Safety, liveness, fairness applied to quantitative argumentation dialogues

A new paper from researchers Arunavo Ganguly, Julian Alfredo Mendez, and Timotheus Kampik applies temporal reasoning concepts—safety, liveness, and fairness—to the domain of quantitative argumentation dialogues. In these dialogues, AI agents repeatedly draw inferences from bipolar argumentation graphs, where nodes have weighted strengths and edges represent supportive or attacking relations. The graph updates between inference rounds, making dynamic reasoning complex. The authors adapt classic temporal logic properties to this setting, offering a formal framework to ensure reliable and equitable argument outcomes.

Specifically, they define strong safety (argument strengths always remain above a justification threshold) and weak safety (strengths eventually reach that threshold). Liveness captures the desirable fluctuation of strengths across the threshold—ensuring arguments aren't stuck below justification. Fairness measures how evenly the property of being 'safe' (above threshold) is distributed across arguments over time. The paper proves relationships among these properties and highlights analytical challenges for providing general guarantees. This work has implications for trustworthy multiagent systems, where argumentation must be both robust and equitable, especially in applications like AI-driven debate, automated negotiation, or collective decision-making.