|Institution:||University of British Columbia|
|Full text PDF:||http://hdl.handle.net/2429/52868|
A mechanism is a formal protocol for collective decision making among self-interested agents. Mechanisms model many important social processes from auctions to elections. They are also widely studied in computer science: the participants in real-world mechanisms are often autonomous software systems (e.g., algorithmic bidding and trading agents), and algorithmic problems (e.g., job scheduling) give rise to mechanisms when users have competing interests. Strategic behavior (or "gaming") poses a major obstacle to understanding mechanisms. Although real-world mechanisms are often fairly simple functions (consider, e.g., plurality voting), a mechanism's outcome depends on both this function and on agents' strategic choices. Game theory provides a principled means for analyzing such choices. Unfortunately, game theoretic analysis is a difficult process requiring either human effort or very large amounts of computation. My thesis is that mechanism analysis can be done computationally, due to recent advances in compact representations of games. Compact representation is possible when a game's description has a suitable independence structure. Exploiting this structure can exponentially decrease the space required to represent games, and exponentially decrease the time required to analyze them. The contributions of my thesis revolve around showing that the relevant kinds of structure (specifically, the structure exploited by action-graph games) are present in important mechanisms of interest. Specifically, I studied two major classes of mechanisms, position auctions (as used for internet advertising) and voting rules. In both cases, I was able to provide exponential improvements in the space and time complexity of analysis, and to use those improvements to address important open questions in the literature. I also introduced a new algorithm for analyzing action-graph games, with faster empirical performance and additional benefits over the previous state-of-the-art. Finally, I created the Positronic Economist system, which consists of a python-based descriptive language for mechanism games, with automatic discovery of computationally useful structure.