The course is intended to be taught as a college level eductional foundation in Decision Intelligence. It is designed for approximately 13 sessions. It can be delivered over 3 days in a Executive Educational format. A high level introduction is offered as a 2 hour lecture at prominent US institutions.

Topics include decision equivalence mappings, optimization under subjective value, multi-criteria tradeoffs, and real-world applications across policy, economics, and intelligent systems.

This course is built around the foundational text on Decision Intelligence and the Equivalence Principle, providing both theoretical depth and practical frameworks for hyper-personalized decision optimization.

Abstract (to be added):
In an era dominated by predictive and generative artificial intelligence, most systems deliver answers that are statistically plausible yet rarely know if it is the right decision for a specific individual, context, or moment in time. Decision Intelligence: Right and Hyper-Personalized introduces a fundamentally different paradigm, grounded in a simple but powerful principle: decisions are tradeoffs. Without explicit tradeoffs, there is no true decision, only close enough approximation. This book formalizes the Decision Tradeoff Equivalence Principle (DAT) and presents a rigorous, mathematically grounded framework for Decision Intelligence that integrates tradeoffs, expert knowledge, and explainable AI into a unified architecture. Moving beyond black-box optimization and “good enough” outcomes, the framework enables hyper-personalized decisions that reflect what truly matters to each individual or organization. Through clear conceptual foundations, formal proofs, functional decision matrices, and real-world examples, (including healthcare treatment selection) the book demonstrates how explicit tradeoffs transform data, knowledge, and preferences into defensible, transparent, and auditable decisions. Explainability is treated not as an afterthought but as a structural property, enabling reverse engineering, sensitivity analysis, and inference of underlying preferences. By distinguishing Decision Intelligence from predictive and generative AI, this work defines a new class of AI systems designed to deliver the right, hyper-personalized decision.

Fadi Micaelian is an entrepreneur with a seasoned background in enterprise software. He served in executive positions at Oracle, BroadVision and Intellectual Ventures. He had leadership roles in several startups with successful exits including ViewStar (now OpenText), and DataBeam (now IBM). And founded Auguri Corp., Viteds LLC and Sparkdit Inc. Fadi is also a distinguished scholar and an inventor, whose work has been published. He was awarded several highly cited patents that are essential in AI/ML and Decision Intelligence. He lectures at ASU, USF and MIT. He is passionate about Technology, Physics and Track and Field. Fadi holds a BS in Physics, a BE in Civil Engineering, and obtained an MS in Engineering from MIT, and an MBA from INSEAD.

Students gain direct exposure to original theoretical contributions, real-world case studies, and emerging research directions shaping the future of decision-centric AI.