Proceedings of the Third Workshop on Experimental Economics and Machine Learning (EEML 2016), Moscow, Russia, July 18, 2016
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.
We propose a general equilibrium model to study the spatial inequality of consumers and firms within a city. Our mechanics rely on Dixit and Stiglitz monopolistic competition framework. The firms and consumers are continuously distributed across a two-dimensional space, there are iceberg-Type costs both for goods shipping and workers commuting (hence firms have variable marginal costs based on their location). Our main interest is in the equilibrium spatial distribution of wealth. We construct a model that is both tractable and general enough to stand the test of real city empirics. We provide some theoretical statements, but mostly the results of numerical simulations with the real Moscow data.
In this paper, we present a cohort-based classification approach to the churn prediction for social on-line games. The original metric is proposed and tested on real data showing a good increase in revenue by churn preventing. The core of the approach contains such components as tree-based ensemble classifiers and threshold optimization by decision boundary.
Целью работы является сравнить изменения в потребительском поведении российских домохозяйств с середины 1990х до сеогдняшних дней. В исследовании рассмотрено потребительское поведение на максимально возможном периоде временных данных из RLMS с фокусом на годах кризиса. Особое внимание уделяется кризисам 1998 и 2008 гг. Чтобы отследить эффекты изменения потребительского поведения после кризисов используется квадратическая модель почти совершенного спроса (QAIDS) на панельных данных. В виду полного размытия панели с 1994 г. используется подход анализа псевдо-панельных данных.
We present two examples of how human-like behavior can be implemented in a model of computer player to improve its characteristics and decision-making patterns in video game. At first, we describe a reinforcement learning model, which helps to choose the best weapon depending on reward values obtained from shooting combat situations. Secondly, we consider an obstacle avoiding path planning adapted to the tactical visibility measure. We describe an implementation of a smoothing path model, which allows the use of penalties (negative rewards) for walking through ``bad'' tactical positions. We also study algorithms of path finding such as improved I-ARA* search algorithm for dynamic graph by copying human discrete decision-making model of reconsidering goals similar to Page-Rank algorithm. All the approaches demonstrate how human behavior can be modeled in applications with significant perception of intellectual agent actions.