Commit
Choose under uncertainty
State an assumption and make a decision before every fact is available.
Competitive Strategy · Graduate marketing
Teaching
strategic judgment.
Competitive Studio
An integrated decision-making lab built around consequential practice and structured reflection.
Dr. Jose Mendoza
Clinical Associate Professor
Academic Director, MS in Integrated Marketing
NYU School of Professional Studies
Faculty-built. AI-supported.
Open for others to explore.
Open for others to explore.
01 / The learning problem
Knowing the framework.
Making the decision.
Students need practice applying strategic frameworks when information is incomplete and other people react to their choices.
Respond
Face a reaction
Adapt when competitors or counterparties change the conditions.
Reconsider
Explain the next choice
Trace the outcome to the reasoning, then explain what should change.
The teaching challenge
Give students repeated opportunities to turn conceptual understanding into defensible strategic judgment.
Source: Mendoza, Teaching Competition Paper, “The Problem.” This is the instructional need addressed by the lab.
02 / The teaching innovation
One teaching method.
Across the course.
Framework
Brief the concept and make the strategic objective explicit.
What are we trying to achieve?Live decision
Commit under time pressure, incomplete information, and competing interests.
What will we do, and why?Consequence
Inspect the result and the responses of competitors or counterparties.
What happened?Reflection
Connect the experience to theory and revise the next decision.
Which assumption needs to change?The innovation connects practice across sessions.
Purpose-built activities share a decision and debrief structure. Students carry earlier reasoning into a culminating recommendation and defend it through peer critique.
Source: Mendoza, Teaching Competition Paper, “The Innovation.”
03 / Experience the method
A rival cuts fares.
What do you do?
You lead an airline on the JFK–Boston corridor. Your team must respond before the next round.
6 competing airlines5 roundsOne shared market
Choose a response. Be ready to explain your assumption.
Discussion prompt adapted from the airline activity. The choices do not run the live simulation.
04 / Consequences and debrief
Same revenue.
Different economics.
Airline profit
Final profit, USD millions · Spring 2026 classroom run
Classroom simulation results; not student learning scores.
$17.69MRevenue for both A and E
$2.89MDifference in reported profit
The debrief examines the reasoning.
Airline E reported $8.74M in total costs; Airline A reported $11.63M. The debrief connects pricing, capacity, product, and brand decisions to the cost structure.
Which assumption needs revision? What would you examine before the next decision?
Source: Airline Simulation Final Assessment, pp. 1–2. Reported figures are rounded. These results illustrate one classroom run, not the outcome of the preceding audience choice.
05 / Learning across sessions
A progression in
strategic reasoning.
Culminating pitch
Students carry competitive and economic reasoning into a recommendation supported by scenario analysis and AHP, then defend it through peer critique.
Learning progression: competition paper. Connections are instructional; they do not imply automatic data transfer between applications. Select a row for activity details and links.
06 / Synthesis in the culminating pitch
One recommendation.
Several kinds of evidence.
Vela has $12 million to invest. Choose one option, or split the budget across two.
AScale the hero product line
BLaunch a Gen Z sub-brand
CExpand into Southeast Asia
DBuild a DTC ecosystem
Vela Skincare is a classroom case. The budget and options are case assumptions.
Scenario planningDoes the strategy hold up
Does the strategy hold up
across plausible futures?
Choose the uncertainties; compare robustness and fragility.
AHP StudioDoes the recommendation
Does the recommendation
depend on our priorities?
Make pairwise comparisons; inspect consistency and sensitivity.
The pitch brings the analyses together. Teams justify the investment and respond to peers’ challenges to their assumptions.
Sources: Strategic Decision Lab; AHP Studio User Guide.
07 / AI supports the method
A partner in practice.
Judgment stays with the learner.
Faculty + AI
Build the environment
GitHub Copilot supported the development of purpose-built tools for the course.
Faculty define the learning objective, inspect the behavior, and shape the debrief.
Student + AI
Practice and interpret
- Negotiate with an adaptive counterpart.
- Receive coaching on inconsistent AHP judgments.
- Discuss an AI-generated explanation of group results.
Student responsibility
Question and decide
- Check the explanation against the results.
- Test the assumptions and alternatives.
- Explain why the final decision is justified.
Make the role of each component explicit.
AHP calculations and scenario robustness measures use defined computations. AI supports interpretation and practice; the negotiation tool also uses AI in some evaluation modes, so its feedback calls for review.
Sources: author’s development account; AHP Studio, Scenario Manager, and Negotiation Sim documentation.
08 / Assessment and debrief
Assessment makes
reasoning visible.
Airline activity assessment
20%Use of data
20%Competitive adaptation
30%Economic performance
30%Final strategic reflection
A question worth pursuing
One AHP reflection describes revising weights until the ranking matched the team’s initial preference.
Did the team improve its judgments, or tune the model to its preferred answer?
Ask students to justify each revision, compare the sensitivity results, and explain what evidence would change their recommendation.
This is a debrief opportunity, not a finding of misconduct. Formal evaluation of learning gains remains in progress.
Sources: Airline Simulation Final Assessment, p. 5; anonymized reflection DL22. Debrief questions are proposed for this presentation.
09 / Student-reported learning
Evidence of how students
describe their reasoning.
“
Selected anonymized excerpts from the submitted reflections. Qualitative examples of student-reported learning, not measured learning gains or a frequency estimate. Earlier percentages based on 53 reflections are excluded.
10 / The faculty takeaway
What faculty can
take into their courses.
- A repeatable teaching methodStart with one decision students struggle to make. Connect the choice, its consequences, and a structured debrief.
- Resources to adaptUse a classroom case, an existing activity, or public code. Match the context and assessment to your learning objective.
- AI as a development and learning partnerCustomize practice with AI. Faculty inspect the activity and feedback; students justify their own decisions.
Start with one objective, one activity, and one debrief.Build connections across sessions as students gain experience.Hosting and AI usage may carry costs; tool access varies.
Public repositories
GitHub ↗MIT verified in five repositories. The supplied Dynamic Pricing repository could not be verified in this review.
Repository licenses reviewed September 11, 2026. Linked licenses govern code reuse. Course materials and NYU marks retain their own rights.
11 / Your next teaching decision
What decision do your students
need to practice making?
Begin with a decision they understand in theory. Give them a chance to make it, examine the consequences, and explain their next choice.
Explore the Decision Lab ↗mma.jose-mendoza.com · presentation
Dr. Jose Mendoza
NYU School of Professional Studies
Google Higher Ed Faculty AI Fellow
Explore the tools
Scan for the public project hub.
MMA Teaching Innovation Competition · September 24, 2026 · New Orleans