farness 0.1.0

Forecasting as a harness for decision-making

Farness

Forecasting as a harness for decision-making.

Instead of asking "Is X good?" or "Should I do Y?", farness helps you:

1. Define what success looks like (KPIs)
2. Expand your options (including ones you didn't consider)
3. Make explicit forecasts (with confidence intervals)
4. Track outcomes to improve calibration over time

Installation

pip install farness

Quick Start

As a Python package

from farness import Decision, KPI, Option, Forecast, DecisionStore
from datetime import datetime, timedelta

# Create a decision
decision = Decision(
    question="Should I take the new job offer?",
    kpis=[
        KPI(name="income", description="Total comp after 2 years", unit="$"),
        KPI(name="satisfaction", description="Job satisfaction 1-10"),
    ],
    options=[
        Option(
            name="Take new job",
            description="Accept the offer at Company X",
            forecasts={
                "income": Forecast(
                    point_estimate=300000,
                    confidence_interval=(250000, 400000),
                    reasoning="Base + equity, assuming normal vesting",
                ),
                "satisfaction": Forecast(
                    point_estimate=7.5,
                    confidence_interval=(6, 9),
                    reasoning="Interesting work, but unknown team",
                ),
            }
        ),
        Option(
            name="Stay at current job",
            description="Decline and stay",
            forecasts={
                "income": Forecast(
                    point_estimate=250000,
                    confidence_interval=(230000, 280000),
                    reasoning="Known trajectory, likely promotion",
                ),
                "satisfaction": Forecast(
                    point_estimate=6.5,
                    confidence_interval=(6, 7),
                    reasoning="Comfortable but plateauing",
                ),
            }
        ),
    ],
    review_date=datetime.now() + timedelta(days=180),
)

# Save it
store = DecisionStore()
store.save(decision)

Command Line

# List decisions
farness list

# Show a specific decision
farness show abc123

# Check calibration
farness calibration

# See what needs review
farness pending

Claude Code Plugin

Install the plugin for interactive decision analysis:

claude plugin marketplace add MaxGhenis/farness
claude plugin install farness@maxghenis-plugins

Then use /farness:decide to run a structured decision analysis.

The Framework

Farness implements a structured decision process:

1. KPI Definition - What outcomes actually matter? Make them measurable.

2. Option Expansion - Don't just compare A vs B. What about C? What about waiting? What about hybrid approaches?

3. Decomposition - Break forecasts into estimable components (Fermi-style).

4. Outside View - Start with base rates before adjusting for specifics.

5. Confidence Intervals - Point estimates aren't enough. How uncertain are you?

6. Tracking - Log decisions and review outcomes to calibrate over time.

Why This Works

• Reduces sycophancy - Harder to just agree when making numeric predictions
• Forces mechanism thinking - Must reason about cause and effect
• Creates accountability - Predictions can be scored later
• Separates values from facts - You pick KPIs (values), forecasts are facts
• Builds calibration - Track predictions over time to improve

Development

git clone https://github.com/MaxGhenis/farness
cd farness
pip install -e ".[dev]"
pytest

License

MIT