Welcome to the Experimental Economics Lab at WT! On this website, you'll find information about experimental economics research by faculty, student research projects, classroom experiments and opportunities to participate.

What is experimental economics?

In experimental economics, we collect data in laboratory (or field) experiments to test economic theories. For example, game theory predicts that players will reach a self-enforcing agreement, where no player wants to change their strategy choice (Nash equilibrium). The theory does not tell us however, how players know to choose this equilibrium strategy and whether it is a good prediction of what individuals will actually do. Using laboratory experiments, we can collect data on players' strategy choices and test whether or not the behavior that we observe is consistent with the theory.

Experiments in the classroom

Over the last couple of years, experiments have also become an increasingly popular teaching tool. At WT, we use classroom experiments in a variety of classes to bring economic theory to life. We use the oTree software by Chen et al. (2016) to program our games. Students can access the games through a web link on their phones, tablets or laptops. Below, you can take a look at how experimental results from our students compare to the theoretical predictions.

Quantity competition

In microeconomics and industrial organization courses, students learn about the Cournot model, where a limited number of firms compete in a market by choosing what quantity to produce. Oftentimes, students wonder how firms know what the optimal quantity predicted by the model is. After playing the computerized game, they see that after a few rounds of play the average quantity produced converges to the Nash equilibrium quantity predicted by the model. Hence it appears that if individuals have the opportunity to repeatedly interact in the same setting, they adapt to their competitors' actions and learn to play the predicted optimal strategy. The graph below shows a summary of experiments from a variety of classes between Fall 2016 and Fall 2018.

Graph of experimental results varying slightly around theoretical prediction




















Beauty Contest Games

In this game, we asked the students to pick a number between 0 and 100. The winner of the game is the person whose guess is closest to 2/3 of the average of all guesses plus 10. What number should one choose?

Let's think about this. If everyone picked 100, then 2/3 of the average of all guesses plus 10 equals 2/3*100+10 = 76.67. Hence guessing any number higher than 76.67 can never be a winning strategy. After eliminating all of these numbers from consideration, suppose everybody now chose 76.37. Then the winning number is 2/3*76.67+10 = 61.11. Hence any number above 61.11 cannot be a winning choice. If we continue with this reasoning (which we call iterated deletion of dominated strategies), we find that in a Nash equilibrium, every player chooses 30.

We asked students in an undergraduate course during the fall semester of 2017 to play this game. As expected, guesses initially were far from the predicted Nash equilibrium of 30, but as students started to learn and understand the game, their average choices started converging towards 30 (see graph below).

Graph where average guess of players converges to the Nash equilibrium prediction


















Other Games

Other games we have implemented in classes at WT include

  • Bank run games:
    Why do bank runs occur and what can we do to prevent them? In our bank run games, students play the role of investors who decide whether to leave their money in the bank or to withdraw. The games allow them to experience that bank runs occur if a large enough fraction of the investors become concerned with the bank's liquidity and withdraw their money.
  • Ultimatum bargaining/ dictator games::
    Do we always act purely out of self interest, as the idea of homo economicus suggests or do we care about fairness? In this game, one player proposes how to split $10 between herself and her partner. The partner can then accept or reject the offer. If we were purely interested in maximizing our own payoffs, the proposer should send no more than the minimum amount possible (for example $0.50) to their partner, and the partner should accept. However, when students play this game, we find that proposers on average propose to split the money 60-40. This indicates that they don't purely act out of self-interest and that fairness or fear of negative repercussions play a role in the decision making process. Likewise, the responding players will on average not accept offers that they deem "unfair".
  • Bertrand price competition:
    In this game, students play the role of firms that are engaged in price competition and they observe first-hand how firms undercut each other's prices and drive market price down towards their marginal cost of production.
  • Anchoring experiments:
    Anchoring is a cognitive bias studied in behavioral economics, where individual's decision making is affected by an initial arbitrary piece of information. For example, in one of our anchoring games students are asked to enter the last two digits of their social security or phone number and then provide price estimates for a couple of items where they likely don't know the true market price. Interestingly, students whose social security numbers or phone numbers end in 00 to 49 on average guess significantly lower amounts than those whose social ends in 50 through 99! This experiment shows how even numbers that have no relationship with the question at hand can serve as arbitrary anchors in our decision making process.

Classroom Experiments for Online Courses

Since classroom experiments rely on the interaction of students in the game, online course pose a unique challenge due to their asynchronous nature. We are currently working with an undergraduate CIS major on a project that will improve the functionality of the experiments in online courses and we are planning to pilot the first game during the fall of 2019.

Undergraduate Research Projects

  • Travis Whitacre (economics): "The Effect of Travel Advisories on Travel Decisions: An Experimental Study"
  • Walker Chesley (CIS): "Classroom Experiments for Online Teaching"



Chen, D. L., Schonger, M., & Wickens, C. (2016). oTree—An open-source platform for laboratory, online, and field experiments. Journal of Behavioral and Experimental Finance, 9, 88-97.
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