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4.0-Decision-Intelligence-Framework

A research-oriented framework for building optimization-driven decision intelligence systems, integrating causal inference, risk modeling, and AI pipelines for large-scale socio-technical systems

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创建于 2026-03-18 · 更新于 2026-10-13 · 今日第 16897 名
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4.0 — Decision Intelligence Framework

The repository whose name matches the endeavor phrase most closely is the one least aligned with it. This rebuild makes the name true: optimization-driven, system-level decision frameworks.

Part of a five-repository portfolio supporting the endeavor described in the EB2-NIW petition: optimization-driven, system-level decision frameworks — integrating operations research, mathematical optimization and applied AI — for domains where a wrong decision carries systemic consequences. The three pillars are financial stability, healthcare safety and secure digital infrastructure.

Petition-grade projects today 0 original, NIW-relevant projects
Verdict Rebuild around its own name

"Petition-grade" means: original work, a stated method, real data at a stated scale, a measured result, and a README a reviewer can follow. Counts exclude duplicates, forks of third-party work, retired projects, and asset-only folders.

Projects

Folder Project Pillar Evidence value
2-optimization-under-uncertainty/ Optimization-Under-Uncertainty Library Cross-cutting — the endeavor itself CORE — the endeavor expressed as code
1-decision-audit-framework/ Decision-Audit Framework Cross-cutting CORE — makes interpretability an artifact
3-icu-triage-optimization/ Multi-Objective ICU Triage Optimization Healthcare Safety CORE — joins the healthcare pillar to the OR framing
4-decision-benchmark-suite/ Decision-Framework Benchmark Suite Cross-cutting CORE — the most citable form of contribution

What each one is

1. Optimization-Under-Uncertainty Library — 2-optimization-under-uncertainty/

Stochastic and robust optimization applied to portfolio allocation and hospital resource allocation.

Why it earns its place: This is your Cornell ORIE training expressed as code, and the petition's Version 3 rests entirely on that framing with nothing to point at.

Target scale: Portfolio allocation and hospital resource allocation instances

2. Decision-Audit Framework — 1-decision-audit-framework/

Decision logging, counterfactual replay, and attribution for AI-assisted decisions.

Why it earns its place: Makes "auditable, interpretable decision frameworks" an artifact rather than a phrase.

Target scale: Decision traces from the other projects in this repository

3. Multi-Objective ICU Triage Optimization — 3-icu-triage-optimization/

Multi-objective ICU triage and alert-threshold optimization.

Why it earns its place: Connects the healthcare pillar to the operations-research framing, which is currently the weakest join in the petition.

Target scale: MIMIC-IV / eICU cohorts, shared with repo 2.0

4. Decision-Framework Benchmark Suite — 4-decision-benchmark-suite/

Regret, calibration and robustness benchmarks across the frameworks in this repository.

Why it earns its place: A benchmark is the most citable form of contribution and the easiest for others to adopt.

Target scale: All frameworks in this repository

Repository layout

4.0-Decision-Intelligence-Framework/
├── 2-optimization-under-uncertainty/
├── 1-decision-audit-framework/
├── 3-icu-triage-optimization/
└── 4-decision-benchmark-suite/

Every rebuilt project carries the same skeleton: README.md, src/, data/, results/, tests/, website/.

Earlier and off-theme prior work (dashboards, consumer apps, templates, and other non-endeavor folders) has been moved to a separate archive-prior-work repository to keep this repository focused on the endeavor. It remains recoverable from this repository's git history.

Ground rules

  1. No number without a run log. Anything cited in the petition must appear in that project's results/README.md with a run date behind it.
  2. No simulated data under a real claim. Sample data lives in data/sample/, labelled, and is never the source of a cited figure.
  3. Adoption must be documentable — named institutions, dated correspondence, registry statistics. Never an inflated count.
  4. Third-party and forked code stays labelled and is never counted.

Scaffold generated from NIW_Project_Portfolio_and_Gap_Plan.xlsx (sheets: Repo Build-Out Plan, Core Ideas at a Glance, NIW Claim vs Repo Evidence, Notion 创业 Alignment). Structure only — no results are claimed here yet.

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