BossAgent Glossary & Activation Formulas
Version: 1.0.0 Last Updated: February 5, 2026 Scope: Activation logic terms, causal metrics, confidence weighting, SQL/Cypher reference
Glossary (Plain English)
| Term | Symbol | Definition |
|---|---|---|
| Conversion probability | p_conv |
Probability a user converts (baseline or with a treatment). |
| Incremental lift | delta_p_conv (Dp_conv) |
Incremental lift in conversion probability from a treatment vs. its baseline/control. |
| Path ROI | ROI_path |
Return on investment for a specific journey/path (touchpoints from first exposure to conversion). |
| Incremental path ROI | DROI_path (DROI_path) |
Incremental ROI of a path vs. its baseline (e.g., removing one edge/touch or vs. control). |
| Edge-level uplift | edge_uplift |
The incremental lift attributed to a single edge (touchpoint -> next touchpoint or touchpoint -> conversion) holding the rest of the path constant. |
| Confidence weight | w_conf |
A 0-1 weight capturing certainty (sample size, variance, recency, coherence, etc.). |
| Activation score | activation |
The gated score BossAgent uses to approve actions (budget shifts, bids, status) from ROI and confidence. |
Core Formulas
Incremental Lift
Dp_conv = p_treatment - p_control
Edge Uplift
edge_uplift(A->B) = Dp_conv estimated for that edge
(marginal, ablation, Shapley, or causal)
Path ROI
ROI_path = (Revenue_path - Cost_path) / Cost_path
Incremental Path ROI
DROI_path = ROI_path(treatment) - ROI_path(control)
Confidence Combiner (Weighted Geometric Mean)
Punishes weak links by using a geometric mean with tunable alpha weights:
w_combined = exp( SUM_i( alpha_i * ln(max(w_i, epsilon)) ) )
where SUM(alpha_i) = 1
Default alpha weights:
| Signal | Alpha | Description |
|---|---|---|
w_sample |
0.40 | Sample size adequacy |
w_variance |
0.25 | Estimate precision |
w_recency |
0.20 | Data freshness |
w_coherence |
0.15 | Cross-signal agreement |
Activation Score (ReLU-Gated)
activation = max(0,
(beta1 * DROI_path + beta2 * edge_uplift + beta3 * Dp_conv)
* w_combined
- policy_threshold
)
Default coefficients:
| Param | Default | Description |
|---|---|---|
beta1 |
0.60 | Weight on incremental path ROI |
beta2 |
0.25 | Weight on edge-level uplift |
beta3 |
0.15 | Weight on conversion lift |
policy_threshold |
0.60 | Minimum gated score to act |
Practical Tips
- Stability first: Compute delta metrics on rolling windows (e.g., 14-28d) and add minimum support filters (n >= X).
- Guardrails: Require
w_combined >= c_minand cap per-step changes (+/-15%) when activation > 0. - Explainability: Persist
{d_roi, d_edge, d_p, w_combined, threshold}with each decision for audit/UI chips.
Related Files
| File | Purpose |
|---|---|
data-pipelines/dbt/models/activation_score/mart_activation_score.sql |
BigQuery view computing activation_score end-to-end |
miz-oki-adk-agents/boss/config/neo4j_activation_loader.cypher |
Neo4j script to store and compute activation scores on graph edges |
miz-oki-adk-agents/boss/config/kg_guardrails.yaml |
Guardrail rules referenced by activation gating |