Readme.md

What Problem This Solves

Most business decisions fail not because of bad models, but because:

• probabilities are uncertain or disputed
• downside risk is asymmetric
• decisions are justified with intuition instead of structure

Decision Kernel Lite provides a decision-first abstraction that makes trade-offs explicit and auditable.

It does not predict. It does not optimize operations. It chooses actions.

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Core Concept

A decision is defined by four primitives:

Actions × Scenarios × Probabilities × Losses

From these, the kernel evaluates actions using three lenses:

Lens	Optimizes for
Expected Loss	Average pain
Minimax Regret	Hindsight defensibility
CVaR	Tail-risk protection

The output is a Decision Card — not a dashboard.

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What This Repository Provides

This repository includes:

• a pure decision kernel (no ML, no forecasting)
• three mathematically sound decision rules
• a Streamlit UI for rapidž input → decision
• an explicit rule-selection heuristic
• a copy/paste Decision Card suitable for exec decks or memos

This is not analytics. It is decision intelligence.

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Decision Rules — When to Use What

Expected Loss (Risk-Neutral)

Use when:

• decisions repeat frequently
• probabilities are reasonably trusted
• variance is acceptable

Optimizes:

• long-run average outcomes

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Minimax Regret (Robust / Political Safety)

Use when:

• probabilities are unreliable or contested
• decisions are one-shot or high-accountability
• post-hoc defensibility matters

Optimizes:

• “I should not regret this choice”

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CVaR (Tail-Risk Protection)

Use when:

• rare bad outcomes are unacceptable
• downside is asymmetric (ruin, safety, bankruptcy)
• survival > average performance

Optimizes:

• average loss in the worst cases

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Heuristic Rule Recommendation

The system includes a simple, transparent heuristic:

• if tail risk dominates average risk → recommend CVaR
• otherwise → recommend Expected Loss

The recommendation is advisory only and can be overridden.

Governance is preserved.

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Repository Structure

decision_kernel_lite/
├── app.py               → Streamlit application
├── requirements.txt     → minimal dependencies
├── Dockerfile           → containerized deployment
├── README.md            → this file
├── Executive_brief.md   → executive narrative
└── Technical_brief.md   → math + implementation

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How to Run

Local

pip install -r requirements.txt
streamlit run app.py

Docker

docker build -t decision-kernel-lite .
docker run -p 7860:7860 decision-kernel-lite

Open: http://localhost:7860

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Deployment

Works on:

• Hugging Face Spaces (Docker SDK)
• local Docker
• any environment that supports Streamlit

No external services required.

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What This Is Not

Decision Kernel Lite deliberately excludes:

• forecasting models
• machine learning
• optimization solvers
• domain-specific logic

Those belong upstream or downstream.

This kernel is intentionally domain-agnostic.

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Positioning

Decision Kernel Lite is designed to be:

• embedded downstream of forecasts
• embedded upstream of optimization
• used standalone for high-stakes choices

It is the decision layer in a larger Decision Intelligence stack.

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Summary

This system delivers:

1. a clear action recommendation
2. multiple risk-aware justifications
3. explicit trade-offs between lenses
4. a governance-ready Decision Card
5. a deployable, minimal interface

Decisions are not predictions. They are commitments under uncertainty.

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