Economics Games

Classroom Games For Teaching Economics

[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.

Summary:
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.

 

facebooktwittergoogle_plusredditpinterestlinkedintumblrmailby feather

Economics Games : Inter-University Student Tournament 2018!

We are launching a new (free) Inter-University Student Tournament, based on an Industrial Organization simulation (close to those that are described here: https://lud.io).

Last year, HEC Montréal won the tournament, Toulouse School of Economics was second and Cardiff Metropolitan University completed the podium (all finalists and ranking). Will you follow them?

The qualification phase consists in playing a few years of a mono-player simulation, between October the 22nd and November the 22nd, 2018 (vs robots behaving like humans did, in previous experiments) . You can play when you want, at your own pace. The 12 best teams will be qualified for the finals (and scores will be reset).

The finals will be played between November the 26th and December the 1st, 2018: Players will have to enter one decision every day, before 21h CET.

Both phases are played online.

 

iogame

 

The winning team will be awarded a voucher around 300€ as a first prize (on amazon or a similar site).

Students can participate on their own, with no need for support from their instructors: The game will require some strategic thinking but there is no prerequisite in economics.
This should be fun, so join the tournament!

One team (2-4 players) from any university or school is welcome to participate. To register, send us a message (deadline for registration: Monday, November the 19th. In some universities, registrations are directly managed by professors: If you are not sure, contact us and we will tell you).

You can also register to the facebook event, to stay informed:  https://www.facebook.com/events/344492182742496

facebooktwittergoogle_plusredditpinterestlinkedintumblrmailby feather

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.

 

bubble

 

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.”

facebooktwittergoogle_plusredditpinterestlinkedintumblrmailby feather

“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.

 

28959007_1224878244311994_2069151575369954465_n

 

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…”

facebooktwittergoogle_plusredditpinterestlinkedintumblrmailby feather

“Contracting under Incomplete Information and Social Preferences”

We have just added a new experiment: “Contracting under Incomplete Information and Social Preferences: An Experimental Study” by Eva Hoppe and Patrick Schmitz (RES 2013).

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

 

HoppeSchmitzContracting

 

Abstract of the paper:

Principal-agent models in which the agent has access to private information before a contract is signed are a cornerstone of contract theory. We have conducted an experiment with 720 participants to explore whether the theoretical insights are rejected by the behavior of subjects in the laboratory and to what extent deviations from standard theory can be explained by social preferences. Investigating settings with both exogenous and endogenous information structures, we find that agency theory is indeed useful to qualitatively predict how variations in the degree of uncertainty affect subjects’ behavior. Regarding the quantitative deviations from standard predictions, our analysis based on several control treatments and quantal response estimations shows that agents’ behavior can be explained by social preferences that are less pronounced than in conventional ultimatum games. Principalsíown social preferences are not an important determinant of their behavior. However, when the principals make contract offers, they anticipate that social preferences affect agents’ behavior.”

facebooktwittergoogle_plusredditpinterestlinkedintumblrmailby feather

An extension to help running experiments with oTree on mTurk

For those of you who design research experiments with the oTree software (http://www.otree.org/) and want to run them on Amazon mTurk.

During the WZB oTree hackathon, we worked, with Essi Kujansuu and Philipp Chapkovski on developing special pages intended to help doing this, when the experiment involves interactions between the participants. Most of the features deal with the dropout/”participant synchronization” problems:

1 – Participants can be offered to do a specific task while waiting for other players to arrive (to ensure that they remain “available” and ready to start the experiment while they wait).

2 – You can offer participants to finish part of the experiment (or just a part of it) if they have been waiting for too long.

3 – It is possible to pay participants for their tasks or for the time they spent on the wait page.

admin_live

The project, mturk-oTree-utils, runs on oTree 1.4, and you can find it, along with more details (in the readme file), here: https://github.com/chapkovski/custom-waiting-page-for-mturk

facebooktwittergoogle_plusredditpinterestlinkedintumblrmailby feather

A Trading Pit Market Experiment

This week, we are adding a Trading Pit Market Experiment, a la Vernon Smith, including price ceilings and price floors, and unit taxes.

The experiment is based on this guide  (written by Antonio Cabrales for the coreecon manual). An extended version, including several countries and international traders will be introduced in a few weeks.

The Trading Pit Market Experiment

 

First page of the guide of the experiment (A. Cabrales for coreecon):

“This experiment is used to introduce students to the working of competitive markets. As The Economy explains in more detail in unit 8, the experiment was first run in 1948 by Edward Chamberlin, whose results were quite different from what equilibrium theory would predict. Later, at the beginning of the 1960’s, Vernon Smith reran the experiments with two key innovations: firstly the prices of agreed trades were made public, the second was to repeat the game several times, with the participants keeping the same valuation in each round. Both the design of the history and the experiment serve various pedagogical objectives, which we now discuss.

  1. The experiment is actually quite different from the way many students picture “demand and supply” when one teaches it to them. It is important that they understand that models are useful to represent situations that are not obviously connected to the model.
  2. It shows that the theory we explains gives empirically validated conclusions. They are in fact more likely to believe the results are true, if they have behaved in the way the theory predicts.
  3. In conjunction with the history of the experiment, it also demonstrates that, as we point out in unit 5, “the rules of the game matter”. It was only when Vernon Smith changed the way the games was played that the behaviour was in accordance with the theory.
  4. As per the previous point, it also shows that empirical work sometimes needs to be persistent and one needs to try many things until one can be sure what makes a particular “treatment” work, and when it does not.

The game should ideally be played before the theory about markets is introduced, to avoid the risk that knowing the theory might affect how some students play. It can be played in quite large groups (at University College London and Universitat Pompeu Fabra groups of 300-400 students have played it successfully), but it works in groups as small as a dozen people (as in Universidad Carlos III)…”

facebooktwittergoogle_plusredditpinterestlinkedintumblrmailby feather

Inter-University Student Tournament: Ranking

 

… And the Winner of the Economics Tournament is … HEC Montréal !!

Toulouse School of Economics, who was 1st from Year 2 to Year 4, ends the game at the 2nd place.

Cardiff Metropolitan University also stands on the podium with a great 3rd place!

 

hecMontreal2

 

Here is the complete ranking of the Final:

  1. HEC Montréal
  2. Toulouse School of Economics
  3. Cardiff Metropolitan University
  4. CentraleSupélec
  5. Universidad de Málaga
  6. Ecole Centrale de Marseille
  7. Ecole Nationale Polytechnique (ENP) – Algeria
  8. University of Glasgow
  9. IMT Atlantique
  10. Athens University of Economics and Business
  11. Aston University

ENSAE ParisTech was also qualified to the Final.

 

Congratulations to you all, this was a hard competition! Among the 31 teams, from 14 countries, who registered, 24 finished the qualification game and 12 qualified to the Final.

 

facebooktwittergoogle_plusredditpinterestlinkedintumblrmailby feather

Inter-University Student Tournament : Qualification for the Final

The 12 teams to qualify for the finals are (unranked list) :

  • Cardiff Metropolitan University
  • Aston University
  • HEC Montréal
  • CentraleSupélec
  • École nationale de la statistique et de l’administration économique
  • Athens University of Economics and Business – Οικονομικό Παν. Αθηνών
  • University of Glasgow
  • Universidad de Málaga
  • Ecole Nationale Polytechnique d’Alger
  • Toulouse School of Economics
  • IMT Atlantique
  • Ecole Centrale de Marseille

Congratulations!

tournResults

It was a pretty hard competition between the 31 teams that registered. Qualifying required a score above 1 313 343 € .

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 finals that will take place next week. Qualified Teams will receive instructions in the next hours by regular email.

facebooktwittergoogle_plusredditpinterestlinkedintumblrmailby feather