PhD Workshop: Topics in Economic Theory
2025-26 Term 1

The topics for this course change every year.
I cover term 1; the second part is during term 2 and is taught by Ran Spiegler.

Course Description

Many of the most important decisions unfold over time. In part 1 of this course, we study how people make such decisions when faced with uncertainty, limited information, and evolving opportunities.

We focus on three fundamental challenges.
First: stopping. From accepting a job offer to selling a house or ending a search, timing is often the decision itself.
Second: searching. From browsing products to developing a technology, individuals must decide not just what to choose, but need to search for what is feasible.
Third: learning from others. People rarely make decisions in isolation – they observe, imitate, and respond to what others do.
Understanding these dynamics is key to understanding real-world behaviour in a variety of contexts, from product and technology adoption to the diffusion of misinformation.

This course brings together classic and modern models that address these questions, combining theoretical rigour with economic insight. While technically rooted, the emphasis is on understanding how decisions are made – and sometimes mis-made – in a variety of contexts, such as labour markets, online platforms, financial settings, and social environments.

The course will focus on discrete-time models with a wide variety of applications and has two main objectives. First, to develop tools to analyse discrete-time problems, which should be part of the toolkit of any economic theorist, but also those interested in cutting-edge research in macroeconomics (labour search), econometrics (sequential testing), and finance (option pricing). Second, to foster theoretical research and inspire theory-driven applied research by providing an overview of a collection of models spanning multiple topics of interest, in which time is of the essence.

In the final part of the course, students will present and discuss their research ideas. The goal is to spark questions and inspire research.

This course is aimed at PhD students interested in topics and methods, and in theory, behavioural economics, and dynamic decision-making more broadly. A good grasp of probability theory, real analysis and optimisation is assumed. The course is open to students from outside UCL; please send me an email if you are interested.

Meeting Times and Location
Monday, 9:00-11:00. Drayton House, room 321.
Meeting Dates: 29 September – 8 December 2025.

Topics (work in progress)

1. Stopping and Choosing: Stopping rules and monotone problems. Job Search. Satisficing. Diamond's Paradox.
   Lecture(s): 29 September, 6 October.
   References: Ferguson (2008 Book); McCall (1970 QJE); Simon (1955 QJE).
   Presentation. Slides. Handouts.
2. Searching: Pandora's Problem and Optimal Search. Martingales and etc. Gittins-Jones Index. Pricing with Pandora Consumers.
   Lecture(s): 13, 20 October.
   References: Weitzman (1979 Ecta); Gittins (1979 JRSS); Gittins and Jones (1979 Biometrika); Choi, Dai, and Kim (2018 Ecta).
   Slides. Handouts.
3. Social Learning: Cascades and Herds. Fads.
   Lecture(s): 27 October.
   References: Bikhchandani, Hirshleifer, and Welch (1992 JPE); Banerjee (1992 QJE); Smith and Sørensen (2000 Ecta); Bikhchandani, Hirshleifer, Tamuz, and Welch (2024 JEL); Chamley (2010 Book).
   Slides. Handouts.
4. Knowing: Knowledge and Common Knowledge. Common Prior Assumption and No-Trade Theorem. Universal Type Space. Learning and Common Learning.
   Lecture(s): 3, 10 November.
   References: Aumann (1976 AMS); Aumann (1999 IJGT, I/II); Maschler, Solan, and Zamir (2013 Book, Ch. 9-11); Milgrom and Stokey (1982 JET); Monderer and Samet (1990 GEB); Rubinstein (1989 AER); Cripps, Ely, Mailath, Samuelson (2008 Ecta).
   Slides. Handouts.
5. Learning in Games: Fictitious Play and Best-Response Dynamics. Potential and Supermodular Games. Evolutionary Game Theory. Other Models of Learning in Games: Berk-Nash Equilibrium and Sequential Sampling Equilibrium.
   Lecture(s): 17, 24 November.
   References: TBD.
6. Project Presentations.
   Lecture(s): 1, 8 December.
   Presentations by students.

Materials will be posted below as the course progresses. Here is a list of papers: (suggestions of papers to be added are welcome!)

Paper Presentations
Most weeks, every member of the class will be required to work in a group of at most 3 people to prepare a 15 minute presentation on an assigned paper. One group will be selected at random to give the presentation at the start of the class.

• 6 October: Caplin and Dean (2011 TE) Search, Choice, and Revealed Preference and Caplin, Dean, and Martin (2011 AER) Search and Satisficing.
• 13 October: Chade and Smith (2006 Ecta) Simultaneous Search.
• 20 October: Choi and Smith (2024) The Economics of Web Search.
• 27 October: Durandard, Vaidya, and Xu (2025) Robust Contracting for Sequential Search.
• 3 November: Chen (2025 AER) Sequential Learning under Informational Ambiguity.
• 10 November: Penta and Zuazo-Garin (2022 REStud) Rationalizability, Observability, and Common Knowledge or Morris, Rob, and Shin (1995 Ecta) p-Dominance and Belief Potential.
• 17 November: Steiner Stewart (2008 TE) Contagion through learning or Oyama, Sandholm, and Tercieux (2015 TE) Sampling best response dynamics and deterministic equilibrium selection or Oyama and Yamamoto (2020 Ecta) Generalized belief operator and robustness in binary-action supermodular games.
• 24 November: Robalino and Robson (2016 AER) The Evolution of Strategic Sophistication (may want to check Stahl (1993 GEB) Evolution of Smartn Players).

Project Presentations
The project presentations are to be structured as follows: 10min project idea (motivation, model sketch, target results, proof intuition if available); 5min discussion.