Inspired by e-participation systems, in this paper we propose a new model to represent human debates and methods to obtain collective conclusions from them. This model overcomes drawbacks of existing approaches by allowing users to introduce new pieces of information into the discussion, to relate them to existing pieces, and also to express their opinion on the pieces proposed by other users. In addition, our model does not assume that users' opinions are rational in order to extract information from it, an assumption that significantly limits current approaches. Instead, we define a weaker notion of rationality that characterises coherent opinions, and we consider different scenarios based on the coherence of individual opinions and the level of consensus that users have on the debate structure. Considering these two factors, we analyse the outcomes of different opinion aggregation functions that compute a collective decision based on the individual opinions and the debate structure. In particular, we demonstrate that aggregated opinions can be coherent even if there is a lack of consensus and individual opinions are not coherent. We conclude our analysis with a computational evaluation demonstrating that collective opinions can be computed efficiently for real-sized debates.

中文翻译：

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支持集体推理的模型：形式化、分析和计算评估

受电子参与系统的启发，在本文中，我们提出了一种新模型来代表人类辩论和从他们那里获得集体结论的方法。该模型通过允许用户将新信息引入讨论中，将它们与现有信息相关联，以及表达他们对其他用户提出的信息的意见，克服了现有方法的缺点。此外，我们的模型并不假设用户的意见是理性的，以便从中提取信息，这一假设极大地限制了当前的方法。相反，我们定义了一个较弱的理性概念来表征连贯的意见，我们根据个人意见的连贯性和用户对辩论结构的共识程度来考虑不同的场景。综合考虑这两个因素，我们分析了不同意见聚合函数的结果，这些函数根据个人意见和辩论结构计算集体决策。特别是，我们证明，即使缺乏共识和个人意见不一致，汇总的意见也可以是连贯的。我们通过计算评估来结束我们的分析，表明可以有效地计算集体意见以进行实际规模的辩论。