Kumu / Micro/Macro Econ
Micro- & Macro-Economic Foundations for Managers
This course teaches economics the way you'll actually use it — as a consultant advising someone who has a decision to make. A small farm deciding what to plant is the graded case engagement. Two more sit alongside it and are entirely optional — a seed company deciding what its patent is worth, and an investigation into where rideshare and taxi earnings really go — for anyone who wants to pursue the material further. Your team then closes the term by running a full case of its own.
Elasticity, market structures, AS–AD, trade, and policy all appear — always in service of a recommendation somebody could act on. And every engagement produces real work-product: AI-assisted, version-controlled, and committed to a public portfolio repo that is yours and keeps working long after the semester ends.
Stage 0 — get GitHub working, before any case
Every engagement below is handed in by committing it to a public repository of your own, so the repository is the first thing this course builds — not a step inside Case 1, but the course's own opening move. One sitting, once, and it is the only setup you do all term. Nothing else here works until it is done.
It comes in two parts: the account, then the workspace.
Part 1 — Get a GitHub account never used GitHub? start here
Create the account and learn the three verbs — add → commit → push.
If you already have a GitHub account you can use that: sign in, add your
hawaii.edu address, and go straight to part 2. If not, sign up with that
hawaii.edu address — it is what unlocks GitHub Education.
Part 2 — Set up your workspace
Stand up the public repo every engagement this term — and every engagement after it — lands in. Built to the standard, named for you, and finished before the first case starts.
Part 1 in full: the Git mechanics walkthrough
Start Here → Git mechanics walks the whole workflow once: local versus remote, add → commit → push in the desktop app and on the command line, editing files straight on github.com, and fixes for the mistakes everyone makes the first time. Read it before part 2 — the workspace page assumes you can already save work to a repo, and does not re-teach the mechanics.
The arc — the graded case, two optional ones, the team case, and your own research
Only the first case is graded. The second and third are optional — same rhythm, same repo, no submission window — and they are there because the argument is worth following to its end, not because anything requires them.
The three cases are one argument told three times. In the first, nobody has any power over price and the only question is how much. In the second, a patent hands one firm the power to choose the price, and the arithmetic — and the welfare consequences — change entirely. In the third, the profits look real until you count what the owner gave up, and most of them turn out to be rent belonging to whoever holds the scarce asset. Then the team case runs the same method on something nobody has pre-solved — and the research paper asks you to do it once more alone, on a global challenge you pick yourself.
Perfect Competition — Decision Analysis price taker · P = MC
You run a 1.5-acre market garden and have to commit the season's planting. Build the marginal-cost engine, find where P = MC for each crop, let Solver pick the mix — then explain why it's right.
Imperfect Competition — Pricing Power Analysis market power · MR = MC optional
Optional · ungraded · self-paced. One company, two market structures: commodity corn seed sold at P = MC against patented GMO seed priced at MR = MC. An ~83× profit gap, a 4.3× markup, and a deadweight-loss verdict you have to argue rather than assert.
Economic Profit & Rent — Earnings Analysis opportunity cost · rent optional
Optional · ungraded · self-paced. Four working lives taken from accounting profit down to economic profit — then the NYC taxi medallion's climb past $1M and its 70% collapse, reproduced by a single division: rent ÷ required return.
Team Case Study team · shared repo
A geopolitical challenge, pitched. The deck is the deliverable — generated from a ten-slide content map and a design spec the team commits first, then audited against both. Built in a shared team repo through branches and pull requests, and presented live with a discussion you moderate.
Individual Research Paper individual · your own topic
A global challenge of your choosing, explained with this course's economics and closed with a recommendation. Same repo workflow as the cases — brief, spec, dated drafts, prompt log — at full scale, with the rails off.
What every engagement produces
Same shape every time, which is the point — by the second case the structure is muscle memory and you can spend your attention on the economics instead of the process. Everything lands in one public portfolio repo, organized by capability and engagement rather than by course, so it keeps working after the semester ends.
| Stage | What you produce | Where it lives |
|---|---|---|
| Brief | The problem in your own words + a hypothesis you can be wrong about, committed before you model anything | docs/briefs/ |
| Build | The spec a model can build from, and the audited workbook that satisfies it | capabilities/<capability>/ |
| Report | The evidence with figures, the memo with the recommendation, and the prompt log | analysis/ · docs/decisions/ |
Briefs ask; specs define; memos answer. And capabilities are what you can do while engagements are evidence — each capability folder names the work that exercised it, which is the line a reader follows from a claim to its proof. Stage 0 walks the whole setup, once, before the first case.
Course tools
Worth opening before your first engagement, and returning to during it.
AI Tools Lab
Chat versus code tools, web versus desktop versus command line, and — the part that matters most here — how to hand a specification to an AI so it builds the artifact you actually specified. Read it before your first build session.
GitHub & your portfolio repo
The repository every engagement lands in, and how it is organized — by capability and engagement rather than by course. The Git mechanics page covers local versus remote, committing, pushing, and how work is submitted.
Excel Solver
Ten minutes, once, before Case 1 needs it: installing Solver on Windows or Mac — and finding out now rather than then that Excel for the web has none — plus what GRG Nonlinear is really doing when it walks uphill from wherever you left the numbers.
Kumu knows this course
Ask the ✳ tutor to explain elasticity with your own product's numbers, stress-test the hypothesis in your brief, or find the ambiguity in a spec before an AI builds the wrong thing from it — all of it logged for your prompt log.
House rules
- Prompt log: all AI use logged — research, drafting, critique, visualization.
- Verify: every AI-supplied statistic gets checked against a trusted source before it enters your analysis.
- AI as supplement: the analysis and the judgment are yours; misuse is treated as an integrity violation.
- Weights, dates, and rubrics live on your syllabus, not here — this site teaches the method, your course sets the terms.