MPhil Experimental Economics 2025-26

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

In this course, we will discuss an overview of several topics in experimental economics. The goal is not to cover all the active research, but to get students a background of experimental economics, and to get to the frontier on a selection of topics. Each year the topics change, with the goal that most of the material will be new for students who have previously taken the course.

This year, I will be focusing on experiments on information and choice process (time, search, and attention); I describe the topics I cover in more detail below.

Course Description

How do people actually make decisions when faced with limited attention, costly search, or cognitive costs? Traditional economic models often assume that individuals weigh all options effortlessly and act optimally. But a growing body of theoretical experimental research reveals a more nuanced picture, whereby the process by which people gather and use information – on prices, the stock market, inflation, competitors, politicians, etc. – plays a central role in shaping their choices.

This year we will cover four main themes: how people acquire and use information; how we can obtain and what we can learn from process data (like response times or attention markers); what it means when choices appear inattentive or noisy. Across all topics, we will overview available methodologies and existing experimental evidence as well as discuss existing open questions. This line of research bridges several fields within economics and beyond (cognitive sciences and computer science) and draws on methods from decision theory, behavioural economics, and information theory. While rooted in experimental and behavioural economics, these topics are also relevant to other fields. It can help shed light on infrequent portfolio rebalancing and investor inattention in finance, inform macroeconomic models of expectation formation and perceived policy credibility, and it enables better testing of discrete choice models in industrial organisation. Further, it has deep implications for policy and for theory alike: if people fail to learn optimally or ignore relevant information, how should we model their behaviour and how should we design institutions in response?

The goal of this course is not just to teach tools, but to spark questions and inspire research. It is aimed at PhD students interested in these topics and methods.

Meeting Times and Location
Tuesday, 13:30-15:30 (TBC)
Meeting Dates: 13 January – 10 March 2026.

Topics

1. Experiments on information acquisition.
   Lecture(s): 13 January.
   References: TBD.
2. Time and other choice process data in experiments.
   Lecture(s): 20, 27 January.
   References: TBD.
3. Stochastic choice and indifference, limited attention and status quo bias.
   Lecture(s): 27 January, 3 February.
   References: TBD.
4. Behavioural markers of complexity.
   Lecture(s): 10 February.
   References: TBD.
5. Applications.
   Lecture(s): 24 February, 3 and 10 March.
   Presentations by students.

Materials will be posted below as the course progresses.

Presentations
The presentations are to be structured as follows: 30min discussion of the paper (motivation, hypotheses, identification strategy, results); 10min project idea; 5min discussion. Please send me an email with your top 3 papers you would like to present by 15 February.