Decision Systems Laboratory

Context

Abstract

This publication documents the design and validation of an end-to-end decision architecture linking econometric forecasts, scenario simulation and allocation governance. Evidence precedes design commitments. Results are tied to operational constraints observed in institutional allocation cycles.

Decision Systems · 2024 — Present · Completed

I · Context

Institutional teams run forecasts, scenarios and allocation rules in separate tools — fragmenting decision logic and destroying auditability.

Governance committees cannot replay prior decisions
Reconciliation latency between forecast updates and allocation memos
Silent model-version drift across disconnected systems

Decision landscape

Committee A

Forecast tool

Scenario deck

Allocation memo

Manual reconcile

Actors, tools and artifacts in the pre-unification state. Friction points marked at reconciliation handoffs.

Operational context for decision systems integration — Fig. 1 — Three disconnected systems feeding one committee cycle. Internal audit, 2023.

II · System

System boundary

External inputs

Market data
Policy shocks
Governance constraints

Decision architecture

Forecast module
Scenario simulator
Allocation engine
Metadata & audit layer

Outputs

Allocation decisions
Audit reports
Sensitivity bounds

In scope: Forecast → scenario → allocation pipeline · Versioned metadata · Replay API

Out of scope: Trade execution · HR systems · CRM

Unified pipeline with explicit I/O and metadata layer spanning all stages.

The system is one pipeline — not three tools synced by spreadsheet.

Internal composition

Governance & audit API

Decision replay, version trail, committee access

Allocation rule engine

Rule evaluation, constraint checks, memo generation

Scenario simulation core

Stress libraries, path sampling, coverage metrics

Forecast & feature ingestion

Estimation, feature store, model registry

Layer dependencies — governance spans all operational modules.

III · Evidence & Design

Evidence · pre-design readings

Reconcile latency

4.2 days

baseline cycle

Forecast RMSE

0.18

holdout

Scenario coverage

62%

library gaps

Version drift

11 / qtr

undocumented

Design thesis

Unify the three decision stages under one metadata layer so every allocation records its full provenance — inputs, model versions, and sensitivity bounds.

Architecture

Metadata & lineage

Shared across all stages

Forecasting

Estimation · features · registry

Scenario simulation

Libraries · sampling · stress

Allocation rules

Constraints · evaluation · audit log

Fig. 6 — End-to-end architecture. Metadata layer spans forecast, scenario and allocation stages.

Rejected alternatives

Alternative	Appeal	Why rejected
Monolith ETL	Simplicity	No scenario coupling
Best-of-breed tools	Flexibility	Reconciliation cost
Spreadsheet orchestration	Familiarity	No audit trail

IV · Outcome

Chronology

Research2023 Q1Complete
Prototype2023 Q3Complete
Staging2024 Q1Complete
Pilot2024 Q3Complete
Production2025Rolling

Operational pipeline

IngestFeature materialization
ForecastModel run & calibration
ScenarioPath simulation
RulesConstraint evaluation
DecisionAudit log & memo

Implementation

Module	Role	Status	Tests
ingest-svc	Data intake	Production	142
forecast-en	Estimation	Production	89
scenario-sim	Simulation	Staging	56
rule-eval	Allocation	Pilot	34

Reconciliation latency

4.2 → 0.6 days

−86% vs baseline committee cycle

Results · full record

Latency

−86%

vs baseline

Calibration

0.94

posterior

Coverage

98%

scenario lib

Stability

3-regime

validated

IV · Outcome

Allocation committees now replay any decision with full input provenance — not reconstructed from email and spreadsheet memory.