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Abstract:
The web economy offers a wealth of challenging problems for algorithmic research, many of which require fundamentally probabilistic techniques.
In this talk, I will briefly discuss two examples from our work. First, I will consider the classic online matching problem, a central primitive in online advertising. We prove a new impossibility result: no online algorithm can achieve a competitive ratio better than 1-e^{1-e}. Second, I will turn to problems arising in discrete choice, a framework widely used in economics. In particular, within random utility models (RUMs) – introduced by Nobel laureate Daniel McFadden – we establish a striking impossibility result about the learnability of user preferences. This result shows that even with massive data and computational power, web platforms face fundamental limits in inferring user behaviour.
This is joint work with Flavio Chierichetti, Mirko Giacchini, Ravi Kumar, Erasmo Tani, Andrew Tomkins and Andrea Vattani.