Proceedings of the Fourth Workshop on Experimental Economics and Machine Learning (EEML 2017), Dresden, Germany, September 17-18, 2017

Vol. 1968. Ахен : CEUR Workshop Proceedings, 2017.

Научный редактор: R. Tagiew, Игнатов Д. И., A. Hilbert, K. Heinrich, R. Delhibabu

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.

 

 

Главы книги

Behavior Mining in h-index Ranking Game

Тагиев Р., Игнатов Д. И., , in: Proceedings of the Fourth Workshop on Experimental Economics and Machine Learning (EEML 2017), Dresden, Germany, September 17-18, 2017Vol. 1968.: Aachen: CEUR Workshop Proceedings, 2017. Ch. 6 P. 52–64.

Добавлено: 17 декабря 2017 г.

Predicting Psychology Attributes of a Social Network User

Rustem M. Khayrullin, Макаров И. А., Жуков Л. Е., , in: Proceedings of the Fourth Workshop on Experimental Economics and Machine Learning (EEML 2017), Dresden, Germany, September 17-18, 2017Vol. 1968.: Aachen: CEUR Workshop Proceedings, 2017. P. 2–8.

Nowadays, the number of people using social network site increases every day. The social networking sites, such as Facebook or Twitter, are sources of human interaction, where users are allowed to create and share their activities, thoughts and place different information about themselves. However, most of this information remains unnoticed. In this work, we propose ...

Добавлено: 10 октября 2017 г.

Black-Box Classification Techniques for Demographic Sequences: from Customised SVM to RNN

A. Muratova, P. Sushko, T. Espy, , in: Proceedings of the Fourth Workshop on Experimental Economics and Machine Learning (EEML 2017), Dresden, Germany, September 17-18, 2017Vol. 1968.: Aachen: CEUR Workshop Proceedings, 2017. P. 31–40.

Nowadays there is a large amount of demographic data which should be analysed and interpreted. From accumulated demographic data, more useful information can be extracted by applying modern methods of data mining. The aim of this study is to compare the methods of classification of demographic data by customizing the SVM kernels using various similarity ...

Добавлено: 9 декабря 2017 г.

Do Women Socialize Better? Evidence from a Study on Sociality Effects on Gender Differences in Cooperative Behavior.

Babkina T., Peshkovskaya A. G., Мягков М. Г., , in: Proceedings of the Fourth Workshop on Experimental Economics and Machine Learning (EEML 2017), Dresden, Germany, September 17-18, 2017Vol. 1968.: Aachen: CEUR Workshop Proceedings, 2017. P. 41–51.

Добавлено: 7 июня 2018 г.

Sociality is Not Lost with Monetary Transactions within Social Groups.

Babkina T., Lukinova E., Sedush A. и др., , in: Proceedings of the Fourth Workshop on Experimental Economics and Machine Learning (EEML 2017), Dresden, Germany, September 17-18, 2017Vol. 1968.: Aachen: CEUR Workshop Proceedings, 2017. P. 18–30.

Добавлено: 4 июня 2018 г.

Ключевые слова: experimental economics, machine learning