It is not only the global financial crisis of the recent years [G. P. Maxton, The end of progress : how modern economics has failed us. John Wiley & Sons, 2011.], which made economists reconsider the path economics as a discipline should take. Since decades, it became obvious that classical theories fail in real world [D. Ariely, Predictably Irrational. Harper, 2009.]. Paul Krugman described the current situation in economics as: "... the central cause of the profession’s failure was the desire for an all-encompassing, intellectually elegant approach that also gave economists a chance to show off their mathematical prowess. Unfortunately, this romanticized and sanitized vision of the economics led most economists to ignore all the things that can go wrong. They turned a blind eye to the limitations of human rationality". Experimental Economics gained its importance as a promising solution of this problem. Human being a subject of research has shifted economists' point of view closer to the psychologists' one: people are no more considered to be rational payoff maximizers. On the other side, growing size and complexity of the data makes the application of state-of-the-art data science essential.In Experimental Economics, laboratory and field experiments are carried out using human subjects in order to improve theoretical knowledge about human behavior during interaction. Although financial rewards restrict subjects' preferences in the experiments, the exclusive application of analytical game theory is not enough to explain the collected data. It calls for the development and evaluation of more sophisticated models. Additionally, the research area includes experiments where human subjects are involved into interaction with automated agents.The more data is used for evaluation, the more statistical significance can be achieved. Since large amounts of behavioral data are required to scan for regularities, along with automated agents needed to simulate and intervene in human interactions, Machine Learning is the tool of choice for research in Experimental Economics. This workshop is aimed at bringing together researchers from both Data Analysis and Economics in order to achieve mutually-beneficial results.

As a part of the renowned international conferrence ECML PKDD 2014 focusing on different branches of Machine Learning, this full-day workshop intends to bridge the gap between two scientific communities: Experimental Economics and AI & Data Mining. The first workshop – EEML 2012 – has been successfully accomplished at ICFCA 2012. The second workshop – EEML 2013 – was held alongside the IEEE ICDM 2013. We hope that this year we can continue our work and encourage even more interaction among the researchers from these two fields.

Submissions

The workshop proceedings will be included into a joint publication as CCIS series of Springer, for which workshop chairs will file soon. A publication on CEUR-WS is guarateed anyway.

1) Paper submissions are limited to a maximum of 12 pages, and follow the Springer format requirements
http://www.springer.com/computer/lncs?SGWID=0-164-6-793341-0

2) Submissions can be done over
https://www.easychair.org/conferences/?conf=eeml2014

3) Each submitted paper is to be carefully reviewed by at least two, preferably three, knowledgeable and experienced reviewers.
4) Every accepted paper is invited to be presented at the workshop date.