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A probabilistic approach to uncertainty in pre-modern history

前现代历史中不确定性的概率方法

基本信息

批准号：
AH/P004571/1
负责人：
Myles Lavan
金额：
$ 19.23万
依托单位：
University of St Andrews
依托单位国家：
英国
项目类别：
Fellowship
财政年份：
2017
资助国家：
英国
起止时间：
2017 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=AH%2FP004571%2F1
关键词：
probabilistic approach uncertainty pre modern

项目摘要

Probability is the most powerful conceptual tool that humans have yet developed to understand and manage uncertainty. Scientists, economists and other researchers engaged in forecasting the future regularly use probabilistic techniques to manage the massive uncertainties they face in estimating future values of parameters such as global temperature or human population. Rather than trying to guess the exact value of the variable they are interested in, they tend to think in terms of 'probability distributions' over ranges of possible values. In effect, they draw on the power of modern computing to model all possible futures in order to see how frequently different outcomes occur, rather than trying to model a single 'best estimate' scenario.Probabilistic estimation of uncertain variables was pioneered in the field of nuclear safety in the 1970s and has since become a workhorse of the natural and social sciences. Decades of experience have refined both the theory that underpins it and the methods for implementing it. Probabilistic models are not infallible, but they capitalise on the power of computing and offer a balance of pragmatism and rigour that makes them far superior to the deterministic 'best estimate' models they displaced. The probabilistic approach is now de rigeur in fields as diverse as economics, meteorology and radiation oncology. Yet it has never been developed as a tool of historical analysis, although many quantitative problems in history share deep structural similarities with the problems where probabilistic approaches are deployed in other fields (most importantly: the presence of multiple independent sources of uncertainty). This Fellowship aims to demonstrate their potential as tools of historical analysis and to work towards disseminating the knowledge and skills needed to use them to other pre-modern historians. The potential reward is an improvement in the quality of our knowledge about many obscure historical parameters. In some cases this will involve generating robust estimates for parameters that have seemed unquantifiable given the current evidence base; in others it will involve unsettling established hypotheses that obfuscate the full extent of uncertainty.The Fellowship will produce (i) studies of three long-standing problems in ancient history, to illustrate the method and its potentially wide application, (ii) a workshop to disseminate and refine the methodology in collaboration with other early-career researchers, (iii) a conference to stimulate wider reflection on the methods we use to think about uncertainty in the past and (iv) a book show-casing the state of the art in, and new approaches to, parametric modellin in ancient history.

概率是人类迄今为止用来理解和管理不确定性的最强大的概念工具。从事预测未来的科学家、经济学家和其他研究人员经常使用概率技术来管理他们在估计全球温度或人口等参数的未来值时所面临的巨大不确定性。他们倾向于根据可能值范围内的“概率分布”来思考，而不是试图猜测他们感兴趣的变量的确切值。实际上，他们利用现代计算的力量来模拟所有可能的未来，以了解不同结果发生的频率，而不是试图模拟一个单一的“最佳估计”场景。不确定变量的概率估计是在20世纪70年代在核安全领域开创的，此后成为自然科学和社会科学的主力。几十年的经验已经完善了支撑它的理论和实现它的方法。概率模型并不是绝对可靠的，但它们利用了计算的力量，并提供了实用主义和严谨性的平衡，使它们远远上级它们所取代的确定性“最佳估计”模型。概率方法现在在经济学、气象学和放射肿瘤学等不同领域都是必需的。然而，它从来没有被开发为历史分析的工具，尽管历史上的许多定量问题与概率方法在其他领域中部署的问题有着深刻的结构相似性（最重要的是：存在多个独立的不确定性来源）。该奖学金旨在展示它们作为历史分析工具的潜力，并致力于向其他前现代历史学家传播使用它们所需的知识和技能。潜在的回报是我们对许多模糊的历史参数的知识质量的提高。在某些情况下，这将涉及对在现有证据基础下似乎无法量化的参数进行可靠的估计;在其他情况下，它将涉及扰乱既定的假设，混淆了全部的不确定性。该奖学金将产生（i）研究古代历史上三个长期存在的问题，以说明该方法及其潜在的广泛应用，（ii）与其他早期职业研究人员合作传播和改进方法的研讨会，（iii）促进对我们过去用来思考不确定性的方法进行更广泛反思的会议，以及（iv）展示最新技术和新方法的书籍，参数化建模的最新进展。