Iterative Goal-Based Voting Leyla Ade Abstract: Goal-based voting is a new voting framework in which agents can submit propositional formulae as their goals. We study iterated applications of the majorities and approval rules in this framework. We introduce notions of satisfaction based on the Hamming distance between an agent’s goal and the interpretations in the outcome under a given rule. The contribution of this thesis is twofold: First, we analyze the convergence of the iteration. We show that the Majority rules and the Approval rule for some satisfaction functions are not guaranteed to terminate, while other cases of Approval voting do always converge. Second, we study the quality of iteration. The first part of this analysis consists of theoretical results, showing that in cases where termination of Approval voting is guaranteed we also have an improvement of the social welfare. The second part consists of an implementation of the iterative process in Python for the cases not covered by our theoretical results, which gives us preliminary insights on the frequency and quality of iteration.