Human reasoning has long been theorised to operate socially, not through isolated individual cognition, but through collective adversarial discourse, a framework known as the Argumentative Theory of Reasoning (ATR). Rather than relying on individual "intellectualist reasoners" as the primary vehicle for truth-seeking, ATR reconceptualises truth as an emergent property of social epistemology: the product of imperfect individual reasoning refined under the adversarial pressure of debate. This distributed method of collective intelligence has guided humanity to ever-greater epistemic heights and underpins the foundational principles of all democratic systems. This thesis breaks new ground by, for the first time, simulating ATR through the multi-agent debate (MAD) of large language models (LLMs). With rigorous empirical analysis, we demonstrate that, when correctly engineering an epistemically diverse set of models, LLM-MAD can significantly improve truth-seeking performance on questionnaire-based tasks, even when individual debate participants exhibit limited standalone performance. Furthermore, we present strong empirical evidence that this performance gain is mechanistically grounded in the central principles of ATR, suggesting that collective reasoning may be universally favourable over individualist reasoning, rather than a quirk in biology or evolution. Finally, drawing on our analysis of debate dynamics, we propose a novel benchmarking methodology that leverages LLM-MAD to measure intrinsic model properties (such as hallucination propensity) in order to compare models in ways that current static benchmarking approaches cannot support.

翻译：人类推理长期以来在理论上被认为具有社会性，并非通过孤立的个体认知运作，而是通过集体的对抗性对话——这一框架被称为“推理论证理论”（ATR）。ATR 不将个体“理性主义推理者”视为探求真理的主要载体，而是将真理重新概念化为社会认知中涌现出的属性：即不完美个体推理在辩论的对抗压力下精炼的产物。这种分布式集体智能方法引导人类达到了前所未有的认知高度，并构成了所有民主制度的基础原则。本论文首次通过大型语言模型（LLM）的多智能体辩论（MAD）来模拟ATR，开创了该领域的新局面。通过严谨的实证分析，我们证明：在正确构建一组认知多样性模型的前提下，即使单个辩论参与者的独立表现有限，LLM-MAD也能显著提升基于问卷任务的真理探求性能。此外，我们提供了强有力的实证证据，表明这种性能提升在机制上植根于ATR的核心原则，暗示集体推理可能普遍优于个体推理，而非生物学或进化中的偶然现象。最后，基于对辩论动态的分析，我们提出了一种新颖的基准测试方法，利用LLM-MAD来测量模型的内在属性（如幻觉倾向），从而以当前静态基准测试方法无法支持的方式对模型进行比较。