evelyyyyynnnnn/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

What it solves

This framework addresses the need for system-level decision-making in high-stakes domains—such as financial stability, healthcare safety, and digital infrastructure—where incorrect decisions can lead to systemic failure. It integrates operations research, mathematical optimization, and applied AI to create auditable and robust decision processes.

How it works

The framework is composed of several specialized modules:

  • Optimization-Under-Uncertainty Library: Implements stochastic and robust optimization for resource allocation in hospitals and financial portfolios.
  • Decision-Audit Framework: Provides decision logging, counterfactual replay, and attribution to ensure AI-assisted decisions are interpretable and auditable.
  • Multi-Objective ICU Triage Optimization: Applies multi-objective optimization specifically to ICU triage and alert-thresholds using real-world cohorts like MIMIC-IV.
  • Decision-Framework Benchmark Suite: Measures regret, calibration, and robustness across all the frameworks to provide citable, standardized performance metrics.

Who it’s for

It is designed for researchers and practitioners in operations research, healthcare administration, and financial risk management who require mathematically grounded, auditable AI decision systems.

Highlights

  • High-Stakes Focus: Specifically targets domains where wrong decisions carry systemic consequences.
  • Auditable AI: Includes a dedicated framework for logging and attribution to move beyond "black box" AI decisions.
  • ** uma-Objective Optimization**: Uses multi-objective optimization for critical healthcare triage.
  • Standardized Benchmarking: Includes a built-in suite to measure robustness and calibration.

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