Quantum Cognition (BADM 590 Pt 1) — Instructional Activity Roster

Source: Instructional Activity Roster.xlsx (emailed by ncyang@illinois.edu, 2026-09-08) Ingested: 2026-09-08 (manual — no Box sync configured for this course) Editing policy: Email a revised .xlsx to msbai@illinihunt.org to update. To enable automatic sync (~4x/day), coordinate with T&L to upload the xlsx to Box in the same structure as badm554/bdi513/fin550.

Status column not tracked (no Box sync pipeline). Contrast with auto-synced courses (badm554, bdi513, fin550) where the Notes column carries Defined → Scripted → Recorded → Edited → Final from Box.

Naming convention

M[#]I[#] = Module # · Item #. Each module has up to 10 slots; intro/conclusion use N/A. Item types: Video; Reading; Jupyter Notebook; Assignment; Live Session; Project Studio; Other.

Course-level items

Sequence Title Item Type Final Length Notes
N/A Course Introduction Video 3 min The hidden assumption, the four-week arc, the synthetic-panel audit project, and the AI Attribution Log. Studio recording.
N/A Course Conclusion Video 3 min The discipline that travels: local judgment, competing mechanisms, discriminating test, evidence boundary, governed action.
N/A Meet Nathan Yang Non-Lecture Video — Faculty bio video. Reuse existing Gies bio if current; otherwise record with module videos.
N/A Course Preview: Quantum Cognition for Human-AI Decision Teaming Non-Lecture Video — Preview: the hidden assumption, four-module arc, synthetic-panel audit project, no math/physics prerequisite, AI Attribution Log. Follow script template and pre-determined slides.

Module 1 — Why Classical Models Break Down

Sequence Title Item Type Final Length Notes
M1I1 The Hidden Assumption: Detection Versus Construction Video 7 min Consolidated script M1M1L1 (merges draft L1 + L2). Studio recording, 10 segments.
M1I2 Three Shapes of Classical Failure Video 7 min Consolidated script M1M1L2 (draft L3, closing edited).
M1I3 From a Catalog of Biases to One Framework Video 7 min Consolidated script M1M1L3 (merges draft L4 + L5; cut overextension, sure-thing, and duplicate interference segments).
M1I4 When Quantum Earns Its Place Video 7 min Consolidated script M1M1L4 (draft L6).
M1I5 Reading: Quantum Cognition for Management, Ch. 1-6 Reading — Decision default, anomalies, unified view, axioms, Northwind case. Selected manuscript chapters via Canvas plus underlying journal articles.
M1I6 Interactive explorer: Three Shapes of Classical Failure Other — Interactive/Quantum_Cognition_Three_Shapes_Interactive.html; students manipulate bracket, order, and separability examples before Diagnostic Post 1.
M1I7 Live Session 1: Why Classical Models Break Down Live Session 90 min See M1S1.
M1I8 Project Studio 1: Company selection and problem framing Project Studio 90 min Instructor sign-off on scope. See M1S2.
M1I9 Weekly Diagnostic Post 1 and peer response Assignment — Canvas discussion; AIAS 2. See M1A1.

Module 1 — Learning Objectives

ModLO Learning Objective Topic ModTopic
M1LO1 Explain the measurement assumption embedded in decision analytics and distinguish detection from construction of judgment. The hidden assumption; detection versus construction M1T1
M1LO2 Identify the three shapes of classical failure (out-of-bracket results, order dependence, non-separability) in a real business decision. Three shapes of classical failure M1T2
M1LO3 Explain why a catalog of biases cannot close and describe incompatibility as the unifying primitive with classical probability nested inside. From a catalog of biases to one framework M1T3
M1LO4 Evaluate a proposed quantum-cognition application against three evidentiary tests (parameter-free signature, cross-task transfer, off-switch) and separate quantum cognition from quantum computing, games, and quantum-inspired ML. When quantum earns its place: the evidence standard M1T4
M1LO5 Select a real, publicly documented company decision and state a preliminary construction hypothesis for the project. Project framing: company selection and decision problem M1T5

Module 2 — Contextuality and Order Effects

Sequence Title Item Type Final Length Notes
M2I1 Reversed Questions: Slate Order and Frame Order Video 7 min Consolidated script M1M2L1 (merges draft L1 + L2; keeps Calloway example, drops widespread-evidence and rivals segments now covered in M2I3).
M2I2 What Non-Commuting Judgments Mean Video 7 min Consolidated script M1M2L2 (draft L3).
M2I3 The QQ Equality and Its Classical Rivals Video 7 min Consolidated script M1M2L3 (merges draft L4 + L5; keeps worked q = 0 example, query theory, measurement bias, four evidence branches).
M2I4 Designing the Order-Effects Audit Video 7 min Consolidated script M1M2L4 (draft L6).
M2I5 Reading: Quantum Cognition for Management, Ch. 7-12 Reading — Order effects and QQ, CHSH, conjunction; Calloway, Caldera, Aluna cases. Wang & Busemeyer QQ article.
M2I6 QQ audit starter notebook (order effect, q statistic, bootstrap) Jupyter Notebook — NumPy notebook in VS Code/Colab; students reproduce M2I3 worked example and adapt to their battery. Submitted via GitHub.
M2I7 Live Session 2: Contextuality and Order Effects Live Session 90 min See M2S1.
M2I8 Project Studio 2: Battery design and proposal workshop Project Studio 90 min Peer critique of frames and battery. See M2S2.
M2I9 Weekly Diagnostic Post 2 and peer response Assignment — Canvas discussion; AIAS 2. See M2A1.
M2I10 Project Part 1: Research proposal (due end of Week 2) Assignment — File upload with AI Attribution Log; AIAS 3. See M2A2.

Module 2 — Learning Objectives

ModLO Learning Objective Topic ModTopic
M2LO1 Diagnose an order effect from the full joint response pattern and distinguish slate order from frame order. Reversed questions; slate order versus frame order M2T1
M2LO2 Explain non-commuting judgments using the projection geometry and state why A-then-B can differ from B-then-A. What non-commuting judgments mean M2T2
M2LO3 Compute the QQ statistic with a bootstrap interval and interpret it against the strongest classical rivals (carryover, query theory, measurement bias). The QQ equality and its classical rivals M2T3
M2LO4 Design a counterbalanced three-item order-effects audit with a pre-specified classical rival, precommitted verdict rules, and a plan for a flat result. Designing the order-effects audit M2T4
M2LO5 Write a research proposal that grounds two competing frames in institutional detail, names the strongest classical rival, and specifies the three-item battery. Project Part 1: research proposal and audit battery M2T5

Module 3 — Human-AI Decision Teaming

Sequence Title Item Type Final Length Notes
M3I1 Cognitive Asymmetry and the Intermediate Step Video 7 min Consolidated script M1M3L1 (merges draft L1 + L2; keeps Meridian Pay bracket and interference example, folds ownership into the close).
M3I2 Which Self the Recommender Optimizes Video 7 min Consolidated script M1M3L2 (draft L3, Juno case).
M3I3 Three Kinds of Synthetic Panel Video 7 min Consolidated script M1M3L3 (draft L4).
M3I4 Running the Audit and the Tier 2 Boundary Video 7 min Consolidated script M1M3L4 (merges draft L5 + L6; keeps three statistics, worked three-panel result, joint verdict rule, three-tier claim ladder).
M3I5 Reading: Quantum Cognition for Management, Ch. 13-17 Reading — Missing layer, interference handoff, incompatible recommenders; Meridian, Juno cases. Synthetic-respondent literature.
M3I6 Synthetic-panel audit notebook (three panels, three statistics, bootstrap, attribution log) Jupyter Notebook — Core project notebook; LLM API access required. Students run on their own battery before Project Studio 3.
M3I7 Live Session 3: Human-AI Decision Teaming Live Session 90 min See M3S1.
M3I8 Project Studio 3: Panel generation, audit troubleshooting, peer critique Project Studio 90 min See M3S2.
M3I9 Weekly Diagnostic Post 3 and peer response Assignment — Canvas discussion; AIAS 2. See M3A1.

Module 3 — Learning Objectives

ModLO Learning Objective Topic ModTopic
M3LO1 Evaluate a human-AI workflow for cognitive asymmetry and compute the interference term for an intermediate assessment step. Cognitive asymmetry and the intermediate step (Meridian Pay) M3T1
M3LO2 Audit whether a recommender reads a stable preference or constructs the incentive state it optimizes, using the two-part incompatibility screen. Which self the recommender optimizes (Juno) M3T2
M3LO3 Generate naive, classical isolated, and constructed synthetic panels from one demographic grid with a complete generation and attribution log. Three kinds of synthetic panel M3T3
M3LO4 Run the three-statistic audit notebook (focal order effect, QQ, interference) with respondent-level bootstrap, apply the joint verdict rule, and state conclusions at the correct evidence tier. Running the audit and the Tier 2 boundary M3T4
M3LO5 Troubleshoot preliminary audit results and critique a peer’s audit design using the reproducibility checklist. Panel generation, troubleshooting, and peer critique M3T5

Module 4 — Audit Verdicts and Managerial Action

Sequence Title Item Type Final Length Notes
M4I1 Reading the Three-Statistic Pattern Video 7 min Consolidated script M1M4L1 (draft L1).
M4I2 From Mechanism to Managerial Action Video 7 min Consolidated script M1M4L2 (draft L2).
M4I3 Writing the Recommendation and Documenting Revision Video 7 min Consolidated script M1M4L3 (merges draft L3 + L4; keeps scope-in-the-nouns, five-sentence summary, revision trigger and type, four-move reflection).
M4I4 Building and Defending the Ten-Minute Client Story Video 7 min Consolidated script M1M4L4 (merges draft L5 + L6; six slides compressed to four segments, defense reduced to evidence-chain method plus tier and falsification questions).
M4I5 Reading: Quantum Cognition for Management, Ch. 18-20 Reading — Real versus decorative, Harlow case, what it is and is not. Plus report, deck, and AI Attribution Log templates.
M4I6 Week 4 Session 1: Individual presentations and oral defense Live Session 90 min Required. Ten-minute talk plus five-minute Q&A per student. See M4S1.
M4I7 Project Studio 4: Debrief and synthesis Project Studio 90 min See M4S2.
M4I8 Project Part 2: Audit report, deck, and oral defense (due end of Week 4) Assignment — AIAS 4 (defense AIAS 0). See M4A1.
M4I9 Reflection memo (800 words) Assignment — AIAS 1. See M4A2.
M4I10 AI Attribution Log template and audit rubric Other — Reference materials used across Parts 1 and 2.

Module 4 — Learning Objectives

ModLO Learning Objective Topic ModTopic
M4LO1 Interpret the joint pattern of focal effect, QQ, and interference as an evidence chain while keeping the strongest classical rival in view. Reading the three-statistic pattern M4T1
M4LO2 Translate each verdict branch into a mechanism-specific managerial action with an owner, evidence tier, and off-switch. From mechanism to managerial action M4T2
M4LO3 Write an evidence-bounded recommendation and a revision log that documents how analytical judgment changed and why. Writing the recommendation and documenting revision M4T3
M4LO4 Build a six-slide, ten-minute client story and defend the verdict under questioning about frames, rivals, tier, and action. Building and defending the ten-minute client story M4T4
M4LO5 Reflect on which reasoning habits (off-switch, strongest rival, tier matching, process ownership) transfer to future AI-supported decisions. Debrief, synthesis, and reflection M4T5

Assessment Summary

Sequence Title Type Points Weight Due Notes
M1A1 Weekly Diagnostic Post 1: Where does the classical model break down? Discussion 10 10% End of Week 1 AIAS 2. One 300-400 word post (detection vs. construction, three shapes applied to real scenario) + one peer response.
M2A1 Weekly Diagnostic Post 2: Slate order or frame order? Discussion 10 10% End of Week 2 AIAS 2. Diagnose an order effect in a real setting, state strongest classical rival; peer response.
M2A2 Project Part 1: Research proposal and three-item audit battery Project Milestone 25 25% End of Week 2 AIAS 3 with AI Attribution Log. Individual. Real company + decision problem + research question + competing frames + classical rival + three-item battery + plan for a flat result. Own employer allowed if evidence is public.
M3A1 Weekly Diagnostic Post 3: Cognitive asymmetry in a human-AI process Discussion 10 10% End of Week 3 AIAS 2. Diagnose one design variable (intermediate judgment, frame order, orientation) in a human-AI workflow; peer response.
M3A2 Audit notebook dry run (Tier 1 validation on simulated data) Assignment 0 0% Before Project Studio 3 Ungraded checkpoint. Run audit notebook on classical and constructed simulated generators to confirm estimator recovers the right regime. GitHub .ipynb.
M4A1 Project Part 2: Audit report, deck, and oral defense Final Project 35 35% Presentations Week 4; report end of Week 4 AIAS 4 (AI is specimen); oral defense AIAS 0. Two panels from one demographic grid; three statistics with bootstrap; verdict at correct evidence tier; client recommendation; revision record vs. Part 1. Notebook (.ipynb), 6-10 slide deck, 10-min talk + 5-min Q&A, AI Attribution Log.
M4A2 Reflection memo (800 words) Reflection 10 10% End of Week 4 AIAS 1 (brainstorming only). What changed in analytical judgment, what prompted revision, which reasoning habit transfers.
Total     100 100%    

Live Sessions and Project Studios

Sequence Title Type Required Duration Notes
M1S1 Live Session 1: Why Classical Models Break Down Live Session Type 1 Recommended 90 min Detection vs. construction; measurement story; catalog of biases. Discussion of Module 1 videos and interactive explorer; set up Diagnostic Post 1. Attendance not required or graded.
M1S2 Project Studio 1: Company selection and problem framing Live Session Type 2: Project Studio Recommended 90 min Students pick company + decision problem + research question; first pass at classical failure. Instructor sign-off on scope before Week 2.
M2S1 Live Session 2: Contextuality and Order Effects Live Session Type 1 Recommended 90 min Non-commuting judgments; QQ equality; walk-through worked q=0 example and QQ starter notebook. Prepares for Project Part 1.
M2S2 Project Studio 2: Battery design and proposal workshop Live Session Type 2: Project Studio Recommended 90 min Workshop frames, three-item battery, randomization plan, classical rival; peer critique. Project Part 1 due end of Week 2.
M3S1 Live Session 3: Human-AI Decision Teaming Live Session Type 1 Recommended 90 min Elicitation by systems (Meridian handoff, Juno recommender); synthetic respondents; three audit statistics and Tier 2 boundary.
M3S2 Project Studio 3: Panel generation, troubleshooting, peer critique Live Session Type 2: Project Studio Recommended 90 min Generate panels; troubleshoot balance, coding, prompt symmetry; peer critique of preliminary three-statistic results. Students arrive with Tier 1 dry run completed (M3A2).
M4S1 Week 4 Session 1: Individual presentations and oral defense Live Session Type 2: Project Studio Required 90 min Ten-minute talk + five-minute Q&A per student; instructor synthesis after each. Schedule a second block if cohort exceeds six presenters. Graded component of Project Part 2; oral defense AIAS 0.
M4S2 Project Studio 4: Debrief and synthesis Live Session Type 2: Project Studio Recommended 90 min What patterns held across audits and where framework did not apply; reflection memo writing time. Part 2 report and reflection memo due end of Week 4.

Total planned live time: 8 sessions × 90 min = 720 min (12 hours)