Third International Workshop on Experimental Economics and Machine Learning (EEML 2016)
In conjunction with the 13th International Conference on Concept Lattices and Their Applications (CLA 2016)
Workshop concentrates on an interdisciplinary approach to modelling human behavior incorporating data mining and expert knowledge from behavioral sciences. Data analysis results extracted from clean data of laboratory experiments will be compared with noisy industrial datasets 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.
The workshop session will be held at the main venue (Myasnitskaya 11) in room 421.
- Economic Applications of Machine Learning
- Economic Innovations and Data Mining
- Experimental Economics and Complex Networks
- Econometrics VS Machine Learning & Data Mining
- Human Behavior Modeling and Game Theory
- Innovative applications of Concept Lattices in Economics & Data Mining
- Interdisciplinary Data Science
- Knowledge Discovery in Economics Domain
- Natural Language Processing in Economics Domain
- Machine Learning for Social Sciences
- New Modeling Languages for Economics (Bayes and Markov Nets, Petri Nets etc.)
- Ontologies for Economics
- Real Data Mining Projects
Rustam Tagiew, Managing Shareholder, Polarez Engineering, Germany
Dmitry Ignatov, Associate Professor, National Research University Higher School of Economics, Russia
Andreas Hilbert, Professor for Business Intelligence, TU Dresden, Germany
Radhakrishnan Delhibabu, Associate Professor, Kazan Federal University, Russia
Submission deadline: June 12, 2016
Notification of acceptance: June 18, 2016
Camera-ready copy: June 30, 2016
Workshop: July 18, 2016
In conjunction with The 13th International Conference on Concept Lattices and Their Applications
Higher School for Economics, Myasnitskaya street 11, Moscow, room 421.
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. EEML 2012 Proceedings are available at the CEUR-Workshop web site: http://ceur-ws.org/Vol-870/. Second EEML took place in Dallas, TX, USA as a part of the workhsop program of IEEE ICDM 2013. EEML 2013 delivered talks are listed on the workshop website http://eeml.hse.ru/2013/ and the proceedings can be downloaded from IEEE Xplore as a part of ICDMW 2013.
Submission ProcedureElectronic version of full paper complete with authors’ affiliations should be submitted through the conference electronic submission system.
Use the submission link http://www.easychair.org/conferences/?conf=eeml2016.
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.