Quantum Cognition — Part 1 of “Quantum Approaches for Decision Making”
Program-level details: See program/curriculum.md
Combined 2026-06-01: This course is now Part 1 of the combined 8-week, 4-credit course “Quantum Approaches for Decision Making” (Part 2 = Quantum Computing, Abhijeet Ghoshal). Full content merge of the two part-files is a pending follow-up. See DECISIONS.md “Spring 2027 sequence revised.”
| Credits: 4 (combined course; this part ≈ Weeks 1-4) | Term: Spring 2027 (Weeks 1-4, from Jan 19 / POT A) | Instructor: Nathan Yang (Marketing, Associate Professor and C.W. Park Faculty Fellow) |
Website/Catalog Description (Combined Course — BADM 590)
Confirmed 2026-06-10 by Maria Rodas. This is the program-level description for the full 8-week course; Part 2 detail is in quantum_optimization.md.
This course pairs two quantum frameworks for working alongside AI, each running four weeks. In the first half, you use the mathematics of quantum probability — no physics or quantum hardware required — to model why human judgment systematically departs from the classical rationality of AI systems, learning to diagnose order effects, framing effects, and conjunction fallacies, then applying an audit methodology to AI-assisted decisions in your own organization. In the second half, you learn quantum computing from the ground up — core concepts, the underlying math, and the algorithms that solve complex business problems like optimization and route planning — building and testing hands-on solutions every week. AI tools accelerate exploration throughout. Portfolio artifact: An executive-ready decision audit and a quantum optimization solution.
Course Overview
Human judgment is not broken — it is structured differently than classical models assume. Quantum cognition applies the mathematical structure of quantum probability theory (no physics required) to explain systematic, predictable departures from classical rationality: preference reversals, conjunction fallacies, order effects, ambiguity aversion. For MSBAi students building AI systems, the stakes are direct: AI models are classically rational, but the humans who use and manage them are not. That cognitive asymmetry is where AI deployments fail in practice.
This course is Part 1 of Quantum Approaches for Decision Making (Spring 2027, Weeks 1-4), running four weeks with two 90-minute sessions per week. Part 2 is Quantum Computing and Decision Making (Weeks 5-8, Abhijeet Ghoshal).
Course Structure
| Week | Live Session (90 min) | Project Studio (90 min) |
|---|---|---|
| 1 | Module 1 — Why Classical Models Break Down | Project: Framing & diagnostic hypothesis |
| 2 | Module 2 — Contextuality and Order Effects | Project: Audit instrument design |
| 3 | Module 3 — Human-AI Decision Teaming | Project: Analysis, findings & redesign |
| 4 | Project: Individual presentations & oral defense | Debrief, synthesis & open frontiers |
Live Session = Meeting 1 (Weeks 1-3, faculty content). Project Studio = Meeting 2 (Weeks 1-4, project work). Both Week 4 sessions devoted to project culmination.
Learning Outcomes (L-C-E Tagged)
| Learning Outcome | L-C-E |
|---|---|
| Explain — without mathematics — why quantum probability frameworks produce better models of human judgment than classical probability, and articulate the managerial implications | Literacy |
| Identify cognitive phenomena (order effects, conjunction fallacies, ambiguity aversion) and apply quantum cognitive models to diagnose their structural sources in real organizational settings | Literacy |
| Evaluate human-AI teaming arrangements for cognitive asymmetry failures, using quantum cognition as a diagnostic lens for where and why breakdowns occur in marketing and strategy contexts | Competency |
| Design and present an individual quantum cognitive audit of a real organizational decision process, including structured diagnosis, evidence-based redesign recommendations, and a defended oral presentation | Expertise |
| Reflect independently on the course’s analytical framework and articulate how it changes your approach to the AI systems you will design and deploy | Expertise |
Assessment
| Component | Weight | AIAS | L-C-E | Description |
|---|---|---|---|---|
| Weekly Diagnostic Posts (×3) | 25% | 2 | L | 300-400 word structured reflection per week applying quantum cognitive framework to a real-world decision scenario. Individual. |
| Project Milestone Briefs (×3) | 25% | 3 | C | Progressive individual deliverables: project brief (Wk 1), audit instrument (Wk 2), draft findings (Wk 3). AI-integrated with required Attribution Log. |
| Final Audit Deck + Oral Defense | 30% | 3/0 | E | Individual 8-10 slide presentation + 5-min Q&A (no AI during Q&A). Deck = AIAS 3 with Attribution Log; oral = AIAS 0. |
| Individual Reflection Memo | 10% | 1 | E | 800-word memo after presentations: what surprised you, what you’d do differently, how this changes your approach to AI systems. |
| Live + Studio Participation | 10% | — | L/C | Quality of engagement in both session types; contribution to peer critique. |
All weights fall within MSBAi 2-credit / 4-week program ranges.
AI Attribution Log required on all project milestones and the final deck. Graded component. Treated as core professional practice.
Student effort: ~10-11 hrs/week (within MSBAi 8-12 hr target).
Content Modules (Live Sessions, Weeks 1-3)
Module 1 — Why Classical Models Break Down (Week 1) Marketing application: Pricing research and willingness-to-pay elicitation. Managerial takeaway: treat pricing survey results as state-dependent constructions rather than stable preferences.
Module 2 — Contextuality and Order Effects (Week 2) Marketing application: Customer journey design and the sequencing of brand touchpoints. Managerial takeaway: customer journey design is quantum measurement design — the sequence of brand touchpoints (not just their content) determines attitudinal state.
Module 3 — Human-AI Decision Teaming (Week 3) Marketing application: AI-generated personalization and the erosion of consumer trust. Managerial takeaway: AI personalization failures are often cognitive architecture mismatches, not accuracy failures.
Proposal status: v12 approved by Vishal (2026-04-27); Maria Rodas to submit to Amanda Brantner for formal academic approval. See DECISIONS.md for full decision record.