Title: Computing Pareto Frontiers for Randomized Mechanisms
In mechanism design we usually want to achieve strategyproofness.
Unfortunately, this is often in conflict with other designing goals, such as efficiency.
Mennle and Seuken (2015) formulated a linear program and created an algorithm that finds a most efficient mechanism for every degree of manipulability.
They called the resulting mechanisms the Pareto frontier.
In this thesis we first prove a theorem that reduces a linear program using equivalence relations based on symmetry arguments, such as anonymity and neutrality.
Second, we implement the algorithm with Java and compute the Pareto frontier for different settings.
These computational results allow us to formulate three new conjectures about the structure of the Pareto frontier.