PhD Workshop: Topics in Economic Theory 1
2026-27 Term 1

The topics for this course change every year.


Course Description

Many of the most important decisions unfold over time. 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 when 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 mostly focus on continuous-time models with a wide variety of applications and has two main objectives. First, to develop tools to analyse continuous-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: 5 October – 14 December 2026. No class on 30 November.

Topics (work in progress)

1. Stopping: when should an economic agent act?
   Introduction to continuous-time stochastic processes, Brownian motion, and and stochastic calculus. Ito integration and Ito's lemma, infinitesimal generator, Dynkin's formula, DPP, HJB, optimal stopping.
   Lecture(s): 5 October –.
   References: Branco Sun Villas-Boas (2012 MnSc), McDonald Siegel (1986 QJE), Dixit (1989 JPE), Dixit Pindyck (1994 ch. 5), Weeds (2002 REStud), Seel Strack (2013 JET). Oksendal (2003 ch. 7, 10), Pham (2009 ch. 1, 3, 5), Karatzas Shreve (1998).
2. Learning: when is the evidence good enough?
   Filtering, coupling, time change, Girsanov's theorem, Brownian bridge.
   Lecture(s): TBD.
   References: Gonçalves (WP), Cripps Gonçalves (WP), Fudenberg Strack Strzalecki (2018 AER). Peskir Shiryaev (2006 ch. II, III).
3. Controlled Learning: how much effort and attention to allocate?
   Introduction to optimal control. Controlled infinitesimal generator.
   Lecture(s): TBD.
   References: Liang Mu Syrgkanis (2022 Ecta), Ke Villas-Boas (2019 JET), Moscarini Smith (2001 Ecta), Morris Strack (WP). Oksendal (2013 ch. 11).
4. Search: what to search next and when to stop?
   Lecture(s): TBD.
   References: Callander (2011 AER), Callander Hummel (2014 Ecta), Urgun Yariv (2025 JPE). Oksendal (2013 ch. 11).
5. Experimentation: when to explore new alternatives and when to exploit known ones and how does this trade-off change in strategic settings?
   Lecture(s): TBD.
   References: Bolton Harris (1999 Ecta). Karatzas (1984 AnnProb), Oksendal (2013 ch. 11).
6. Conflict: how do opposing parties dynamically manage conflict in a stochastic world?
   Introduction to viscosity solutions. Comparison/maximum principle.
   Lecture(s): TBD.
   References: Gul Pesendorfer (2012 REStud), Barilla Gonçalves (2024 TE). Crandall Ishii Lions (1992 BullAMS), Pham (2009 ch. 4).
7. Social Learning: how can we learn from others' actions?
   Lecture(s): TBD.
   References: Laiho Murto Salmi (2025 TE), Murto Välimäki (2013 JET).
8. Pricing and Market Experimentation: how to price new products and in oligopolistic dynamic markets?
   Lecture(s): TBD.
   References: Bergemann Välimäki (2006 JPE), Bergemann Välimäki (2000 REStud).
9. Bargaining and Dynamic Adverse Selection: how do private information and bargaining power determine agreement, delay, and breakdown?
   Lecture(s): TBD.
   References: Daley Green (2012 Ecta), Daley Green (2020 AER), Ortner (2019 GEB).
10. Career Concerns and Reputation: how to manage reputation over time?
    Lecture(s): TBD.
    References: Holmstrom (1999 REStud), Board Meyer-ter-Vehn (2013 Ecta), Cisternas (2018 AEJMicro), Faingold Sannikov (2011 Ecta).
11. Moral Hazard and Private Experimentation: how to design incentives in dynamic settings?
    Lecture(s): TBD.
    References: Sannikov (2008 REStud), Georgiadis (2015 REStud), McClellan (2022 Ecta).
12. Information Design: how to persuade dynamically?
    Lecture(s): TBD.
    References: Escudé Sinander (2023 TE), Urgun Whitmeyer (WP).
13. Learning in Games 1: Fictitious Play and Best-Response Dynamics.
    Lecture(s): TBD.
    References: Fudenberg and Levine (1998 Book, Ch. 2-4; 2009 ARE).
    Slides. Handouts.
14. Learning in Games 2: Approachability, Calibration, and Adaptive Algorithms.
    Lecture(s): TBD.
    References: Blackwell (1956); Maschler, Solan, and Zamir (2013 Book, Ch. 14); Foster and Vohra (1997 GEB, 2018 GEB); Foster and Hart (2021 JPE; 2023 TE) Hart and Mas-Colell (2003 AER); Hart (2005 Ecta).
    Slides. Handouts.
15. Project Presentations.
    Lecture(s): 7 and 14 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.

• 14 October:
• 21 October:
• 28 October:
• 4 November:
• 18 November:
• 25 November:

Project Presentations
The project presentations are to be structured as follows: 20min project idea (motivation, model sketch, target results, proof intuition if available); 10min discussion.
7 December

14 December