| Sarah's Tech & Research Blog
VCG is neither individually rational nor budget balanced in general, but could there exist some other incentive-compatible mechanism that is? This post from Game Theory Online (http://www.game-theory-class.org) gives an example that proves the Myerson-Satterthwaite Theorem, which states that there is no such mechanism.
In general, VCG is neither budget balanced nor individually rational. This post from Game Theory Online (http://www.game-theory-class.org) introduces some additional assumptions under which it is possible to get individual rationality and budget balance in VCG. It features Kevin Leyton-Brown (UBC).
If VCG is so wonderful, why don’t we use it for everything? This post from Game Theory Online (http://www.game-theory-class.org) describes some of the VCG’s serious drawbacks. It features Kevin Leyton-Brown (UBC).
This post from Game Theory Online (http://www.game-theory-class.org) gives an example of VCG in action. It features Yoav Shoham (Stanford).
This post from Game Theory Online (http://www.game-theory-class.org) formally defines the VCG mechanism, and proves some of its desirable properties. It features Matt Jackson (Stanford).
The Vickrey-Clarke-Groves mechanism, or VCG, is one of mechanism design’s success stories. This post from Game Theory Online (http://www.game-theory-class.org) gives a high-level introduction to the VCG mechanism, what it does, why it is important, and the kinds of settings to which it applies. It features Kevin Leyton-Brown (UBC).
In this work, we propose a new, general way of modeling privacy in players’ utility functions. Specifically, we only assume that if an outcome o has the property that any report of player i would have led to o with approximately the same probability, then o has a small privacy cost to player i.
Towards constructing mechanisms for privacy-aware agents, we put forward and justify a model of privacy-aware mechanism design. We then show that privacy-aware mechanisms are feasible.
This paper explores the role of privacy-preserving algorithms in mechanism design, specifically using differential privacy to ensure that participants have limited effect on the outcome of the mechanism and limited incentive to lie.