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Книга

Proceedings of the Third Workshop on Experimental Economics and Machine Learning (EEML 2016), Moscow, Russia, July 18, 2016

Vol. 1627. Aachen: CEUR Workshop Proceedings, 2016.
Научный редактор: R. Tagiew, D. I. Ignatov, A. Hilbert, R. Delhibabu.

This volume contains the papers presented at the Third International Workshop on Experimental Economics and Machine Learning held on July 18, 2016 at the National Research University Higher School of Economics, Moscow. This proceedings concentrates on an interdisciplinary approach to modelling human behavior incorporating data mining and expert knowledge from behav- ioral sciences. Data analysis results extracted from clean data of laboratory ex- periments are of advantage if compared with noisy industrial datasets from the web and other sources. In their turn, insights from behavioral sciences help data scientists. Behavior scientists 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 fi nancial rewards restrict subjects pref- erences in experiments, the exclusive application of analytical game theory is not enough to explain the collected data. It calls for the development and evalua- tion of more sophisticated models. The more data is used for evaluation, the more statistical signi fi cance can be achieved. Since large amounts of behavioral data are required to scan for regularities, Machine Learning is the tool of choice for research in Experimental Economics. In some works, automated agents are needed to simulate and intervene in human interactions. This proceeding aims to create a forum, where researchers from both Data Analysis and Economics are brought together in order to achieve mutually-bene fi cial results. This year the workshop has hosted nine regular papers and two research proposals on a variety of topics related to di ff erent aspects of human behavior in games, demography, economy crises, stock markets, etc. Each paper has been reviewed by two PC members at least; all these papers rely on di ff erent data analysis techniques and the presented results are supported by data. The representatives of R&D department of Imhonet company, Vladimir Bo- brikov and Elena Nenova, have presented a keynote talk concerning how to consistently value recommendations produced by recommender systems. We would like to thank all the authors of submitted papers and the Pro- gram Committee members for their commitment. We are grateful to our invited speaker and our sponsors: National Research University Higher School of Eco- nomics (Moscow, Russia), Russian Foundation for Basic Research, and ExactPro. Finally, we would like to acknowledge the EasyChair system which helped us to manage the reviewing process.

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Proceedings of the Third Workshop on Experimental Economics and Machine Learning (EEML 2016), Moscow, Russia, July 18, 2016