2018 Inter-University Student Tournament: Ranking

… And the Winner of the 2018 Economics Tournament is … Eötvös Loránd University!!


Amherst College ends the game in 2nd position. HEC Montréal also stands on the podium, in 3rd position.




Here is the complete ranking of the Final:

  1. Eötvös Loránd University
  2. Amherst College
  3. HEC Montréal
  4. Toulouse School Of Economics
  5. CentraleSupélec
  6. The Australian National University
  7. Universidade de Coimbra
  8. Universiteti i Tiranës
  9. University of Michigan
  10. Augustana University
  11. University of Chicago
  12. National Economics University

Among the 87 teams, from 32 countries, who registered, only 12 qualified to the Final.

Congratulations to you all, this was a tough competition!


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2018 Inter-University Student Tournament: Qualification for the Final

The 12 teams to qualify for the Finals of the Economics Tournament are:

  • Amherst College
  • Augustana University
  • Australian National University
  • CentraleSupélec
  • Eötvös Loránd University
  • HEC Montréal
  • National Economics University
  • Toulouse School of Economics
  • University of Chicago
  • University of Coimbra
  • University of Michigan
  • University of Tirana
    (unranked list)



It was a pretty hard competition between the 87 teams that registered. Qualifying required a score above 2.165.163

We would like to thank the teams who did not qualify, for taking part in our tournament and we hope that you found it fun!

Scores are now reset, for the Final that will take place next week. The finalists will receive instructions by regular email.

Good luck to all!

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Duopoly with Differentiated Demand and Capacity Constraints (B. Çelen & S. Feldmann)

We are adding a new competition game, by Bogaçhan Çelen and Sven Feldmann (Melbourne Business School).

Players are randomly and anonymously paired with another participant, and play 6 rounds of an airline duopoly game with capacity constraints and differentiated demand.

The airlines simultaneously choose capacity and prices with which they compete. The system of demand is linear and symmetric (model of demand).


The rules can be found here and and we also included a simulation “outside” of the game, to help players determine their best choice: simulation

The game is the last game in the list in the industrial organization section.

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Monopoly version of the Price/Production game with Quotas, Taxes…

If you are an instructor running the market game with quotas, taxes… (rules), you may find it easier to start by inviting your students to play the simulation as a monopoly, before introducing competition.




This is now possible with one click in the mono-player simulation section (“Play as a monopoly” button, third simulation, https://simu.io).

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The Carbon Game is in open access until the end of August

The complete version of the game about CO2 emissions and environmental policies (on which the final of the 2017 tournament was partly based) is in open access on the site, until the end of August.

To create the game, click on the button in the blue well on the home page of the commercial site: https://lud.io/

The companion document can be downloaded here: https://lud.io/resources/site/manual/carbon-game-manual.pdf


During the final, out of the 12 teams to qualify, the winner, HEC Montréal,  scored 2 296 587. The second team (Toulouse School of Economics) scored 2 287 370 and the 3rd (Cardiff Metropolitan University) 2 105 320. Can you do better?

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[Video] Model of Demand for the Airline Game

Complements for instructors about the model of demand of the Airline Economics Simulation

You must be familiar with the simulation before watching this video: https://lud.io/transport or https://economics-games.com/industrial-organization.

1 – General Considerations and Customers’ Characteristics: 1:07
2 – Dynamics of sales inside a phase: 4:11
3 – Comparing customers’ characteristics between phases: 7:42
4 – Why the effect of frequencies on sales exhibit decreasing returns: 11:19
5 – When can a cheaper firm sell less than its competitor? Example 1 (because of different frequencies or comfort): 14:22
6 – When can a cheaper firm sell less than its competitor? Example 2 (because of seat quotas): 18:04
7 – When can a cheaper firm sell less than its competitor? Example 3 (because of the random component of individual choices): 22:54

The video is maybe not polished enough to show to your students, but should be quite useful to the instructors who run the Airline Game in their courses.


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The Bubble Game, An Extension


Jieying Hong, Sophie Moinas, and Sébastien Pouget have written a very interesting experimental and theoretical extension to the Bubble Game, studying learning in speculative bubbles. A great extension to direct your students to, after running the bubble game on our site.




Abstract of the paper:

“Does traders’ experience reduce their propensity to participate in speculate bubbles? This paper studies this issue from a theoretical and an experimental viewpoint. We focus on a game in which bubbles, if they arise, are irrational, as in the Smith, Suchanek, and Williams (1988)’s set up. Our theoretical results are based on Camerer and Ho (1999)’s Experience-Weighted Attraction learning model. Adaptive traders are assumed to adjust their behavior according to actions’ past performance. In the long run, learning induces the market to converge to the unique no bubble equilibrium. However, learning initially increases traders’ propensity to speculate. In the short run, more experienced traders thus create more bubbles. An experiment shows that bubbles are very pervasive despite the fact that subjects have become experienced. Our estimation of the EWA model also indicates that learning is at work.”

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“A Classroom Inflation Uncertainty Experiment”

We have just added a new experiment: “A Classroom Inflation Uncertainty Experiment” by Denise Hazlett (IREE 2007).

The paper is available on the site of the journal, and the game is in the “macro section” on our site.




Abstract of the paper:

This classroom experiment uses a double oral auction credit market to demonstrate how inflation uncertainty causes a wealth transfer between borrowers and lenders. The experiment also shows the social cost of inflation uncertainty when borrowers and lenders cannot agree on a nominal interest rate that compensates each for their risk. In this case, the credit market fails to allocate funds to the highest-valued investment projects. The experiment provides hands-on experience with the effects of anticipated and unanticipated inflation, giving students a common background for a discussion of the economic costs of inflation. It can be used in principles, intermediate macroeconomics,money and banking, or financial economics courses, with 8–60 students…”

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