Iterative Judgment Aggregation
This work extends iterative consensus methods from voting to judgment aggregation, addressing a specific problem in computational social choice, but it appears incremental as it adapts known techniques to a broader context.
The paper tackles the problem of collective decision-making in judgment aggregation by proposing an iterative consensus-building algorithm based on graph movements, and it analyzes termination conditions and computational complexity compared to existing operators.
Judgment aggregation problems form a class of collective decision-making problems represented in an abstract way, subsuming some well known problems such as voting. A collective decision can be reached in many ways, but a direct one-step aggregation of individual decisions is arguably most studied. Another way to reach collective decisions is by iterative consensus building -- allowing each decision-maker to change their individual decision in response to the choices of the other agents until a consensus is reached. Iterative consensus building has so far only been studied for voting problems. Here we propose an iterative judgment aggregation algorithm, based on movements in an undirected graph, and we study for which instances it terminates with a consensus. We also compare the computational complexity of our iterative procedure with that of related judgment aggregation operators.