-design

Turns ambiguous future claims into time-bounded, independently resolvable forecasting questions. Applies to event definitions, forecast contracts, tournament question writing, and disputes about what counts as an outcome; does not assign probabilities by itself.

forecasting-reference-classes

Builds defensible base rates from comparable historical cases and applies reference-class forecasting. Applies when estimating project outcomes, event frequencies, timelines, costs, or when a compelling individual story may be obscuring the outside view.

forecasting-talent-validation

Evaluates whether apparent forecasting talent persists beyond the sample used for selection. Applies to identifying top forecasters, validating claimed superforecasting performance, comparing humans or models, and designing honest tournament leaderboards.

forecasting-update-ledger

Maintains a timestamped, append-only forecast history with evidence-based revision and resolution rules. Applies when updating a prior forecast, documenting what changed, planning review triggers, or auditing hindsight; does not start monitoring without authorization.

forecasting-workflow

Produces an end-to-end, research-informed probability forecast with a resolvable question, base rate, evidence updates, dependency checks, counterevidence, and a scoring plan. Applies to substantive future-event forecasting requests; uses narrower technique skills only when available and does not certify superforecaster status.