Image of brain made of currency

Experimental Economics Group

Understanding human decision-making
A multidisciplinary research group using experimental methods to broaden our understanding of behavioural economics and how people make decisions.

About Us

The overarching research goal of the experimental economics group is to understand human behaviour. We use laboratory experiments to test existing and develop new theories of how people make choices. Our work is highly multidisciplinary, combining insights and methods from economic theory, psychology, neuroscience, political science, and marketing.


Our Research Projects

A key assumption in models of dynamic economic decision making is how people form expectations over future events. Many competing theories have been proposed to model the expectation formation process. This project aims to unify and empirically test the most promising theories in the literature to create a unified theory of expectation formation. The findings will inform models of the economy to provide predictions and policy advise that better reflects how people make decisions in practice. Contact:

Although healthier, stronger and better at reasoning than younger children, adolescents’ morbidity and mortality rates double. This project funded by an ARC DECRA to Tymula used experimental economics methods to study how presence of peers affects risk tolerance, discounting, or propensity to make errors. The findings will advance understanding of decision-making across the lifespan, inform theoretical modelling and advise policy how to reduce the risks/costs to adolescents. Contact: 

This project funded by an ARC Discovery grant investigates well-known behavioural “biases” in probability and value perception through the lens of neurobiology. Through its interdisciplinary approach, this project will provide a novel and brain-compatible understanding of how people make decisions. The results will be of interest to researchers from several domains of social science, which focus on how people decide. Contact:

Economists model self-control problems through time-inconsistent preferences. Empirical tests of these preferences largely rely on experimental elicitation methods using monetary rewards, with several recent studies failing to find present bias for money. Collaborating with 4 Chinese high schools, this project investigates whether adolescents show present bias for money and food, and if individual measure of present bias are correlated across reward types. The findings will advance understanding of time preference across different domains. Contact: Xueting Wang

We tend to consider advice from others when making key decisions which affect will our future labour market experiences. Examples of such decisions are which subjects to study or whether to opt for a more ambitious job or a less ambitious one. Silva-Goncalves and van Veldhuizen study whether advice that is based on subjective judgment may be shaped by gender stereotypes and help to sustain gender gaps in labour market outcomes. Contact:

This project, funded by the ARC, studies the use of strategy-proof matching algorithms to solve allocation problems in designed markets (school or house allocation problems. Strategy-proofness means that participants never gain from not revealing their preference order. This property has been seemingly validated by experimental research, but new evidence suggests that participants could be prone to follow misleading advice and thus try to strategise. We further tested the strategy-proofness property to find how advice can actually affect behaviour in theoretically strategy-proof algorithms and find out whether learning can counteract or complement the effect of advice. Contact:

Our People


Alex Berger, Jacob Patrick Dooley, Bradley Jackson, Lucas Phillips, Robert Rutledge


Mahdi Akbari, Zhenlin Kang, Archer Kirk, Allan Yip


Alexander Cornish, Mara Hammerle, Ilya Klauzner, Thomas Rudgley, Alexander Svenson, Chi Kit Wong 


Hee Jin Choi, Julie Guo, Muziqian Mu Guo, Tim Robinson (University Medalist), Adam Teperski, Ting Jun Xu


Tianyang Han, Vindesh Nadan, Guanghan Zhang


Xueting Wang


Jackson Whitehair