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EEML 2013 : International Workshop on Experimental Economics and Machine Learning


When Dec 8, 2013 - Dec 8, 2013
Where Dallas
Submission Deadline Aug 17, 2013
Notification Due Sep 24, 2013
Final Version Due Oct 15, 2013
Categories    interdisciplinary data science   experimental economics   machine learning   game theory

Call For Papers

As a part of renowned international conferences on different branches of machine learning, this full-day workshop intends to integrate scientists from experimental economics with those from AI. First workshop – EEML 2012 – has been successfully accomplished at ICFCA 2012 and we look forward to encourage more and more researchers' interactions from both fields. The anticipated target groups of attendees are the Experimental Economics researchers seeking for more efficiency in their research
and the Data Mining researchers seeking for real world data beyond academic toy problems.

In Experimental Economics, laboratory and field experiments are conducted on subjects in order to improve theoretical knowledge about human behavior in interactions. Although paying different amounts of money restricts the preferences of the subjects in experiments, the exclusive application of analytical game theory does not suffice to explain the recorded data. It exacts the development and evaluation of more sophisticated models. The research area additionally includes experiments, where human subjects are involved into an interaction with automated agents. Nowadays experiments are conducted using state-of-art software like z-Tree, which produces huge text data sets.

The more data is used for the evaluation, the more of statistical significance can be achieved. Since huge amounts of behavioral data are required to be scanned for regularities and automated agents are required to simulate and to intervene human interactions, Machine Learning is the tool of choice for the research in Experimental Economics. We hope that this workshop associated with the conference and other related workshops will help to gather researchers from data analysis and economic communities in order to gain the beneficial results.

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