Agent-based modeling is a technique used to study relationships between variation in parameter values or patterns of interaction at the micro-level and outcomes at the macro-level. By using computer simulation of landscapes inhabited by cells, or “agents,” the modeler can produce many virtual histories of the landscape under different initial conditions (randomized or not) and under various experimental conditions. In this article we report the findings of experiments run with the Agent-Based Argument Repertoire (ABAR) Model—experiments designed to help answer some of the practical questions that arise in discussions of the contribution-enhanced public discourse, that is, more and better deliberation or argumentation among citizens might contribute to the quality of democracy.