Model Integration Flow
IQPMeasures Impact
→
DIEMValues Economically
↔
RIMeasures Resilience
IQP produces attributable impact data that feeds DIEM's Impact Value component. DIEM's control-loss costs integrate with RI's Recovery Time Cost. RI measures the survivability of the decision system that produces impact.
IQP
Impact Quantification Protocol
Core Model
Measuring causal, attributable impact of institutional decisions
I = (Actual - Counterfactual) x Attribution +/- Confidence
▾
Fundamental Equation
I(d, Y, t) =
[ A(Y, t) - C(Y, t) ]
× α(d, t)
± CI(d, Y, t)
Purpose
IQP provides a rigorous, standardized methodology for measuring the causal impact of institutional decisions. It answers the fundamental question most performance measurement systems evade: What change in outcome is attributable to a specific decision, beyond what would have occurred without it? Traditional performance measurement conflates correlation with causation. IQP eliminates this ambiguity by requiring every impact claim to survive a counterfactual test.
Core Variables
| Symbol | Variable | Description | Domain |
|---|---|---|---|
| I(d, Y, t) | Impact | Net change in outcome Y attributable to decision d at time t | Real numbers |
| A(Y, t) | Actual Outcome | Observed, empirically measured value of outcome indicator Y at time t | Empirical |
| C(Y, t) | Counterfactual | Estimated outcome if the decision had not been made or executed | Constructed |
| α(d, t) | Attribution Coefficient | Proportion of observed difference credibly attributable to the decision | [0, 1] |
| CI(d, Y, t) | Confidence Interval | Combined uncertainty from data quality, methodology, and counterfactual error | Non-negative |
| δ | Impact Decay Rate | Rate at which impact diminishes over time after initial realization | [0, 1] |
| Dp(d, t) | Displacement | Extent to which impact in one area is offset by negative effects elsewhere | [0, 1] |
| γ(di, dj) | Interaction Coefficient | Synergy or conflict between co-active decisions in a portfolio | [-1, +∞) |
Calculation Method
- Define outcome indicators — Select measurable variables (Y) that capture the intended effect of the decision. Each decision requires at least one primary indicator.
- Establish baseline — Collect pre-decision time-series data on Y to establish trends and levels during the baseline period B.
- Construct counterfactual — Estimate what Y would have been without the decision, using one of four approved methods: Historical Baseline Extrapolation, Difference-in-Differences, Synthetic Control, or Expert Panel Consensus.
- Measure actual outcome — Collect post-decision empirical data on Y at defined measurement points within the impact horizon H.
- Calculate raw impact gap — Compute A(Y, t) - C(Y, t) for each measurement point to isolate the decision-attributable change.
- Determine attribution coefficient — Assess the share of the observed gap that is credibly caused by the decision using multi-factor attribution analysis.
- Apply decay and displacement — Adjust for impact decay over time and any displacement effects in adjacent domains.
- Compute confidence interval — Propagate uncertainties from data quality, counterfactual construction, and attribution estimation into a combined CI.
Sector Calibration
| Sector | Discount Rate | Impact Decay | Primary CF Method | Key Consideration |
|---|---|---|---|---|
| Culture | 2-3% | Low (0.02-0.10) | Expert Panel + Baseline | Non-linear outcomes; 5-10yr impact lags; self-reinforcing cultural changes |
| Heritage | 1-2% | Very low (0.01-0.05) | Baseline + Deterioration Models | Irreversibility premium applied; permanent loss = infinite counterfactual value |
| Education | 3-5% | Moderate (0.05-0.15) | Diff-in-Diff + Synthetic Control | Even small effect sizes (0.10-0.25 SD) are meaningful at scale across millions |
| Technology | 8-12% | High (0.15-0.40) | Peer Comparison + Baseline | Rapid obsolescence; network effects amplify non-linearly; J-curve trajectories |
| Government | 3-5% | Low-Moderate (0.03-0.12) | Diff-in-Diff + Expert Panel | Entire populations affected; no untreated comparison within jurisdiction |
| Finance | 6-12% | Moderate-High (0.10-0.30) | Peer Comparison + Market Data | High measurability; strong regulatory frameworks; systemic risk propagation |
Layer Integration
Primary Input
Layer 10: Impact Governance
IQP is the core quantitative engine of Layer 10, producing all impact measurement, attribution, and reporting data.
Key Output
Layer 13: Institutional Learning
IQP provides the quantified feedback signal that makes institutional learning empirical rather than anecdotal.
Upstream
Layer 2: Intent
Provides decision intent parameters, intended impact targets, and outcome indicator definitions.
Upstream
Layer 3: Decision Intelligence
Provides decision classification, risk assessment, and scenario analysis for method selection.
Downstream
DIEM Model
IQP's attributable impact data is the primary input for DIEM's Impact Value (IV) component.
Downstream
Layer 8: Resource Alignment
IQP scores drive resource reallocation toward high-impact and away from low-impact decisions.
Key Outputs
Impact Point Estimate
The attributed, counterfactual-tested net impact of each decision with confidence bounds.
IQP Score (0-100)
Standardized score enabling cross-decision comparison. 50 = on target, 90+ = exceptional.
Portfolio Impact
Aggregate impact across related decisions accounting for synergies and conflicts.
Impact Trajectory
Time-series pattern revealing impulse, ramp, step, hump, J-curve, or oscillating dynamics.
Impact Efficiency Ratio
Ratio of measured impact to resources consumed: IER = I / R. Higher is better.
Net Present Impact
Time-adjusted cumulative impact discounted to present value for economic comparability.
DM
Decision Impact Economic Model
Core Model
Quantifying the economic value of institutional decision governance
EV = Impact Value - Control Loss + Synchronization Gain
▾
Fundamental Equation
EVd =
IV
- CL
+ SG
Purpose
DIEM translates the dynamics of decision control, execution synchronization, and impact realization into monetary and utility-denominated terms. Institutions routinely measure the cost of operations but almost never measure the economic cost of losing control over a decision after authorization, or the economic value gained when execution timing is properly synchronized. DIEM answers the fundamental question: What is the economic return on governing decisions well?
Core Variables
| Symbol | Variable | Description | Source |
|---|---|---|---|
| EVd | Economic Value | Net economic contribution of a governed decision | DIEM output |
| IV | Impact Value | Monetized value of attributable impact, discounted over time horizon T | Derived from IQP |
| CL | Control Loss | Economic cost from four pathways: Drift, Override, Decay, Fragmentation | DIEM Control module |
| SG | Synchronization Gain | Value from proper timing, dependency alignment, and resource efficiency | DIEM Sync module |
| rd | Discount Rate | Rate at which future decision impact is discounted to present value | Sector-calibrated |
| CLI | Control Loss Index | Normalized score (0-1) representing severity and breadth of control loss | [0, 1] |
| SER | Sync Efficiency Ratio | Ratio of actual synchronization to theoretical optimal synchronization | [0, 1] |
| GROI | Governance ROI | Return on investment in decision governance: EVd / Decision Cost | DIEM output |
Calculation Method
- Derive Impact Value from IQP — Convert IQP's attributable impact into economic terms using Market Valuation, Shadow Pricing, Willingness-to-Pay, or Multi-Attribute Utility Function, then discount over the impact time horizon T.
- Quantify Control Loss across four pathways — Measure Drift (gradual deviation from intent), Override (unauthorized parameter changes), Decay (governance attention erosion), and Fragmentation (inconsistent implementation across units). Each: CLj = IVintended x Lj x Sj.
- Aggregate Control Loss — Sum all four pathways: CL = IVintended x SUM(Lj x Sj). Normalize to the Control Loss Index (CLI).
- Calculate Synchronization Gain — Compute three sub-components: SGtiming (optimal execution timing), SGdependency (inter-decision alignment), and SGresource (resource allocation efficiency).
- Compute Economic Value — Apply the fundamental equation: EVd = IV - CL + SG. A negative EVd indicates net value destruction.
- Calculate Governance ROI — Divide Economic Value by total Decision Cost (analysis + governance + execution + control costs). GROI > 3.0 = strong value creation.
- Report with confidence bounds — Express IV as a range [IVlower, IVupper] at specified confidence level. Propagate uncertainty through all components.
Sector Calibration
| Sector | Discount Rate | Timing Sensitivity (kt) | Dependency Sensitivity (kdep) | Primary Valuation |
|---|---|---|---|---|
| Culture | 3-5% | 0.03-0.10 | 0.05-0.15 | Multi-Attribute Utility + WTP |
| Heritage | 1-3% | 0.05-0.20 | 0.10-0.25 | Shadow Pricing (contingent valuation) |
| Education | 3-5% | 0.05-0.15 | 0.15-0.30 | Shadow Pricing (Mincer returns) |
| Technology | 8-15% | 0.15-0.30 | 0.20-0.40 | Market Valuation + Shadow Pricing |
| Government | 3-5% | 0.10-0.25 | 0.15-0.35 | Multi-Attribute Utility (Green Book) |
| Finance | 6-12% | 0.20-0.50 | 0.15-0.35 | Market Valuation (direct monetization) |
Layer Integration
Primary Input
Layer 10: Impact Governance
IQP outputs provide the attributable impact data for Impact Value (IV) calculation.
Critical Input
Layer 6: Decision Control
Control monitoring data, breach logs, and governance activity metrics feed Control Loss (CL) calculation.
Critical Input
Layer 7: Execution Sync
Timing data, dependency tracking, and resource utilization feed Synchronization Gain (SG) calculation.
Key Output
Layer 8: Resource Alignment
GROI and Decision Cost breakdowns inform resource allocation to governance activities.
Key Output
Layer 13: Institutional Learning
Patterns in CL and SG across decisions drive governance capability development priorities.
Key Output
Layer 11: Strategic Resilience
Value-at-risk from governance failure justifies resilience investment via RI model.
Key Outputs
Economic Value (EVd)
Net economic contribution of each governed decision in monetary or utility units.
Governance ROI (GROI)
Return on governance investment. >3.0 = strong, 1.0-3.0 = positive, <0.5 = redesign needed.
Control Loss Index (CLI)
Normalized 0-1 score of governance failure severity across drift, override, decay, fragmentation.
Sync Efficiency Ratio
Ratio of actual to optimal synchronization performance. 1.0 = perfectly synchronized execution.
Portfolio Economic Value
Aggregate EV across a decision portfolio, accounting for cross-decision economic effects.
CL by Pathway
Breakdown showing which control loss pathway (drift, override, decay, fragmentation) destroys most value.
RI
Resilience Index
Core Model
Measuring institutional capacity to absorb shocks and recover decision control
RI = (w1 x SAC + w2 x ARR + w3 x IRTC) / (w1 + w2 + w3)
▾
Fundamental Equation
RI =
(w1 × SAC
+ w2 × ARR
+ w3 × IRTC)
/ (w1 + w2 + w3)
Purpose
The Resilience Index provides a rigorous, standardized methodology for measuring the capacity of an institution to absorb shocks, recover decision-governing control, and adapt its strategic trajectory without catastrophic loss of mission coherence. Traditional resilience assessments treat resilience as a qualitative attribute. RI eliminates this by decomposing institutional resilience into three measurable, independently verifiable components synthesized into a single weighted index that is comparable across time, institutions, and sectors.
Core Variables
| Symbol | Variable | Description | Domain |
|---|---|---|---|
| RI(t, S) | Resilience Index | Composite resilience metric at time t for shock scenario S | [0, 1] |
| SAC | Shock Absorption Capacity | Proportion of shock absorbed without losing decision control below critical threshold. Decomposed into structural, redundancy, buffering, and anticipatory sub-components. | [0, 1] |
| ARR | Adaptive Recovery Rate | Speed and quality of decision-governing control restoration to target operating level after shock | [0, 1] |
| IRTC | Inverse Recovery Time Cost | Normalized transformation of total recovery cost. IRTC=1 means costless recovery; IRTC=0 means costs at maximum tolerable threshold. | [0, 1] |
| w1, w2, w3 | Component Weights | Sector-calibrated weights. Defaults: w1=0.35 (SAC), w2=0.35 (ARR), w3=0.30 (IRTC) | Positive reals |
| DGCL | Decision-Governing Control Level | Normalized capacity to make, execute, and control decisions per DCOS architecture | [0, 1] |
| Ms | Shock Magnitude | Maximum percentage degradation of decision control caused by the shock at peak impact | [0, 1] |
| φ(n, ρ) | Compound Shock Modifier | Reduction factor for compound shocks: 1 / (1 + ρ x (n-1)), where n = number of concurrent shocks | (0, 1] |
Calculation Method
- Define shock scenarios — Identify shock types (exogenous, endogenous, compound) with characterized magnitude, velocity, duration, predictability, and scope. Cover all four stress regimes: Normal, Elevated, Severe, Extreme.
- Measure Shock Absorption Capacity (SAC) — Assess four sub-components: Structural (architectural capacity under load), Redundancy (alternative pathways and backups), Buffering (reserves — financial, human, temporal), Anticipatory (detection and preemptive mitigation). Default weights: 0.30, 0.20, 0.25, 0.25.
- Measure Adaptive Recovery Rate (ARR) — Analyze the recovery curve shape (V, U, L, W, Nike-swoosh, or overshoot) by combining recovery speed (1 - Tr/Tr,max) and recovery quality (DGCLrecovered / DGCLtarget).
- Calculate Recovery Time Cost (RTC) — Sum four cost components: Direct (resource expenditure), Indirect (opportunity costs of suspended decisions), Synchronization (re-synchronization of desynchronized execution streams), and Trust (stakeholder confidence erosion). Normalize to IRTC.
- Calibrate weights — Apply sector-specific or context-specific weights for w1, w2, w3. Validate through sensitivity analysis.
- Compute RI — Apply the weighted formula. For multi-shock assessment, compute probability-weighted aggregate RI. For compound shocks, apply the compound modifier φ.
- Report with confidence envelope — Propagate component uncertainties into RI confidence bounds. Classify: <0.05 = high confidence, 0.05-0.10 = moderate, 0.10-0.20 = low, >0.20 = unreliable.
Sector Calibration
| Sector | SAC Emphasis | Recovery Curve | RTC Dominant Cost | Key Risk |
|---|---|---|---|---|
| Culture | Buffering + Structural | U-shaped (slow, 12-36 months) | Trust erosion | Irreplaceable cultural assets; mission drift under political pressure |
| Heritage | Structural + Anticipatory | L-recovery (permanent losses possible) | Direct + Irreversibility | Destroyed heritage is permanently lost; ARRquality=0 for irreversible losses |
| Education | Redundancy (alt. delivery) | V-shaped (fast recovery essential) | Indirect (compounding learning loss) | Each day of disruption accelerates learning loss; must recover in 2-8 weeks |
| Technology | Redundancy + Structural | V-shaped (minutes to hours for ops) | Indirect + Trust | Slow recovery = permanent user/market loss; network effects amplify damage |
| Government | Structural + Buffering | U-shaped (bureaucratic recovery) | Trust (democratic legitimacy) | Cannot cease operations; resilience failure cascades into social crisis |
| Finance | Buffering (capital reserves) | V-shaped (confidence-critical) | All four components | Individual fragility creates systemic risk; contagion accelerates with time |
Layer Integration
Primary Home
Layer 11: Strategic Resilience
RI is the core quantitative model for Layer 11. Bidirectional: RI scores inform resilience assessment; Layer 11 defines shock scenarios and triggers crisis protocols.
Critical Input
Layer 6: Decision Control
Provides real-time decision-governing control level (DGCL) data that RI tracks through shock, degradation, and recovery.
Critical Input
Layer 12: External Intelligence
Environmental signals and disruption anticipation feed SACanticipatory sub-component and shock scenario definitions.
Key Output
Layer 7: Execution Sync
Re-synchronization cost data from RTC feeds back to optimize execution synchronization post-shock.
Key Output
DIEM Model
RTC feeds into DIEM's control-loss cost component. RI measures survivability of the value-producing decision system.
Key Output
Layer 13: Institutional Learning
Post-shock RI analysis generates the learning signal L(t) in the dynamic RI formulation, compounding resilience over time.
Key Outputs
RI Score (0-1)
Composite resilience metric. >0.70 = strong (Level 4-5 maturity); <0.40 = fragile (Level 1-2).
SAC Sub-Scores
Structural, Redundancy, Buffering, and Anticipatory scores identifying specific architectural vulnerabilities.
Recovery Curve Analysis
V, U, L, W, Nike-swoosh, or overshoot pattern classification revealing recovery dynamics.
Resilience Envelope
Range of shock magnitudes and types within which the institution maintains control above critical threshold.
Stress Test Results
RI scores across Normal, Elevated, Severe, and Extreme stress regimes including compound shocks.
Dynamic RI Trajectory
Time-series tracking resilience evolution driven by learning, degradation, and investment effects.