Pricing and Market Design

[Syllabus]

Course Description

Every market must decide who gets what, and on what terms. This course asks:

When do simple prices work, when do they fail, and what replaces them?

Across the course, prices will play a leading role, as tools to extract surplus, ration scarcity, decentralize allocations, correct externalities, and experiment and learn. We will combine ideas from operations research, economics, and computer science to study demand, scarce capacity, customer choice, strategic behavior, private information, and institutions such as auctions and matching. The questions we will study are increasingly important in digital platforms, where algorithms – and now increasingly autonomous agents – both learn from markets and change the data the markets generate.

This is intended to be a mathematically substantive, model-driven course for senior OR/CS undergraduates and master’s students. That said, I will aim to keep our organizing principle as economic question first, mathematical machinery second: we will introduce optimization, probability, learning, or game-theoretic tools when a market-design problem demands it. Most lectures will begin with concrete market questions, develop a model, and try to extract reusable principles rather than presenting the math in isolation.

Course Information

• Instructor: Sid Banerjee, email
• Lectures: Tuesday/Thursday, 1:25–2:40 p.m., CIS 142
• Office: 229 Rhodes Hall

Detailed dates, assessment information, and course policies will be posted when finalized.

Learning Goals

By the end of the course, students should be able to:

• formulate and solve basic pricing and allocation models using buyer values, demand, choice, and resource constraints;
• interpret LP dual variables and dynamic marginal values as prices or opportunity costs;
• analyze demand learning using concentration bounds, regret, optimism, and the feedback between decisions and observations;
• distinguish exact, fluid, and clairvoyant benchmarks, and explain how scale affects performance and tractability;
• model substitution using random-utility models and optimize simple assortments; and
• analyze strategic and informational problems and compare prices, auctions, mechanisms, reputation, and matching as market-design interventions.

Prerequisites and Background

Required background

Comfort with linear optimization, basic probability, calculus, and mathematical modeling, approximately at the level of ORIE 3300 and ORIE 3500 (or equivalent). You should know (or be willing to learn) to use LP duality and complementary slackness; random variables, expectation, conditional probability, and common distributions; and elementary calculus.

Helpful but not required

Prior exposure to economics, game theory, stochastic processes, or algorithms. Some assignments may involve computation or simulation, so familiarity with Python or a comparable language will be helpful.

References

There is no required textbook; however we will assign readings from three main references (all available online through Cornell Library):

• Rakesh Vohra and Lakshman Krishnamurthi, Principles of Pricing.
• Tim Roughgarden, Twenty Lectures on Algorithmic Game Theory.
• Kalyan Talluri and Garrett van Ryzin, The Theory and Practice of Revenue Management.

Selected course notes and papers will supplement these references, particularly for learning, online allocation, reputation, and matching.

Tentative Lecture Plan

The plan below is tentative. Topics, dates, and the division between lectures may change with pace. Lecture-note links will be activated as materials are posted.

Unit 1: Pricing, Demand, and Learning

• Lecture 1 — Aug. 25: Pricing with full information: surplus, market clearing, and congestion tolls

  • Lecture notes: [Lec 1]
  • Suggested Reading:
    • Vohra, Ch. 1 and §§2.1–2.2 [V&L]
    • T&vR, §§1.1 and 1.3 [T&vR]
    • Naor (1969) [paper]
  • Queue Lab: [game] — an interactive pricing in queues simulator

• Lecture 2 — Aug. 27: From values to demand: quantiles, virtual values, and elasticity

  • Lecture notes: [Lec 2]
  • Suggested Reading:
    • Vohra, §§4.1–4.2 and §§4.14.1–4.14.2 [V&L]
    • T&vR, selected parts of §§7.2–7.3 [T&vR]

• Lecture 3 — Sept. 1: From optimal pricing to learning: markup, greedy failure, and regret

  • Lecture notes: [Lec 3]
  • Suggested Reading:
    • Vohra, §§4.2–4.5 and §4.14.2 [V&L]
    • Slivkins, Ch. 1 [Slivkins]

• Lecture 4 — Sept. 3: Learning to price: optimism under uncertainty and UCB

  • Lecture notes: [Lec 4]
  • Suggested Reading:
    • Slivkins, §§1.3.1–1.3.3 and selected parts of Ch. 2 Slivkins

Unit 2: Scarcity, Scale, and Online Allocation

• Lecture 5 — Sept. 8: Single ressource RM and rationing (and a quick DP primer)

• Lecture 6 — Sept. 10: Network revenue management: fluid LPs and bid prices

• Lecture 7 — Sept. 15: From fluid predictions to confidence-aware decisions

• Lecture 8 — Sept. 17: Bayes Selector: predicting the clairvoyant

Unit 3: Customer Choice and Assortment

• Lecture 9 — Sept. 22: The spiral-down effect: when availability corrupts demand data

• Lecture 10 — Sept. 24: Choice models and substitution

• Lecture 11 — Sept. 29: Assortment optimization under MNL

Unit 4: Auctions, Game Theory, and Mechanisms

• Lecture 12 — Oct. 1: Posted prices versus auctions

• Lecture 13 — Oct. 6: Game theory for market design

• Lecture 14 — Oct. 8: Auction formats and strategic bidding

Oct. 13: Fall Break — no class

• Lecture 15 — Oct. 15: Truthful allocation in single-parameter environments and Myerson’s lemma

• Lecture 16 — Oct. 20: Monopoly reserves, Myerson, and simple near-optimal auctions

Unit 5: Segmentation and Richer Pricing

• Lecture 17 — Oct. 22: Observable and hidden customer types: segmentation and screening

• Lecture 18 — Oct. 27: Menus and self-selection: versioning and nonlinear pricing

• Lecture 19 — Oct. 29: Multidimensional values: bundling and multi-product pricing

Unit 6: Allocation, Competition, and Information

• Lecture 20 — Nov. 3: Multi-item allocation and combinatorial auctions

• Lecture 21 — Nov. 5: VCG: optimization plus incentives in multi-parameter environments

• Lecture 22 — Nov. 10: Pricing under competition: capacity, differentiation, and repeated interaction

• Lecture 23 — Nov. 12: Adverse selection and the market for lemons

• Lecture 24 — Nov. 17: Reputation, trust, and information in markets

Unit 7: Matching, Platforms, and Synthesis

• Lecture 25 — Nov. 19: Matching and markets without money

• Lecture 26 — Nov. 24: Platforms and market-design synthesis

Nov. 26: Thanksgiving Break — no class

• Lecture 27 — Dec. 1: Case workshop or flex lecture

• Lecture 28 — Dec. 3: Course synthesis, review, or project presentations, depending on the final assessment plan

Possible extensions, as time permits, include overbooking, finite-inventory dynamic pricing, proper scoring rules, censored-demand estimation, multi-parameter revenue maximization, and auction extensions.