集体智能作为降低广泛全球灾难性风险的基础设施
Collective Intelligence as Infrastructure for Reducing Broad Global Catastrophic Risks
AI总结：
本文将全球灾难性风险视为集体智能问题，综述提升人类群体信息处理与协作表现的条件，并探讨其在GCR缓解中的应用及未来研究方向。
摘要：
学术界和慈善界对全球灾难性风险（GCRs）日益关切，这些风险包括人工智能安全、流行病、生物安全和核战争。许多（即便不是全部）风险情境的结果都取决于人类群体的表现，例如政府或科学界能否有效协作。我们提议将这些问题视为集体智能（CI）问题——即如何有效处理分布式信息的问题。CI是一个跨学科研究领域，其应用涉及人类和动物群体、市场、机器人集群、神经元集合以及其他分布式系统。在本文中，我们主张提升人类群体的CI能够增强针对多种风险的总体韧性。我们总结了CI文献中关于改善人类群体表现之条件的研究发现，并讨论了现有CI研究成果可应用于GCR缓解的方式。我们还在这两个新兴领域令人振奋的交叉点上，提出了若干未来研究方向。
英文摘要：
Academic and philanthropic communities have grown increasingly concerned with global catastrophic risks (GCRs), including artificial intelligence safety, pandemics, biosecurity, and nuclear war. Outcomes of many, if not all, risk situations hinge on the performance of human groups, such as whether governments or scientific communities can work effectively. We propose to think about these issues as Collective Intelligence (CI) problems -- of how to process distributed information effectively. CI is a transdisciplinary research area, whose application involves human and animal groups, markets, robotic swarms, collections of neurons, and other distributed systems. In this article, we argue that improving CI in human groups can improve general resilience against a wide variety of risks. We summarize findings from the CI literature on conditions that improve human group performance, and discuss ways existing CI findings may be applied to GCR mitigation. We also suggest several directions for future research at the exciting intersection of these two emerging fields.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
