从看不见的过去为看不见的未来提供经验教训:深层时间中的风险和复原力

C. M. Barton, J. E. Aura-Tortosa, Oreto García‐Puchol, Julien Riel-Salvatore, Isaac I. Ullah
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引用次数: 0

摘要

风险和抗灾能力这两个相互关联的概念本质上都是着眼于未来的。风险的两个主要方面是有害事件在未来发生的概率以及这种事件造成不同程度损失的概率。复原力同样是指生物、社会或技术系统的组织结构,使其能够抵御未来风险的有害后果。虽然风险和复原力都与未来有关,但对它们的评估却要着眼于过去--过去发生的有害事件、这些事件造成的损失,以及发生这些事件时系统在减轻损失方面的成败。大多数常见的风险和抗灾能力衡量标准都依赖于过去几十年的记录。然而,对于主宰我们星球的耦合社会生态系统的可持续性而言,风险和复原力的长期动态具有同等甚至更大的重要性。历史科学,包括考古学,对于及时评估风险和复原力,规划可持续的未来至关重要。我们面临的挑战是,过去和未来都是不可见的,我们无法直接观察到任何一方。我们介绍了近期考古研究中的一些实例,这些实例提供了对史前风险和复原力的见解,以说明考古学如何通过大规模元分析、数据科学和建模来应对这一挑战。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Lessons for an invisible future from an invisible past: Risk and resilience in deep time
The interrelated concepts of risk and resilience are inherently future-focused. Two main dimensions of risk are the probability that a harmful event will happen in the future and the probability that such an event will cause a varying degree of loss. Resilience likewise refers to the organization of a biological, societal, or technological system such that it can withstand deleterious consequences of future risks. Although both risk and resilience pertain to the future, they are assessed by looking to the past – the past occurrence of harmful events, the losses incurred in these events, and the success or failure of systems to mitigate loss when these events occur. Most common risk and resilience measures rely on records extending a few decades into the past at most. However, much longer-term dynamics of risk and resilience are of equal if not greater importance for the sustainability of coupled socioecological systems which dominate our planet. Historical sciences, including archeology, are critical to assessing risk and resilience in deep time to plan for a sustainable future. The challenge is that both past and future are invisible; we can directly observe neither. We present examples from recent archeological research that provide insights into prehistoric risk and resilience to illustrate how archeology can meet this challenge through large-scale meta-analyses, data science, and modeling.
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