论文标题
集体智能作为降低全球灾难性风险的基础设施
Collective Intelligence as Infrastructure for Reducing Broad Global Catastrophic Risks
论文作者
论文摘要
学术和慈善社区越来越关注全球灾难性风险（GCR），包括人工智能安全，大流行，生物安全和核战争。许多（如果不是全部）风险情况的结果取决于人类群体的表现，例如政府或科学社区是否可以有效地工作。我们建议将这些问题视为集体智能（CI）问题 - 如何有效地处理分布式信息。 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，机器人群，神经元的收集以及其他分布式系统。在本文中，我们认为，在人类群体中改善CI可以提高针对各种风险的一般弹性。我们总结了CI文献中有关改善人类绩效的条件的发现，并讨论现有的CI发现可能应用于缓解GCR。我们还建议在这两个新兴领域的激动人心的交叉点上为未来研究的几个方向。
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.
