EEML 2017

Workshop concentrates on an interdisciplinary approach to modeling human behavior incorporating data mining and/or expert knowledge from behavioral sciences. Data analysis results extracted from clean data of laboratory experiments can be compared with noisy industrial data-sets from the web e.g.. Insights from behavioral sciences will help data scientists. Behavior scientists will see new inspirations to research from industrial data science. Market leaders in Big Data, as Microsoft, Facebook, and Google, have already realized the importance of experimental economics know-how for their business.

In Experimental Economics, although financial rewards restrict subjects preferences in experiments, 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. 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.

Proceedings

All accepted papers will be included in the workshop’s proceedings to be published online on the CEUR-Workshop web site in a volume with ISSN, indexed by Scopus and also integrated into RePEc.

Submission Procedure

The submission Web page for EEML 2017 is

https://easychair.org/conferences/?conf=eeml2017.

Electronic version of full paper complete with authors’ affiliations should be submitted through the conference electronic submission system. Manuscripts must be prepared with LaTeX or Microsoft Office and should follow the Springer format available at http://www.springer.de/comp/lncs/authors.html.The maximum number of accepted papers by an individual author that can be covered by the workshop’s registration charge is 3. The papers over 12 pages are not allowed.

Workshop Co-chairs

Rustam Tagiew, Entrepreneur, Ontonovation, Germany Dmitry Ignatov, Associate Professor,National Research University HSE, Russia Andreas Hilbert, Professor for Business Intelligence, TU Dresden, Germany Kai Heinrich, Scientific Assistant, TU Dresden, Germany Radhakrishnan Delhibabu, Associate Professor, Kazan Federal University, Russia

Keywords

Game Theory, Web Mining, Mechanism Design, Behavioral Science, Machine Learning, Business Intelligence, Data Mining, Experimental Economics, Complex Networks, Econometrics, Human Behavior Modeling, Concept Lattices, Behavioral Economics, Data Science, Knowledge Discovery, Text Mining, Social Sciences, Bayes Nets, Markov Nets, Petri Nets, Neural Nets, Decision Trees, Linear Models, Clustering, Ontologies, Real Data, Cognitive Science