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Fifth International Workshop on Experimental Economics and Machine Learning (EEML 2019)

 

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.

Topics of interest

Topics of interest include but are not limited to:

  • 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
  • Interpretable Machine Learning

Keywords

Game Theory, Web Mining, Mechanism Design, Behavioral Science, Machine Learning, Business Intelligence, Data Mining, Experimental Economics, Complex Networks, Econometrics, Human Behavior Modeling, Concept Lattices, Behavioral Economics, Data Science, Knowledge Discovery, Text Mining, Social Sciences, Bayes Nets, Markov Nets, Petri Nets, Neural Nets, Decision Trees, Linear Models, Clustering, Ontologies, Real Data, Cognitive Science

Workshop co-chairs

Rustam Tagiew, Alumni of TU Bergakademie Freiberg, Germany
Kai Heinrich, Scientific Assistant, TU Dresden, Germany
Dmitry Ignatov, Associate Professor, National Research University Higher School of Economics, Russia
Aleksey Buzmakov, Associate Professor, National Research University Higher School of Economics, Russia

Invited speakers

Ayelet Gneezy, Associate Professor of Behavioral Sciences & Marketing, Rady School of Management, University of California San Diego
Title: Field Experimentation in Marketing Research
Abstract: Despite increasing efforts to encourage the adoption of field experiments in marketing research (e.g., Campbell 1969; Cialdini 1980; Li et al. 2015), the majority of scholars continue to rely primarily on laboratory studies (Cialdini 2009). For example, of the 50 articles published in Journal of Marketing Research in 2013, only three (6%) were based on field experiments. The goal of this article is to motivate a methodological shift in marketing research and increase the proportion of empirical findings obtained using field experiments. The author begins by making a case for field experiments and offers a description of their defining features. She then demonstrates the unique value that field experiments can offer and concludes with a discussion of key considerations that researchers should be mindful of when designing, planning, and running field experiments.

Dr. Gneezy's research has been published in leading academic journals, including Science, PNAS, the Journal of Marketing Research, Marketing Science, the Journal of Personality and Social Psychology, and the Journal of Consumer Research, and was featured by top media outlets such as The Wall Street Journal, The New York Times, Scientific American, The Huffington Post, and The Atlantic.

Her research addresses a wide variety of questions pertaining to consumer behavior such as behavioral pricing, prosocial behavior & charitable giving, social preferences (e.g., promise accounting, negative reciprocity, fairness), and factors affecting individuals’ quality of life. In her research, Dr. Gneezy collaborates with both small (e.g., a local winery) and large (e.g., Disney) firms and organizations, allowing her to conduct field experiments and test her predictions in “the wild.”

She is the co-founder and faculty director of the Rady School of Management’s US-Israel on Innovation & Economic Sustainability (USIC), and the Center for Social Innovation & Impact (CSII).

Vasily A. Leksin, Unit Leader in Recommender Systems, AVITO.ru, Moscow
Title: Recommender Systems for online classified advertisements
Abstract: I’ll tell about our journey from simple linear models to neural networks in the development of Recommender Systems in Avito and the current recommendations architecture. I will share successes and failures in building a highly loaded Recommendation Systems. We will discuss the gap between the latest scientific approaches and methods applicable in production systems and why we claim that the “keep it simple” principle works well in production systems.

Since 2013 Vasily Leksin has been working at AVITO.ru. Up to 2016, Vasily held a position of Lead Analyst. He was responsible for building and leading Antifraud team. Since 2016 Vasily performs as a Unit Leader in Recommender Systems. The team of talented Data Science professionals, Vasily leads, took 3’d place of 112 at RecSys Challenge 2018. Recommender systems generate significant share (roughly 30%) of Avito app buyers activity now.

He graduated in 2007 as a “Master of Applied Mathematics and Physics” at the “Moscow Institute of Physics and Technology” (Russia, Moscow). In 2012 he obtained his degree of “Candidate of Physico-Mathematical Sciences” (the equivalent of PhD) at the “Dorodnicyn Computing Centre of RAS”, Ph.D. thesis: "Stochastic models in customer environment analysis".

The most recent Vasily’s scientific publications in a field of recommender systems are:

  • A hybrid two-stage recommender system for automatic playlist continuation which has been published in RECSYS CHALLENGE'18: PROCEEDINGS OF THE ACM RECOMMENDER SYSTEMS CHALLENGE 2018
  • Combination of Content-Based User Profiling and Local Collective Embeddings for Job Recommendation. V. Leksin, A. Ostapets, M. Kamenshikov, D. Khodakov, V. Rubtsov. Published in: CEUR Workshop Proceeding, Vol. 1968, No. Experimental Economics and Machine Learning, 2017
  • Job Recommendation Based on Factorization Machine and Topic Modelling. V. Leksin, A. Ostapets. ACM RecSys Challenge 2016 workshop
  • Evolution of content moderation approaches for online classifieds: from action recommendations to automation. Ivan Guz, Vasily Leksin, Mikhail Trofimov, Alexandra Fenster. MMPR-17 proceedings, 2015
  • Semi-supervised Tag Extraction in a Web Recommender System. Leksin V., Nikolenko S. I. Proceedings of the 6th International Conference on Similarity Search and Applications (SISAP 2013), Lecture Notes in Computer Science, 20

 

Proceedings

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.

Submission Procedure

Electronic version of full paper complete with authors’ affiliations should be submitted through the conference electronic submission system. Use the submission link https://easychair.org/conferences/?conf=eeml2019.

Manuscripts are limited to be from 5 to 12 pages long and must be prepared with LaTeX or Microsoft Office and should follow the Springer format available at http://www.springer.de/comp/lncs/authors.html.

Venue

In conjunction with the 7th International Conference on Applied Research in Economics 2019.

Higher School of Economics, 38 Studencheskaya street, Perm, Russia

Auditorium 103

Workshop Progarm

Auditorium 103 (38 Studencheskaya street, Perm, Russia)
9:00 (GMT+5)
Registration and cofee

Coffee and tea available - Dining Hall

9:50-10:00
Opening remarks
10:00-11:00
KEYNOTE TALK

Professor Ayelet Gneezy, Rady School of Management, UC San Diego, USA

Field Experimentation in Marketing Research

11:00-12:30
Regular Talks I
11:30-12:00
Fundamental Factors Affecting the IMOEX Russia Index: Retrospective Analysis

Anastasiia Saltykova and Agata Lozinskaia

12:00-12:30
Deep Reinforcement Learning with VizDoom First-Person Shooter

Dmitry Akimov and Ilya Makarov

The Comparison of Methods for Individual Treatment Effect Detection

Daria Teterina and Maria Temirkaeva

12:30-13:00
Coffee break - Dining Hall
13:00-14:30
Regular Talks II
13:00-13:30
Effective Algorithms for Constructing Multiplex Networks Embedding

Ilya Makarov and Pavel Zolnikov

13:30-14:00
Sequence Mining for Searching Interpretable Demographic Patterns

Anna Muratova, Robiul Islam and Ekaterina Mitrofanova

14:00-14:30
Neural Network Associative Forecasting of Demand for Goods

Vasiliy Osipov, Nataly Zhukova and Dmitriy Miloserdov

14:30-15:30
Lunch (self serve)
15:30-16:10
INDUSTRY TALK

Vasily A. Leksin, PhD, Unit Leader in Recommender Systems, AVITO.ru, Moscow

Recommender Systems for online classified advertisements

16:10-17:50
Short Talks and Project Proposals
16:10-16:30
Deception Detection in Online Media

Ilya Makarov and Alsu Zaynutdinova

16:30-16:50
Timur Kadyrov and Dmitry Ignatov

Attribution of Customer’s Actions Based on Machine Learning Approach

16:50-17:10
DeepQuarantine

NameNikita Benkovich, Roman Dedenok and Dmitry Golubev

17:10-17:30
General Game Playing B-to-B Price Negotiations

Friedrich Michael

17:30-17:50
Glioblastoma Multiforme Classification On High Resolution Histology Image Using Deep Spatial Fusion Network

Sobana Sumi P and Radhakrishnan Delhibabu

17:50-18-00
Closing remarks