🪲 BugOut Index

Methodology — v1.0.0

The BugOut Index aggregates six normalized metrics into a single weighted score on a 0–100 scale, where higher values indicate greater societal stability. This page describes how the current week's number was produced.

1. Inputs and weights

Metric	Raw value	Unit	Normalization range	Normalized	Weight	Source
Inflation Rate	3.29	%	−10 to 15	46.86	15.0%	FRED (CPIAUCSL)
Violent + Property Crime	2,723.00	per 100k	500 to 8,000	70.36	12.0%	Real-Time Crime Index
Unemployment Rate	4.30	%	0 to 25	82.8	12.0%	FRED (UNRATE)
Debt-to-GDP Ratio	122.57	%	0 to 200	38.72	12.0%	FRED (GFDEGDQ188S)
Homelessness Rate	0.23	% of pop.	0 to 0.5	54.0	9.0%	HUD AHAR
Trust in Government	41.00	% confidence	0 to 80 (inverted)	51.25	12.0%	Edelman Trust Barometer

2. Normalization

Each metric is normalized to 0–100 using predefined thresholds:

normalized = (1 − (raw − min) / (max − min)) × 100

Trust in Government is inverted (higher trust → higher stability) by applying 100 − normalized after the above transform.

3. Aggregation

The index is the weight-normalized sum of the per-metric scores:

BOI = Σ (normalized_i × weight_i) / Σ weight_i

4. Risk bands

Range	Band	Interpretation
70 – 100	High Stability	Low risk — focus on long-term planning.
55 – 69.99	Moderate Stability	Warning signs — monitor trends; prepare contingencies.
40 – 54.99	Low Stability	Heightened risk — initiate preparedness measures.
Below 40	Critical Instability	Collapse likely — activate bug-out plans.

5. Limitations and caveats

Freshness mismatch. Inflation, unemployment, and debt-to-GDP update monthly or quarterly; the Edelman trust score updates annually; homelessness is annual (HUD PIT). The BOI is recomputed weekly but will only move when underlying data publishes.
National scope. Crime and homelessness figures are national averages or approximations; local conditions vary substantially.
Not a forecast. The BOI describes current observed conditions relative to chosen thresholds, not future outcomes.
Threshold sensitivity. Because the index is built on fixed normalization ranges, slow drift inside a range will not move the score — the thresholds are calibrated to reflect historically extreme conditions at the endpoints.

6. Reproducibility

Every weekly run commits the raw inputs, the computed normalized scores, and the final index to this repository. The code that produced this page is open source; you can recompute any historical week by checking out that commit and re-running runtime/publish/weekly_run.py.