An Axiomatic Approach to Ranking Systems

This talk will survey some of our recent work on the axiomatic approach to ranking systems. Ranking systems are systems in which agents rank each other to produce a social ranking. In the axiomatic approach we study ranking systems under the light of basic properties, or axioms. This study can be viewed as an extension of the celebrated theory of social choice, and is applicable to a variety of settings such as page ranking and trust systems.

In this talk I will present our axiomatization theorem for the PageRank ranking system, present an impossibility and possibility result for general ranking systems, and discuss the issue of incentives in ranking systems. Finally, I will show some results regarding personalized ranking systems, where a specialized ranking is generated for each agent.

Speaker Details

Moshe Tennenholtz is a professor with the faculty of Industrial Engineering and Management at the Technion, where he holds the Sondheimer Technion Academic Chair. During 1999-2002 he has been a visiting professor at Stanford CS department, where he has also been a research associate during the years 1991-1993. Moshe received his B.Sc. in Mathematics from Tel-Aviv University (1986), and his M.Sc. and Ph.D. (1987, 1991) from the Department of Applied Mathematics and Computer Science in the Weizmann Institute. His area of research lies in the interface between Artificial Intelligence and Game Theory. Among his contributions, in joint work with colleagues and students, he introduced the theories of artificial social systems, co-learning, distributed games, and non-cooperative computing, the axiomatic approach to ranking systems, as well as the study of program equilibrium and learning equilibrium.Until recently Moshe served as the editor-in-chief of the Journal of Artificial Intelligence Research (JAIR). He is also an associate editor of Games and Economic Behavior (GEB), the international journal of autonomous agents and multi-agent systems, and an editorial board member of the AI magazine, and of the Journal of Machine Learning Research (JMLR).

Date:
Speakers:
Moshe Tennenholtz
Affiliation:
Technion--Israel Institute of Technology

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