This course introduces Decision Intelligence through the Decision Tradeoff Equivalence Principle, showing how “right” decisions emerge from correctly modeled tradeoffs rather than from pure optimization or prediction. Students learn how inference engines, expert knowledge, and explainable AI combine to produce hyperpersonalized, sustainable, and trustworthy decisions.

• Master the Decision Tradeoff Equivalence Principle
• Identify and formalize decision tradeoffs
• Demonstrate tradeoff equivalence across domains
• Understand the mathematics underlying decision inference
• Integrate expert knowledge into AI systems
• Explain decisions using tradeoff-based reasoning
• Design Decision Intelligence solutions for real-world use cases

Class 1 — Decisions Are Tradeoffs

The Decision Tradeoff Equivalence Principle

• Why decisions are not objectives or predictions
• Tradeoffs as the fundamental unit of decision-making
• Formal statement of the Decision Tradeoff Equivalence Principle
• Decision equivalence vs. outcome equivalence

Key Insight: Two decisions are the same decision if they encode the same tradeoffs.

Exercise: Identify hidden tradeoffs in a familiar decision.

Class 2 — Demonstrating the Principle

• Visual and numerical demonstrations of tradeoff equivalence
• Reframing different decisions into a common tradeoff structure
• When different policies collapse to the same inference
• False choice diversity

Workshop: Transform multiple decisions into a single tradeoff representation.

Assignment: Demonstrate equivalence between two real-world decisions.

Class 3 — The Math Behind Tradeoffs

• Decision spaces, outcome spaces, and tradeoff spaces
• Constraints as implicit tradeoffs
• Multi-objective formulations and Pareto fronts
• Equivalence classes in decision space

Assignment: Rewrite a constrained optimization problem as a tradeoff inference problem.

Class 4 — Healthcare Example

• Clinical decision-making as a tradeoff system
• Safety, efficacy, cost, time, and ethics
• Personalized tradeoffs across patients
• Why guideline-based optimization fails

Case Study: Treatment selection or care-path optimization.

Assignment: Map all tradeoffs in a healthcare decision and identify equivalence.

Class 5 — Expert Knowledge Augmented AI & Sustainability

• Why data-only AI is unsustainable
• Expert knowledge as constraints, priors, and rules
• Knowledge-augmented inference engines
• Sustainability as a long-horizon tradeoff

Assignment: Incorporate an expert sustainability constraint into a decision model.

Class 6 — Explainable AI

• Why explainability fails in predictive models
• Explanation vs. justification vs. transparency
• Tradeoff-based explanations
• Decision-first explainability

Key Insight: A decision is explainable only through its tradeoffs.

Assignment: Explain a decision using tradeoff contributions instead of features.

Class 7 — The Math Under Explainable AI

• Mathematical structure of explanations
• Tradeoff sensitivity and marginal impact
• Counterfactual decision equivalence
• Local vs. global explainability

Assignment: Perform a sensitivity analysis on a tradeoff-driven decision.

Class 8 — eCommerce Example

• Pricing, recommendations, and promotions as tradeoff systems
• Revenue vs. trust vs. long-term value
• Personalization without exploitation
• Decision equivalence across user segments

Case Study: Dynamic pricing or recommendation decisions.

Assignment: Analyze an eCommerce decision for hidden tradeoff collapse.

Class 9 — The Solution and Innovation Framework

• End-to-end Decision Intelligence architecture
• Inference engines vs. optimizers
• Hyperpersonalized tradeoff inference
• Innovation enabled by tradeoff visibility

Workshop: Design a Decision Intelligence solution for a real problem.

Class 10 — Comparative Analysis

• Review of all Decision Systems
• Focus on MCDA
• Compare DI with MCDA
• Compare DI with Parametric Based Solutions
• Compare DI with Artificial Intelligence

Final Deliverable: A Decision Intelligence Comparative Analysis.

Class 11 — Benefits

• Business impact and ROI
• Risk reduction and compliance
• Sustainability and fairness
• Trust, adoption, and governance
• Measuring decision quality vs. outcome quality

Final Deliverable: A Decision Tradeoff Equivalence Analysis or Decision Intelligence Solution Design.

Class 12 — Ethical Issues

• Impact of Tradeoff-based Decision Intelligence on Energy Consumption
• Impact of Tradeoff-based Decision Intelligence on Sustainability
• Impact of Tradeoff-based Decision Intelligence on Jobs

Final Deliverable: Graduation Ceremony.