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<pdf width="1500px">https://ceur-ws.org/Vol-1627/preface.pdf</pdf>
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                                  Preface


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 financial 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 significance 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-beneficial results.
    This year the workshop has hosted nine regular papers and two research
proposals on a variety of topics related to different 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 different 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.


July 18, 2016                                                   Rustam Tagiew
Moscow                                                        Dmitry I. Ignatov
                                                                Andreas Hilbert
                                                       Radhakrishnan Delhibabu

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