用语义向量模拟用户观点演化，可控调节共识与分歧程度。
Generating consensus and dissent on massive discussion platforms with a semantic-vector model
- 基于语义向量和二维格点模型模拟用户观点交互
- 耦合参数β>0时形成全局共识，β<0时产生最大分歧
- 为集体智能平台提供调控观点多样性与凝聚力的工具
在大规模讨论平台上达成共识对减少噪声、实现最优集体决策至关重要。然而人类固有的观点坚持倾向限制了全局解决方案的出现。为此，集体智能（CI）平台致力于发现更优的全局解。本文提出一种基于标准O(N)模型的动力学系统，推动语义相似观点的聚合。系统将用户建模为二维格点上的节点，通过预训练嵌入模型计算其观点的语义向量，并引入近邻相互作用。分析表明，当耦合参数β > 0时，系统趋于铁磁态（全局共识）；β < 0时则进入反铁磁态（最大分歧），用户最大化与邻居的语义距离。该框架可有效控制CI平台中凝聚力与多样性的权衡。
原文摘要 · Abstract (English)
Reaching consensus on massive discussion networks is critical for reducing noise and achieving optimal collective outcomes. However, the natural tendency of humans to preserve their initial ideas constrains the emergence of global solutions. To address this, Collective Intelligence (CI) platforms facilitate the discovery of globally superior solutions. We introduce a dynamical system based on the standard $O(N)$ model to drive the aggregation of semantically similar ideas. The system consists of users represented as nodes in a $d=2$ lattice with nearest-neighbor interactions, where their ideas are represented by semantic vectors computed with a pretrained embedding model. We analyze the system's equilibrium states as a function of the coupling parameter $β$. Our results show that $β> 0$ drives the system toward a ferromagnetic-like phase (global consensus), while $β< 0$ induces an antiferromagnetic-like state (maximum dissent), where users maximize semantic distance from their neighbors. This framework offers a controllable method for managing the tradeoff between cohesion and diversity in CI platforms.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。