Releasing the Automated Game Play Datasets
This blog post is cross-posted from the Open Economics Blog.
We are very happy to announce that the Open Economics Working Group is releasing the datasets of the research project “Small Artificial Human Agents for Virtual Economies“, implemented by Professor David Levine and Professor Yixin Chen at the Washington University of St. Louis and funded by the National Science Foundation [See dedicated webpage].
The authors of the study have given their permission to publish their data online. We hope that through making this data available online we will aid researchers working in this field. This initiative is motivated by our belief that in order for economic research to be reliable and trusted, it should be possible to reproduce research findings – which is difficult or even impossible without the availability of the data and code. Making material openly available reduces to a minimum the barriers for doing reproducible research.
If you are interested to know more or you like to get help in releasing research data in your field, please contact us at: economics [at] okfn.org
An important requirement for developing better economic policy recommendations is improving the way we validate theories. Originally economics depended on field data from surveys and laboratory experiments. An alternative method of validating theories is through the use of artificial or virtual economies. If a virtual world is an adequate description of a real economy, then a good economic theory ought to be able to predict outcomes in that setting.
An artificial environment offers enormous advantages over the field and laboratory: complete control – for example, over risk aversion and social preferences – and great speed in creating economies and validating theories. In economics, the use of virtual economies can potentially enable us to deal with heterogeneity, with small frictions, and with expectations that are backward looking rather than determined in equilibrium. These are difficult or impractical to combine in existing calibrations or Monte Carlo simulations.
The goal of this project is to build artificial agents by developing computer programs that act like human beings in the laboratory. We focus on the simplest type of problem of interest to economists: the simple one-shot two-player simultaneous move games. The most well-known form of these are “Prisoner’s Dilemmas” – a much studied scenario in game theory which explores the circumstances of cooperation between two people. In its classic form, the model suggests that two agents who are fully self-interested and rational would always betray each other, even though the best outcome overall would be if they cooperated. However, laboratory humans show a tendency towards cooperation. Our challenge is therefore developing artificial agents who share this bias to the same degree as their human counterparts.
There is a wide variety of existing published data on laboratory behavior that will be the primary testing ground for the computer programs. As the project progresses, the programs will be challenged to see if they adapt themselves to changes in the rules in the same ways as human agents: for example, if payments are changed in a certain way, the computer programs will play differently: do people do the same? In some cases we may be able to answer these questions with data from existing studies; in others we will need to conduct our own experimental studies.
Find the full list of available datasets here