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Regular version of the site

Book

EEML 2012 – Experimental Economics in Machine Learning

Academic editor: R. Tagiew, D. I. Ignatov, A. Neznanov, J. Poelmans.

In Experimental Economics, laboratory and f eld experiments are conducted on subjects in order to improve theoretical knowledge about human behavior in interactions. Although paying diff erent amounts of money restricts the preferences of the subjects in experiments, the exclusive application of analytical game theory does not suce to explain the recorded data. It exacts the development and evaluation of more sophisticated models. In some experiments, human subjects are involved into an interaction with automated agents and these agents are used for simulating human interactions. The more data is used for the evaluation, the more of statistical signi cance can be achieved. Since huge amounts of behavioral data are required to be scanned for regularities and automated agents are required to simulate and to intervene human interactions, Machine Learning is the tool of choice for the research in Experimental Economics. Moreover modern economics extensively involves network structures, which can be modeled as graphs or more complicated relational structures.

This volume contains the papers presented at the inaugural International Workshop on Experimental Economics and Machine Learning (EEML 2012) held on May 9, 2012 at the Katholieke Universiteit Leuven, Belgium. This year the committee decided to accept 8 full papers for publication in the proceedings and two abstracts for presentation at the conference. Each submission was reviewed by on average 3 program committee members. R. Tagiew proposes a new method for mining determinism in human strategic behavior. N. Buzun et al. present a comparison of methods and measures for overlapping community detection. A. Fishkov et al. discuss a new click model for relevance prediction inWeb search. A. Drutsa et al. applied novel data visualisation techniques to socio-semantic network data. Gilabert et al. made an experimental study on the relationship between trust and budgetary slack. O. Barinova et al. proposed using online random forest for interactive image segmentation. A. Bezzubtseva et al. built a new typology of collaboration platform users. V. Zaharchuk et al. proposed a new recommender system for interactive radio network services. D. Ignatov et al. designed a prototype system for collaborative platform data analysis.

We would like to express our gratitude to all contributing authors and reviewers, especially to Malay Bhattacharyya, Hoang Thanh Lam, Olga Barinova and Alexandra Kaminskaya for their enormous e orts. We also want to thank our sponsors Amsterdam-Amstelland police, IBM Belgium, Research Foundation Flanders, Vlerick Management School, OpenConnect Systems and Higher School of Economics.

Chapters
EEML 2012 – Experimental Economics in Machine Learning