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D-NAV Documentation

The language and signals behind faster, clearer decisions

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Overview

D-NAV (Decision Navigator) is a framework for making faster, clearer decisions by quantifying the key factors that influence every choice. It transforms subjective decision-making into objective analysis through structured evaluation and real-time insights.

How It Works
1

Rate Variables

Score 5 key factors from 1-10

2

Get Insights

See real-time metrics and patterns

3

Make Decisions

Act with confidence and clarity

Core Ingredients

The five fundamental variables that shape every decision (rated 1-10 each)

Impact

Upside/importance if it works

1-3:Low impact
4-6:Moderate impact
7-10:High impact
Cost

Time • money • effort • focus

1-3:Low cost
4-6:Moderate cost
7-10:High cost
Risk

Downside, what could go wrong

1-3:Low risk
4-6:Moderate risk
7-10:High risk
Urgency

How soon action is needed

1-3:Low urgency
4-6:Moderate urgency
7-10:High urgency
Confidence

Evidence & readiness to execute

1-3:Low confidence
4-6:Moderate confidence
7-10:High confidence

Scoring Guidelines

Scoring is in-the-moment: 1 = minimal, 10 = maximum. You’re rating how it feels right now — tomorrow’s “10” might change with new info or context.

Derived Signals

Three key metrics calculated from your core variables to reveal decision patterns

Return
Impact − Cost

Value after cost

PositiveGain
NeutralBreak-even
NegativeLoss
Stability
Confidence − Risk

Survivability

Stable≥ 0
Uncertain≈ 0
Fragile< 0
Pressure
Urgency − Confidence

Execution stress

Calm< 0
Balanced≈ 0
Pressured> 0

Strategic Insight

Return (R), Stability (S), and Pressure (P) are the core physics behind every decision. We group them to expose different risks: Return = value after cost, Stability = survivability, Pressure = execution stress. Strategic short-term negative return can be acceptable when Stability stays ≥ 0 and runaway Pressure is avoided.

Each decision gets an R, P, and S sign: R+ (gain), R0 (roughly break-even), R- (loss); P- (calm), P0 (balanced), P+ (pressured); S+ (stable footing), S0 (uncertain footing), S- (fragile footing). These signs feed directly into the Decision Archetypes shown below.

Merit & Energy

The two fundamental components that make up the D-NAV score

Merit
Impact − Cost − Risk

Inherent quality of the bet (unit economics minus risk drag)

High Merit:Strong value proposition
Low Merit:Weak or risky bet
Energy
Urgency × Confidence

Applied energy — how hard & how ready you’ll push now

High Energy:Ready to execute
Low Energy:Not ready or urgent

At the system level, we sum these components per category. Category Merit is the total inherent quality of decisions in that category (how strong the bets are after cost and risk). Category Energy is the total execution push (how much urgency × confidence leadership spends there). Together they show where judgment actually creates value and where decision effort is being spent.

D-NAV Formula

The core calculation that combines Merit and Energy

D-NAV = Merit + Energy
(Impact − Cost − Risk) + (Urgency × Confidence)

D-NAV blends Merit (quality of the bet) with Energy (execution momentum). High D-NAV = a strong bet and/or a strong push — always read Return,Stability, and Pressure to avoid hidden traps and slow bleeds.

Compare Mode

Side-by-side comparison between a Base scenario and a Scenario you adjust with sliders

Delta Calculations
Δ

Delta (Δ)

ΔX = XScenario − XBase. Positive = Scenario is higher.

ΔR

ΔReturn

Δ(Impact − Cost) — net value change after cost.

ΔS

ΔStability

Δ(Confidence − Risk) — survivability change.

ΔP

ΔPressure

Δ(Urgency − Confidence) — execution stress change.

ΔD

ΔD-NAV

Δ[(Impact − Cost − Risk) + (Urgency × Confidence)] — overall quality × push change.

Best Feasible Nudge (Optimizer)

Finds the best feasible improvement under your guardrails. Searches small slider adjustments (typically 1-point steps) to maximize ΔD-NAVwhile respecting posture and stability constraints.

Consulting-grade nudge prompts:

  • Raise D-NAV without increasing Pressure
  • Improve Stability without sacrificing Return
  • Reduce Pressure while keeping D-NAV above X

Controls in the optimizer:

  • Goal: choose which metric to optimize (D-NAV, Return, Pressure, or Stability deltas)
  • Constraints: guardrails that must not be violated (e.g., don't increase Pressure; don't decrease Return or Stability)
  • Threshold: minimum acceptable floor (e.g., keep D-NAV at least a target value)
  • Urgency-up opt-in: higher urgency often raises Pressure, so the opt-in keeps it explicitly guarded

What you'll see:

  • Recommendation label: e.g., Best feasible nudge: Confidence 6 → 7
  • Expected deltas across D-NAV, Return,Pressure, and Stability
  • Driver list (Top 3) that explains which sliders move the outcome
  • Narrative insight: e.g., “Recommendation: Confidence 6 → 7. Expected deltas: ΔD-NAV +4.0, ΔReturn +0.0, ΔPressure −1.0, ΔStability +1.0. Why: improves survivability by raising confidence; reduces execution stress without lowering return.”

System Compare (Adaptation & Entities)

Scenario Compare is local: it shows how a single decision or slider configuration differs from your base case. System Compare looks at judgment physics over time or across entities.

  • Adaptation Compare — same entity, different period. Shows how average Return, Pressure, Stability, category weights, and archetype mix shift between snapshots.
  • Cross-Company Compare — two entities in the same period. Shows how posture and archetype patterns diverge even if headline outcomes look similar.

In both cases we focus on three deltas: ΔR/ΔP/ΔS (posture), Δ category weight (where judgment load moved), and Δ archetype mix (behavioral identity).

Learning & Momentum

Track short-, mid-, and long-horizon learning signals across your decision stream and by category

Learning Curve Index (LCI)
Rebound / Drawdown

Recovery efficiency after dips

>1.0Over-recovery
≈1.0Full recovery
<1.0Under-recovery

Learning Curve Index (LCI) measures recovery efficiency after dips using Rebound / Drawdown. Values > 1.0 mean over-recovery (you bounce back higher than before), values around1.0 mean full recovery, and values < 1.0 mean under-recovery (you don't fully repair the damage).

Momentumn
slope(MAn(D-NAV))

Trend velocity over the last n decisions via least-squares slope on a moving average

Also calculated for Return, Stability, Pressure

Additional Metrics

Moving Averages

MAn(X) = rolling average

EMAn(X) = faster, recent-weighted

Cross-Category Effects

Decisions in one arena can influence another (attention/energy budgets). We show both global momentum and per-category momentum.

Defaults: short = 15, mid = 50, long = 100 decisions. Short = steering; mid = course; long = climate.

Recovery Metrics

We track how the system behaves immediately after setbacks:

  • Decisions to recover — average number of decisions it takes to return to baseline after a negative dip.
  • Win rate after dips — percentage of follow-on decisions after a drawdown that improve the situation rather than worsen it.
  • Decision debt — share of decisions that leave a lasting negative footprint even after recovery attempts. High decision debt means bad calls cast a long shadow over the system.

Decision Archetypes

Each outcome is defined by the signs of Return, Pressure, and Stability (R–P–S). Together they describe how the decision felt to make (pressure), how safe it left the system (stability), and whether it created value (return).

Legend: P+ = pressured, P0 = balanced, P- = calm; S+ = stable footing, S0 = uncertain footing, S- = fragile footing; R+ = gain, R0 = flat, R- = loss.

Breakthrough

R+P+S+

Gain with stable footing; pressured execution.

Advance

R+P0S+

Gain with stable footing; balanced execution.

Harvest

R+P-S+

Gain with stable footing; calm execution.

Sprint

R+P+S0

Gain with uncertain footing; pressured execution.

Build

R+P0S0

Gain with uncertain footing; balanced execution.

Coast

R+P-S0

Gain with uncertain footing; calm execution.

Gamble

R+P+S-

Gain with fragile footing; pressured execution.

Moonshot

R+P0S-

Gain with fragile footing; balanced execution.

Prospect

R+P-S-

Gain with fragile footing; calm execution.

Grind

R0P+S+

Flat with stable footing; pressured execution.

Maintain

R0P0S+

Flat with stable footing; balanced execution.

Idle

R0P-S+

Flat with stable footing; calm execution.

Firefight

R0P+S0

Flat with uncertain footing; pressured execution.

Routine

R0P0S0

Flat with uncertain footing; balanced execution.

Drift

R0P-S0

Flat with uncertain footing; calm execution.

Strain

R0P+S-

Flat with fragile footing; pressured execution.

Wobble

R0P0S-

Flat with fragile footing; balanced execution.

Teeter

R0P-S-

Flat with fragile footing; calm execution.

Overreach

R-P+S+

Loss with stable footing; pressured execution.

Erode

R-P0S+

Loss with stable footing; balanced execution.

Complacency

R-P-S+

Loss with stable footing; calm execution.

Burn

R-P+S0

Loss with uncertain footing; pressured execution.

Waste

R-P0S0

Loss with uncertain footing; balanced execution.

Leak

R-P-S0

Loss with uncertain footing; calm execution.

Meltdown

R-P+S-

Loss with fragile footing; pressured execution.

Collapse

R-P0S-

Loss with fragile footing; balanced execution.

Decay

R-P-S-

Loss with fragile footing; calm execution.

Notation Guide

Mathematical symbols and abbreviations used throughout D-NAV

ΔX

Delta (change)

Compare: XScenario − XBase. Time series: Xt − Xt−1.

MAn(X)

Moving Average

Rolling average over the last n decisions

EMAn(X)

Exponential Moving Average

More weight on recent points

Momentumn(X)

Momentum

Least-squares slope of MAn(X) (positive = trending up)

LCI

Learning Curve Index

Rebound / Drawdown around local dips

Loss Streak

Loss Streak

Consecutive count of Return < 0

DDI
Decision Debt Index
Proportion or index of decisions that continue to impose negative drag after recovery attempts.