Advanced Microeconomics — Course Guide

A rigorous course from individual choice and duality to games, information, mechanisms, externalities, and market design.

Advanced Microeconomics — From Choice to Institutions

Course question

How do choices become equilibrium outcomes, when are those outcomes efficient, and how should rules change when information, strategy, or external effects prevent markets from working well?

This is an upper-undergraduate course and a bridge to graduate microeconomic theory. Mathematics is used to make assumptions and mechanisms visible—not as a substitute for economic interpretation.

What you should already know

partial derivatives, total differentials, and constrained optimisation;
basic matrix notation and second-order conditions;
probability, conditional expectation, and Bayes' rule;
supply, demand, elasticity, and elementary game theory;
the difference between a positive prediction and a normative judgment.

Five-minute diagnostic

For max xy subject to 2x + y = 12, can you derive and verify the optimum?
Why do u(x) and 3u(x) + 7 represent the same ordinal preferences?
Does a Nash equilibrium have to be Pareto efficient?
What information is private in an insurance contract?
Why is a first-order condition insufficient at a corner?

If 1 or 5 is difficult, revise constrained optimisation. If 3 or 4 is difficult, keep a “prediction versus welfare” and a “public versus private information” column in your notes.

Learning outcomes

By the end, you will be able to:

derive Marshallian and Hicksian demand, expenditure, profit, and cost functions;
use comparative statics and duality to measure behavioural and welfare changes;
solve choices under risk and competitive, strategic, and informational equilibria;
distinguish efficiency, stability, incentive compatibility, and strategy-proofness;
solve benchmark oligopoly, contract, auction, public-good, and matching models;
implement a small equilibrium or allocation algorithm and verify its output;
connect theory to current evidence without treating one estimate as universal;
write a concise policy or design memo with assumptions, mechanism, incidence, and limits.

Consumers search, sellers price, algorithms learn, the platform ranks products and sells advertisements, and regulators assess market power. The case links demand, games, oligopoly, private information, auctions, behavioural design, and platform governance.

B. Climate-risk insurance

Households differ in exposure and information; insurers classify risk; prevention is partly hidden; regulation limits pricing; public policy shares catastrophic losses. The case links expected utility, adverse selection, moral hazard, externalities, and mechanism design.

Course map

Module	Core question	Main output
1. Choice and duality	What does optimisation reveal, and what can choice data recover?	demand/cost derivation
2. Comparative statics and welfare	What changes after a price or policy shock?	Slutsky and welfare calculation
3. Risk and insurance	How are uncertain payoffs valued and shared?	certainty-equivalent analysis
4. General equilibrium	Can all individual plans be mutually feasible?	exchange-equilibrium solution
5. Game theory	Which strategies are mutually optimal?	equilibrium argument
6. Oligopoly and algorithms	How do timing and market rules shape power?	model comparison
7. Information and contracts	What changes when type or action is hidden?	IC/IR contract
8. Mechanisms and auctions	Can rules make private incentives serve a goal?	truthful mechanism
9. Behavioural and experimental micro	Which benchmark assumptions fail predictably?	experiment appraisal
10. Externalities and public goods	How should social effects enter private choice?	instrument comparison
11. Matching and market design	How should scarce, indivisible positions be assigned?	deferred-acceptance run
12. Integrated design studio	How do the tools combine in one institution?	final design memo

Read every model in this order

Rendering diagram…

Use this eight-part record:

agents — who chooses?
objects — quantities, actions, reports, or matches?
preferences/payoffs — what is maximised?
constraints — resources, technology, information, or incentives?
timing and information — who knows and moves when?
solution concept — optimum, competitive equilibrium, Nash, Bayesian Nash, stability?
welfare criterion — Pareto, total surplus, social welfare, revenue, fairness?
empirical implication — what observation could reject or refine the mechanism?

An assumption defines the model.
A theorem follows from assumptions.
An estimate follows from a research design and dataset.
A recommendation also needs a welfare criterion and feasibility judgment.

Minimal technical toolkit

Tool	Used for	Check before trusting the answer
Lagrangian/KKT	constrained choice	feasibility, complementary slackness, corners
envelope theorem	value-function derivative	optimum is regular; parameter enters as stated
implicit function theorem	local comparative statics	relevant Jacobian is nonsingular
Hessian/Jacobian	curvature and stability	local versus global result
Bayes' rule	beliefs after signals	probabilities and information sets
fixed point/backward induction	strategic equilibrium	timing and off-path actions
algorithm	allocation or equilibrium computation	termination, incentives, tie-breaking

A 12-week route

Week	Preparation	Seminar or laboratory
1	choice, duality, revealed preference	derive demand and cost functions
2	Slutsky and welfare	transit-fare policy calculation
3	risk and insurance	deductible design
4	general equilibrium	Edgeworth-box allocation
5	static and dynamic games	entry-game workshop
6	oligopoly	compare Cournot, Bertrand, Stackelberg
7	information and contracts	screening menu
8	auctions and mechanisms	first- versus second-price auction
9	behavioural and experimental methods	preregister a choice experiment
10	externalities and public goods	carbon-policy hearing
11	matching	run and audit deferred acceptance
12	integration	platform-design defence

Assessment alignment

Task	Weight	What it tests
derivation problem sets	30%	setup, solution, verification
model comparison note	20%	comparative statics and welfare
evidence replication or critique	20%	theory–data connection and boundary
final market-design memo	30%	incentives, allocation, implementation, communication

For a graduate extension, add existence conditions, proof sketches, robustness to non-convexity or incomplete information, and an empirical identification discussion.

Recent evidence used in the course

The application notes draw on recent work on production networks (Baqaee & Farhi, 2024), algorithmic gasoline pricing (Assad et al., 2024), machine-learning credit models (Fuster et al., 2022), digital-ad auctions (Bergemann, Bonatti & Wu, 2025), nudges at scale (DellaVigna & Linos, 2022), and current carbon-pricing institutions (World Bank, 2026).

Each chapter states what the evidence identifies and what it does not. “Recent” does not mean “universally applicable.”

Start

Open Module 1. Before differentiating, write the agent, objective, feasible set, and the economic meaning of every multiplier.

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Capstone — Should Northbridge Expand Pathways?

Integrate lottery, cutoff and staggered-rollout evidence into an auditable policy recommendation

Module 1 — Choice, Duality, and Revealed Preference

Consumer and producer choice, value functions, envelope results, demand properties, and revealed-preference tests.