Experimental and Behavioural Economics

The Experimental and Behavioural Economics Research Group (EBERG) draws its membership from economists based at the Economics Department at Warwick who work in the fields of Experimental Economics, Behavioural Economics and/or Subjective Wellbeing (“Happiness Economics”). Experimental methods are used in many fields of economics, including behavioural economics, public economics, labour economics, political economy, game theory, and financial economics. Behavioural economics is an attempt to understand decision-making in the context of the many psychological, cognitive and emotional factors that influence behaviour. Behavioural economists typically build on traditional economic models with insights from psychology or neuroscience. Since behavioural economics concerns the underlying motivations for behaviour it can be hard (though not impossible) to find data to support or develop behavioural theories without the use of experimental methods which explains the close relationship between the two fields.

Experimental and behavioural research are fundamentally interdisciplinary and this is reflected in the fact that the group is linked to other similar groups across the University of Warwick and beyond. DR@W is the overarching interdisciplinary group of all behavioural scientists in Warwick which, together with EBERG, also takes members from the Behavioural Science Group at Warwick Business School and behavioural and experimental psychologists based in the Psychology Department, and hosts a weekly seminar, the DR@W Forum. Many members of EBERG are also affiliated with Bridges, an interdisciplinary centre that includes behavioural and experimental work in its remit that also hosts regular seminars and workshops. Behaviour, Brain and Society is one of the University of Warwick’s global research priorities (GRPs) and the co-ordinator of EBERG sits on the board of the GRP. Several group members are actively involved in the ESRC CAGE centre. Theme 3 of CAGE is led by the co-ordinator of EBERG and has a special focus on subjective wellbeing.

People

Academics

Academics associated with the Reseach Group Name research group are:

Daniel Sgroi

Co-ordinator

Kirill Pogorelskiy

Deputy Co-ordinator

Research Students

Matthias Endres

Elaheh Fatemi Pour

George Ferridge

Massimiliano Furlan

Carole Gao

Andrea Guerrieri D'Amati

Priyama Majumdar

Malavika Mani

Lily Shevchenko

Shubhra Singh

Tiffany Tee Jie Lin

Agustin Troccoli Moretti

Xueying Zhao

Negar Ziaeian Ghasemzadeh

Events

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DR@W Forum - Hybrid Session: Rich Shiffrin (Indiana)

2:30pm - 3:45pm, Thu, 07 Jul '22 Export as iCalendar

Scientists studying decision making often provide a set of choices, each specified with values or distributions of values, and probabilities or distributions of probabilities. E.g. “Would you prefer $100 with probability 1.0 or $1 with probability .9 and $1000 with probability 0.1?” Other decision research examines choices made in the absence of most quantitative information, E.g. “Would you prefer a Ford now or a Porsche a year from now?”, “which food would you prefer”, but models the findings with precise quantitative assumptions. Yet other research does neither, for example modeling verbally stated choices with verbally stated heuristics. This article asks about the relevance of the first two research approaches for much of decision making made in life. The use of quantitative research and modeling is unsurprising, given that this approach underlies most of science. In life, values and probabilities are almost always partly or wholly vague and qualitative rather than quantitative. E.g. When deciding which house to buy there are relevant features such as size, color, neighborhood schools, construction materials, attractiveness, and many more, but the decision maker finds it difficult and of little use to assign these precise values or weights. Nonetheless, humans have evolved to make decisions in such vaguely specified settings. I provide an example showing how a very high degree of uncertainty can defeat the application of quantitative decision making, but such a demonstration is not critical if quantitative research and modeling produces a good understanding of and a good approximation to decision making in the natural environment. This perspective addresses these issues.

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