rfi

name

rfi

description

Crowdsourced forecasting questions and predictions from the RAND Forecasting Initiative (formerly INFER). Policy-relevant forecasting questions with crowd probabilities, individual forecaster predictions with rationales, and comments. Use for any question about geopolitics, national security, science and technology policy, or when you need calibrated crowd forecasts as base rates. All methods support cutoff_date for backtesting.

RAND Forecasting Initiative (RFI)

SDK that provides access to the RAND Forecasting Initiative crowdsourced forecasting platform (formerly INFER, powered by Cultivate Labs). It covers policy-relevant forecasting questions with aggregated crowd probabilities and individual forecaster predictions with rationales.

Quick Start

from sdk_rfi import Client

client = Client()  # Uses RFI_EMAIL and RFI_PASSWORD env vars

Key Methods

Method	What it does
client.questions.list(...)	List forecasting questions with filtering by status, tags, challenges, date ranges
client.questions.get(question_id)	Get a specific question with answers and crowd probabilities
client.prediction_sets.list(question_id=...)	Get individual forecaster predictions with rationales for a question
client.comments.list(commentable_id=..., commentable_type=...)	Get discussion comments on a question

Data Coverage

• Domain: Politics / policy forecasting
• Countries/Regions: Global (US policy focus)
• Time range: Questions from ~2020 to present
• Update frequency: Event-driven (new questions published, forecasts updated continuously)
• Key data: Forecasting questions on geopolitics, national security, science/technology policy, economics, biosecurity, nuclear risk, AI governance

Forecasting Patterns

• Crowd forecast as base rate: Use client.questions.list() to find questions matching your topic, then read the crowd probability from question.answers[i].probability as a calibrated starting point.
• Trend analysis via prediction history: Use client.prediction_sets.list(question_id=X) to see how individual forecasts changed over time. Plot created_at vs forecasted_probability to detect momentum.
• Expert rationale mining: Individual prediction sets include rationale text explaining the forecaster's reasoning. Use client.prediction_sets.list(question_id=X) and examine rationales for weak signals and arguments.
• Cross-reference with resolution: Use client.questions.list(status="closed") to find resolved questions. Compare crowd probability at various cutoff dates against actual outcomes to measure calibration.
• Comment-based signal detection: Use client.comments.list(commentable_id=X, commentable_type="Forecast::Question") to find discussion threads that may contain links to evidence or emerging developments.

Common Queries

# Get all active forecasting questions
questions = client.questions.list()
for q in questions.questions:
    print(f"{q.id}: {q.name}")
    for a in (q.answers or []):
        print(f"  {a.name}: {a.probability_formatted}")

# Get individual forecasts and rationales for a question
forecasts = client.prediction_sets.list(question_id=1234)
for ps in forecasts.prediction_sets:
    print(f"{ps.membership_username}: {ps.rationale}")
    for pred in (ps.predictions or []):
        print(f"  Answer {pred.answer_id}: {pred.forecasted_probability:.1%}")

# Backtest: get data as it was available on a past date
past_questions = client.questions.list(cutoff_date="2025-06-01")
past_forecasts = client.prediction_sets.list(question_id=1234, cutoff_date="2025-06-01")

Full Method Reference

See references/methods.md for all 4 methods with complete parameter details.