The use of behavioural economics insights and economic experiments has become increasingly recognised within economics, by other social and behavioural sciences, and, recently, by policy-makers and the public at large both in the UK and all over the world.
Behavioural and experimental economists in the UK can of course join the Economic Science Association (ESA), the Royal Economics Society (RES), the European Economic Association (EEA), as well as many other professional associations, and participate in their events and activities.
Until July 2024, however, there has been no specific network of behavioural and experimental economists based in the UK.
Initiatives such as the ESRC-funded Network for Integrated Behavioural Science (NIBS) by the Universities of Nottingham, East Anglia and Warwick were launched in recent years (2012-2022) to promote conversations (e.g., the popular annual NIBS conference) between behavioural and experimental economists, psychologists, and other behavioural scientists.
However, the NIBS’ funded period has unfortunately come to an end in 2022. In the meanwhile, other regional networks and initiatives have been launched, such as the Behavioural and Experimental Northeast Cluster (BENC) between Durham University and Newcastle University, or the London Behavioural and Experimental Group (LBEG) and the London Experimental Week (LEW) among various universities in London (LSE, King's College, UCL, Middlesex, Birkbeck, City, Queen Mary, Royal Holloway, etc), but always at a local rather than at a UK-wide level. Other, interdisciplinary, ESRC and UKRI initiatives are currently trying to partially fill this gap by fostering the creation of a national hub for behavioural research, for example.
At the same time, the community of behavioural and experimental economists has significantly grown, becoming increasingly international and diverse with the opening of several new labs, centres, and groups at Birmingham, Durham, Essex, Exeter, Glasgow, King’s College London, Lancaster, Leeds, Leicester, LSE, Newcastle, among other places.
Thus, we believe there is a potential diffuse interest for linking up all these new colleagues, labs, and initiatives into a broader UK network, and for this reason we have launched the BEE UK network.
How did you end up in behavioural or experimental economics?
I wasn’t fully aware of this field when I began graduate school. My initial interests were in
environmental economics and questions about cooperation, externalities, and the role of
markets. Then I took a course in Experimental Economics and was immediately drawn in.
Traditional models often felt too limited to capture the richness of human behaviour—
especially in areas like public goods, norms, and environmental or social dilemmas.
Experimental methods offered a clean, causal way to uncover the mechanisms driving these
behaviours. Once I began running experiments, I was hooked by the combination of
methodological rigor and the creativity the field demands.
What question or problem currently motivates your research the most, and why?
My research currently centres on two main themes. The first is inspired by social and
environmental dilemmas, where I examine mechanisms that promote cooperation within and
across groups and the roles played by guilt, normative principles, and redistribution.
The second theme focuses on norms, institutions, and the behavioural drivers of inequality,
particularly gender inequality. Many economic outcomes are shaped not only by incentives but
also by social expectations, confidence, and evaluation practices. My work investigates how
these behavioural and institutional factors interact, and how we can design environments—
whether in workplaces, communities, or markets—where individuals can thrive regardless of
their identity or background.
What do you see as the most important developments or challenges for behavioural and
experimental economics over the next 5–10 years?
A key development in the field is large-scale experiments which now allows researchers to
study behaviour in settings with higher external validity. A related emerging challenge is the
influence of AI on data quality, particularly as many researchers rely on online platforms for
participant recruitment and experimental execution. Understanding and addressing how
AI-generated responses or AI-mediated behaviour may distort data will be increasingly
important for the discipline. In this context, laboratory experiments under controlled conditions
can play a crucial benchmarking role, helping researchers compare behaviour observed online
with behaviour in more tightly controlled environments.
How has your research evolved over time?
My research began with a focus on environmental markets, social preferences, cooperation,
and competition, often using laboratory experiments. Over time, I expanded this work to
examine topics related to corruption, gender, leadership, performance evaluation, and
institutional design. I now conduct laboratory, online, and field experiments and also
collaborate with scholars across disciplines. My research, in general, aims to generate
behavioural insights, with a particular focus on how norms and institutions can be strengthened
to promote fairness and efficiency.
What are the biggest challenges you’ve faced in your research?
An important challenge has been to design experiments that capture the complexity of
real-world decision-making while maintaining strong internal validity. We are also
increasingly expected to translate our findings into meaningful policy recommendations—by
journals, grant agencies, and policymakers. This can take time, as policy-relevant insights often
emerge only after multiple related projects accumulate enough evidence to clarify what is both
effective and scalable.
What advice would you give to PhD students or early-career researchers entering the
field today?
Choose research questions that matter to you—curiosity and a sense of purpose will sustain
you through setbacks. Build strong methodological foundations in experimental design, theory,
and empirical methods, and don’t hesitate to draw on insights from other disciplines when they
strengthen your research. Learn how to design clean identification strategies.
Build collaborations, both vertical (with more senior scholars) and horizontal (with your peers).
Apply for grants periodically; even unsuccessful applications can be valuable because they
force you to clarify your ideas, articulate why your work matters, and refine your research
agenda. And finally, patience and humility will serve you well throughout an academic career.
If you could redirect £10m of research funding tomorrow, what area would you direct it
to?
What a luxury that would be! I would invest in a large-scale research program examining the
behavioural drivers of inequality—particularly how identity and social norms operate within
rapidly evolving institutional and technological environments. A substantial portion would
support building experimental infrastructure across countries and sectors, enabling researchers
to track how preferences, beliefs, norms, and behaviours evolve over time. Such longitudinal,
cross-context data would be useful for designing policies that meaningfully improve
cooperation, reduce inequality, and strengthen institutions. The program would also actively
support and involve many junior scholars, helping develop the next generation of researchers
in the field.
I was first exposed to experiments in a game theory course delivered by Werner Gueth, who had moved to Humboldt University where I was an undergraduate student at the time. I wrote my undergraduate thesis on theoretical asymmetric auctions though and moved to the Universitat Pompeu Fabra with the thought to specialise in contract design and IO. While there I met Rosemarie Nagel and took her experimental econ class which got me finally hooked.
I think there are mainly two areas now. One relates to emotions and how they affect our decision making. The other is behaviour towards those that a decision maker perceives to be different from themselves (e.g. differences based on gender, age, ethnicity, educational background etc).
Let’s start with the challenges. I think one of the biggest one is to teach our next generation of experimentalists (and those that might just sometimes complement their research with experiments) to properly conduct experiments. It has become relatively cheap to run online experiments. I have seen quite a few cases where researchers think they can quickly churn out some data and only later think about contrasting alternatives. I am not against online experiments (and have conducted them myself). They have provided a lifeline during Covid and are a great tool especially if one wants to reach a certain demographic or get a representative sample. However, great care should be taken in ensuring subjects pay attention and responses truly come from well incentivised human decision makers. The other challenge is funding. Researchers at well-endowed universities with access to larger research funds can conduct studies with dozens of treatments and many more subjects than others who are limited in their funds. The funding landscape in the UK has become increasingly competitive and limiting.
As for the important developments, well, AI is certainly something to watch, from the use of synthetic cultural agents to how our minds change with access to AI and what it means for our decision making. We also need to move beyond blaming individuals for systematic failures/nudging them to make “better” decisions to building more inclusive societies with an active role for policy and governments basically redesigning institutions so that mistakes don’t compound.
I started out with only lab experiments and have over time ventured into both online as well as field experiments. I am in the process of embarking on my first RCT.
Funding always seems to be a limiting factor. Early on I was advised not to waste my time on questions like happiness and emotions. Once I got tenure, I felt I could wander a bit more from the mainstream. I once also was told not to bring my still nursing daughter into a talk as a male presenter found the “suckling” noise disturbing. Now that she is older, balancing travel as a single mum remains an issue. Luckily, most institutions/conferences have been supportive of me bringing my daughter along, but her school doesn’t appreciate my research travels with her as much.
Be curious, never stop asking questions and read older papers yourselves instead of just asking AI to summarize them. Pay attention to detail and recognise the beauty of simple designs.
I wish! 😊
Personally, I would support culturally diverse investigations that are not just focused on one country or subject pool. Gender norms across the continents and discrimination against those that are not necessarily mainstream need to be investigated more and more consistently, ideally with interdisciplinary teams of researchers.
From a discipline perspective, I would suggest funding a diverse portfolio from foundations, aggregations to institutions and measurement. For example, I think we need more mechanism and market design robust to bounded rationality. I would also fund more long-horizon field experiments and high-stakes decisions (finance, labour, health, energy etc.) and support replication + scaling, not just novelty. I also think as a discipline we need to get macro economists a bit more on board of the behavioural bandwagon. Lastly, I would also invest in measurement and methods. We need better elicitation of beliefs and preferences and more combining experiments with administrative data.
For my history and more details see
My Experimental Meanderings, Theory and Decision, 77, 291-296, 2014, doi:10.1007/s11238-014-9464-x.
and
Addendum to "My Experimental Meanderings", Theory and Decision, 79, 273, 2015.
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I was doing pure economic theory - which was intellectually satisfying, but increasingly unrealistic as a description of reality. I wanted to see what people actually do, rather than what they should do if acting optimally.
Currently, I am investigating behaviour under ambiguity, both static and dynamic. While there are many theories of optimal behaviour under ambiguity, there is remarkably little on actual behaviour. I aim to fill this gap.
It needs to discover actual behaviour (both static and dynamic) in conditions of ambiguity. This will be difficult, and involve the investigation of ad hoc rules or rules of thumb. We need to steer experimental economics away from investigating the validity of theories of optimal behaviour. This latter assumes a level of sophistication of decision-making which seems to far exceeds human ability.
See my article cited above – which traces the history of (some part of) experimental economics over the period since its birth.
Convincing funding bodies that experimental work is worth the funds, both for payments to subjects, and for experimental infrastructure (machines, software and research assistance).At York, they want us to do experiments with Prolific – which I resist with all my strength.
Choose a good supervisor, one who is enthusiastic and knowledgeable about experimental economics, and listen to them. Choose your topic area carefully as this will probably dominate your academic life. Avoid game theory.
I would avoid investigating game theory as too much work has been done in this field. I would prefer the investigation of behaviour under ambiguity and the evolution of social norms.
How did you end up in behavioural or experimental economics?
I’ve interpreted this as “How did I get into it in the first place?” I’ll start with that and leave how I ended up for later questions.
There were two initial stimuli.
The first was the work that my then colleague at the University of Newcastle, Mike Jones-Lee, was doing for the Department of Transport. He and other colleagues were undertaking a large survey of members of the British public to try to elicit the monetary values they placed on changes in the risks of being killed in road accidents. Their aim was to provide the DoT with a ‘Value of Statistical Life’ (VSL) for use in road safety cost-benefit analyses. Mike presented some of the main features and findings of the study in internal departmental seminars.
One issue that arose was whether individuals’ evaluations of particular risks to themselves were independent of the distribution of risks to others. With the assistance of a couple of dozen undergraduate students who volunteered to interview a convenience sample of members of the Newcastle public, I conducted a survey to explore this issue. It was, perhaps, a somewhat unsophisticated attempt, but it provided some interesting results and was published and led to Mike inviting me to collaborate more closely with him – a collaboration during the decades that followed which involved numerous co-authors working on a variety of projects concerned with valuing non-market goods and bads. More about that below.
The second stimulus came from Daniel Kahneman’s and Amos Tversky’s Prospect Theory. My first reaction to the theory itself was to be quite sceptical: their model struck me as like a Heath Robinson contraption, with several components strung together and extra assumptions bolted on to get around inconvenient implications. On the other hand, their experimental results were intriguing: as I worked through their various decision scenarios, I found myself often inclined towards the patterns of response which ‘violated’ rational (Expected Utility) theory. Since I regarded myself as rational, I supposed that there must be some alternative rational explanation for these patterns of choice. I had some hunches about such an alternative. And crucially, I had the good fortune to have Bob Sugden as a Newcastle colleague: together we developed (our forms of) Regret Theory and Disappointment Theory to account for a number of the K&T ‘irregularities’ and some more ‘phenomena’ besides, such as certain breaches of monotonicity and transitivity.
However, the bulk of the experimental risky choice and valuation data existing at that time came from studies that were not designed with the structures of regret and disappointment in mind. In order to test various novel implications of our models, the obvious thing to do was to design fresh experiments that examined those implications more directly. So that’s how I got into that branch of experimental economics, with our first paper, exploring what we supposed were regret and disappointment effects, being published in 1987.
By that time, Bob had moved to the University of East Anglia (UEA) where he established a group using experimental methods and I had moved to the University of York where I assisted John Hey in founding EXEC, the Centre for Experimental Economics. During the following 5 or 6 years, Bob and I (sometimes with other co-authors) published a number of papers reporting experiments intended to examine various implications of regret theory for choices between laboratory lotteries. All seemed to go well, in the sense that the implications of regret theory appeared to be borne out by the results of those experiments.
However, a non-experimental economist at UEA (Steve Davies), after attending a presentation of some of this work, wondered whether the same results could be explained as a kind of display effect whereby more weight was given to payoffs that were displayed more often as a result of subdividing states of the world (termed ‘event-splitting effects’). Again, the obvious thing to do was to design an experiment to test that conjecture – which is what Bob Sugden and Chris Starmer did. On that basis, they concluded that the bulk of many lab experiment effects previously attributed to regret were, in fact, due to event-splitting. I was convinced by those results and by related results found by Steve Humphrey and did not undertake any further experiments testing for supposed regret effects in laboratory choices between lotteries.
That is not to say that I rejected the potential importance of anticipated regret in human decision making. There are many fields of real world activity – e.g. health behaviour, career choices, financial decisions, large consumer purchases – where regret may be an influential consideration. But experiments involving large numbers of binary choices between low-payoff lotteries that are each made in just a few seconds appear to be light on regret and heavier on display effects.
How has your research evolved over time?
The two branches of my research have evolved in somewhat different ways. I'll start with the survey research. For quite a long time – too long – I held on to the belief that when it came to important things like the health and safety of oneself and significant others, people would be able to express preferences and values – not necessarily very precisely, but within a reasonable ballpark – and that we could access them to a reasonable extent if we refined the survey techniques sufficiently.
So, much of the evolution of that branch of my research entailed developing different designs, building in consistency checks to see if there was internal consistency; and if (when) there wasn't, to give us ideas about how to modify subsequent designs and try again.
However, after three decades of this kind of work, I came to the conclusion that most people don't have highly-articulated preferences or values for health and safety which are amenable to elicitation by the kinds of surveys that we are accustomed to conduct. Using the terminology that Daniel Kahneman adopted and popularised, standard surveys are vulnerable to all sorts of ‘System 1’ effects and will not provide a sound basis for public health/safety/environmental policy. Whether methods involving more extensive and intensive ‘System 2’ deliberation will achieve such an objective remains to be seen.
Now for the other branch – lab experiments. One of the things I became increasingly aware of was the ‘noise’ in most participants’ choices and valuations: that is, if the same option or pair of options was presented more than once in the course of an experiment (generally scattered about among dozens of other questions/tasks) it was not uncommon for a participant to respond differently on different instances. (A parallel phenomenon was observable in the health and safety surveys, where many people expressed considerable uncertainty about their health and safety responses, often giving quite wide ranges around their ‘best’ estimates.)
So the question was – and I think still is – how to account for and allow for the noise/variability in people’s decisions. Simply plugging in some conveniently specified off-the-shelf ‘error’ term seems inappropriate and potentially misleading. Patterns of response times appear compatible with the idea that the variability reflects some mental process at work in the generation/construction of responses. However, as far as I’m aware, there is as yet no strong consensus about how best to model that process and apply that model to the interpretation of the data. Variants within the broad class of ‘accumulator’ / ‘drift diffusion’ / ‘sequential sampling’ models seem to have the kind of general characteristics required, but it is not obvious (to me) that there is one specification that leads the field.
What are the biggest challenges you’ve faced in your research?
It may sound glib and evasive to respond with something like “The biggest challenges are the next ones”; but that is how I feel.
I have been fortunate in having had enough funding and many talented and productive collaborators throughout my career. So the only real challenges have been trying to figure out what to do next when ideas I’ve thought had great promise turn out to be less promising – or indeed, well off the mark – when put to the test.
What question or problem currently motivates your research the most, and why?
I’ll combine that with the later question:
If you could redirect £10m of research funding tomorrow, what area would you direct them to?
I indicated in my responses to Q2 that we don’t yet have adequate models of individual decision processes, nor do we have reliable answers to the question of how government departments/agencies should value the benefits/harms of public policies in areas such as health, safety and the environment. £10m won’t really go far towards filling those gaps, but they are the issues to which I should like to see more resources – including my own – allocated.
What do you see as the most important developments or challenges for behavioural and experimental economics over the next 5–10 years?
A continuing challenge is to establish the relationship between a ‘lab’ experiment (or survey) and whatever behaviour in the ‘natural’ environment the experiment is supposed to be shedding light upon. Ideally, the emphasis should be on field experiments where participants are unaware that they are in a particular treatment group as part of a larger study and that their ‘behaviour of interest’ is just one of the many things they are doing in the course of their normal life activities. Of course, that is a counsel of perfection: in many areas of interest, a ‘proper’ field trial may be too costly and time-consuming and may be blocked by vested interests. Still, to the extent that lab experiments or surveys appear to be the best that can be done under the prevailing circumstances, researchers need to address the limits to external validity if and when any claims are being made about the implications for the world outside of the lab.
Another challenge is as follows. My heart sinks a little when authors claim to be ‘controlling for’ certain ‘characteristics’ of their participants – such as their ‘risk attitude’ or ‘personal time discount rate’ or ‘degree of sociality’ – when what they have done is included a couple of risk tables or time trade-off questions or a dictator/ultimatum game and extrapolated from these. It is as if the authors think that an individual has some stable, all-purpose index of each of these things that can be measured by a simple instrument and then used in the analysis of their data from an experiment that may have a quite different structure and/or parameters. But the facts are that different instruments may elicit substantially different indices from the same individual and different rankings across samples. If part of the appeal of conducting experiments is to apply scientifically sound methods to generate robust results, then ignoring past experimental evidence about the unreliability of these so-called controls seems . . . well, not very good science.
What advice would you give to PhD students or early-career researchers entering the field today?
In a way, this is the most difficult question of the 7 because much of the answer depends on the person’s motivations and skills, the topics/fields they are interested in, the quality of the research environment they inhabit and the support they receive from colleagues, and so on. I don’t think I have any generic advice to offer over and above the things that can be inferred from my earlier answers.