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https://ceur-ws.org/Vol-1968/preface.pdf
Rustam Tagiew, Kai Heinrich, Dmitry I. Ignatov, Andreas Hilbert,
Radhakrishnan Delhibabu (Eds.)
EEML 2017 – The 4th International Workshop on
Experimental Economics and Machine Learning
September 17-18, 2017, Dresden, Germany
Volume Editors
Rustam Tagiew,
ONTONOVATION, Dresden, Germany
Kai Heinrich,
Business Intelligence Research, Faculty of Business and Economics
Technische Universität Dresden, Germany
Dmitry I. Ignatov,
Department of Data Analysis and AI, Faculty of Computer Science
National Research University Higher School of Economics, Moscow, Russia
Andreas Hilbert,
Business Intelligence Research, Faculty of Business and Economics
Technische Universität Dresden, Germany
Radhakrishnan Delhibabu,
Institute of Information Technology and Information Systems
Kazan Federal University, Russia
The proceedings are published online on the CEUR Workshop Proceedings web
site, Vol. 1968, in a series with ISSN 1613-0073.
Copyright c 2017 for the individual papers by papers’ authors, for the Volume
by the editors. All rights reserved. No part of this publication may be reproduced,
stored in a retrieval system, or transmitted, in any form or by any means without
the prior permission of the copyright owners.
Preface
This volume contains the papers presented at the Fourth International Workshop
on Experimental Economics and Machine Learning held during September 17-18,
2017 at Technische Universität Dresden, Germany.
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.
Due to the problem importance, it is not surprising that the Royal Swedish
Academy of Sciences has decided to award the Sveriges Riksbank Prize in Eco-
nomic Sciences in Memory of Alfred Nobel 2017 to Richard H. Thaler (University
of Chicago, IL, USA) ”for his contributions to behavioural economics”. Thus, he
has incorporated psychologically-based assumptions such as limited rationality,
social preferences, and lack of self-control into analyses of economic decision-
making. By exploring their consequences, he has shown how these human fea-
tures systematically affect individual decisions and even market outcomes.
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 data. It calls for the development and evaluation
of ancillary models. The more data is used for evaluation, the more statistical
significance can be achieved. Proven regularities from one dataset can help to un-
derstand another datasets. Since large amounts of behavioral data are required
to scan for regularities, Machine Learning is the tool of choice for research in Ex-
perimental 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 six regular papers out of 11 and one
research proposal on a variety of topics related to different aspects of human
behavior in games, demography, social and monetary interactions, recommender
systems for job markets, stock markets, scientific publication activity, etc. Each
paper has been reviewed by three PC members at least; all these papers rely
on different data analysis techniques and the presented results are supported by
data.
Dr. Kai Heinrich from TU-Dresden has presented a keynote talk on Data
Science and Economics.
We would like to thank all the authors of submitted papers and the Program
Committee members for their commitment. We are grateful to local organis-
ers and our sponsor: Technische Universität Dresden. Finally, we would like to
acknowledge the EasyChair system which helped us to manage the reviewing
process.
September 17-18, 2017 Rustam Tagiew
Dresden Kai Heinrich
Dmitry I. Ignatov
Andreas Hilbert
Delhibabu Radhakrishnan
Organisation
Program Committee
Fadi Amroush University of Granada, Spain
Danil Fedorovykh National Research University Higher School of Eco-
nomics, Moscow, Russia
Jaume Baixeries Universitat Politècnica de Catalunya, Catalunya,
Spain
Kai Heinrich Technische Universität Dresden, Germany
Andreas Hilbert Technische Universität Dresden, Germany
Dmitry I. Ignatov National Research University Higher School of Eco-
nomics, Russia
Heinrich Jasper Technische Universität Bergakademie Freiberg, Ger-
many
Alexander Karpov National Research University Higher School of Eco-
nomics, Russia
Mehdi Kaytoue LIRIS - INSA de Lyon, France
Mikhail Khachay Krasovsky Institute of Mathematics and Mechanics
of RAS, Russia
Natalia Konstantinova University of Wolverhampton, UK
Yevgeniya Kovalchuk Birmingham City University, UK
Xenia Naidenova Military Medical Academy, Russia
Amedeo Napoli LORIA Nancy (CNRS - Inria - Université de Lor-
raine), France
Alexey Neznanov National Research University Higher School of Eco-
nomics, Russia
Sergey Nikolenko Steklov Mathematical Institute, Russia
Heather Pfeiffer Akamai Physics, Inc., US
Jonas Poelmans Clarida Technologies Ltd., UK
Delhibabu Radhakrishnan Kazan Federal University, Kazan, Russia
Artem Revenko Semantic Web Company GmbH, Austria
Rustam Tagiew ONTONOVATION, Germany
Elena Treshcheva Saratov Federal University, Russia
Elena Tutubalina Kazan Federal University, Russia
Dmitry Ustalov Krasovsky Institute of Mathematics and Mechanics
of RAS and Ural Federal University, Russia
Table of Contents
Keynote Talk
Deep Learning and Economical Applications . . . . . . . . . . . . . . . . . . . . . . . . . . 1
Kai Heinrich
Regular Papers
Predicting Psychology Attributes of a Social Network User . . . . . . . . . . . . . 2
Rustem M. Khayrullin, Ilya Makarov and Leonid E. Zhukov
Combination of Content-Based User Profiling and Local Collective
Embeddings for Job Recommendation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
Vasily Leksin, Andrey Ostapets, Mikhail Kamenshikov, Dmitry Kho-
dakov and Vasily Rubtsov
Sociality is Not Lost with Monetary Transactions within Social Groups . . 18
Evgeniya Lukinova, Tatiana Babkina, Anna Sedush, Ivan Menshikov,
Olga Menshikova and Mikhail Myagkov
Black-Box Classification Techniques for Demographic Sequences: from
Customised SVM to RNN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
Anna Muratova, Pavel Sushko and Thomas Espy
Do Women Socialize Better? Evidence from a Study on Sociality Effects
on Gender Differences in Cooperative Behavior . . . . . . . . . . . . . . . . . . . . . . . 41
Anastasia Peshkovskaya, Mikhail Myagkov, Tatiana Babkina and Ev-
geniya Lukinova
Behavior Mining in h-index Ranking Game . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
Rustam Tagiew and Dmitry I. Ignatov
Abstracts
Empirical Evaluation of Neural Networks on Stocks of Pakistan Stock
Exchange . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
Ali Abdullah, Ambreen Hanif and Noman Javed