不确定性锥是一种强大的工具，根据项目开始时的知识量（或缺乏知识）来描述完成项目所需时间的不确定性。在这篇文章中，我们探讨了Codebots的一组人员进行的一项实验，以确定不确定性锥的应用如何提高项目估计。

作为软件评估系列的一部分，我们探讨了管理期望作为任何软件项目的一部分的重要性。本文将重点讨论不确定性锥，这是一个强大的工具，可以减轻范围界定和估计过程中涉及的风险。它的核心是基于项目开始时的知识量（或缺乏知识）来描述完成项目所需时间的不确定性。它最终解释了项目因未知而失败的风险，以及你试图估计的未来越远，你就越有可能出错的事实。这是一个有趣的悖论，我们想准确地预测未来，但我们不能真正准确，因为这只是一种估计。

如果您还没有，我建议您熟悉我们之前在Codebots进行的一项实验，以了解如何最好地管理软件中的风险。这项工作的动机是我们的软件团队在进行评估时经常过于乐观。

在为自己配备了提出实验的最佳程序后，我们在固定范围估计中发现了一个问题。无论项目的长度如何，每个报价都至少包含80%的工作量变化。由于我们没有应用不确定性锥，我们为客户提供现实估计和管理他们期望的能力受到了严重阻碍。

第一步：了解问题

如上所述，当我们在报价阶段提供固定范围的价格时，我们在估计中遇到了一个问题。（固定范围意味着提供确切的要求，但时间长度灵活）。无论项目的长度如何，由于有大量的变化，我们总是会跳过。

根本原因——我们没有一个不确定的锥。我们没有考虑到这样一个事实，即你试图预测的未来越远，时间方差的增加就越大。这是由于存在许多特定风险，其中最严重的风险是估计不准确、范围变化和最终用户参与。因为这一切都是在一开始就估计的，所以没有机会改变估计来解释这些发现的风险。相反，这些风险的影响在项目的整个生命周期内都会加剧，这意味着项目越长，你就越想预测，因此你就越有可能出错。

第二步：提出一个假设

在消化了这个问题后，我们接下来致力于开发一个假设，以测试如何将不确定性锥准确地应用于我们的固定范围估计。

由此，假设在给定项目长度的情况下，可以使用一个公式来计算不确定性锥的大小。

第三步：计划实验

根据我们的假设，我们开始计划一个实验来测试我们的不确定性锥的命题。为此，我们生成了一个二次函数来模拟我们的圆锥体，其中一个参数表示预测的发育周数。然后，我们可以根据我们试图预测的未来，将这个乘数应用于我们的估计，以解释时间方差。

如果我们将此应用于以前项目的估算数据，那么我们就可以确定带有变化的固定范围价格是否更准确。

步骤4：收集数据

我们从各种项目中收集了数据，包括分配和花费的时间、完成的故事、完成前的时间信息，包括关于项目和遇到的风险的附加说明。

根据这些估计，我们使用不确定性锥计算了每个项目的假设固定范围值。使用这些信息，我们能够检查我们完成初始工作和额外变化所实际花费的时间是否与使用不确定性锥生成的新估计值相当。事实证明，改进后的估计和实际花费的时间非常相似！

第五步：做出决定

最后，数据表明，如果我们将不确定性锥应用于一个固定的范围，使用一个考虑到项目长度的公式，我们最终会得出一个估计，该估计将考虑到项目开发过程中发生的变化。

最终，我们建议开发团队不要试图估计整个项目，而只关注和估计少量的工作。这将有助于减少未知因素的影响，从而减少不确定性锥的大小。这也意味着，随着团队在项目中的工作，他们会对项目有更多的了解，当涉及到估计下一块工作时，他们可以减少不确定性，做出更准确的估计。

在这篇文章中，我们将探讨“不确定性锥”如何影响数字项目，以及如何利用它来改善协作，并构建更好、更快、更便宜的东西。

在复杂的数字项目中，为固定范围估计固定费用的模型会让团队面临潜在的失败。虽然项目利益相关者可能在项目开始时就清楚地了解期望的结果，但没有人可能在一开始就知道每个项目的细节。

相反，通过详细的发现过程或产品路线图参与，项目利益相关者通过合作建立共识，直到前进的道路变得清晰。

用项目管理的话说，在项目谈判和更好地理解确切规范之间的这段时间被称为不确定性锥。如果处理不当，可能会对您的项目造成严重破坏。

一个破碎的过程

承担固定投标固定范围项目的机构会让自己和他们的项目走向失败。当出现超出范围的请求时，无论这个想法有多好，他们都必须推迟，以遵守预算或时间表。

当客户被告知可能提高最终产品质量的新想法将产生额外成本或推迟目标截止日期时，他们会感到沮丧。在这种情况下，客户和代理机构就最佳潜在结果进行合作的自由度是有限的。这是RFP存在问题的众多原因之一。

一次又一次，项目团队以信守合同承诺的名义，在创造力和质量方面妥协。这种限制意味着代理商只能制造出满足客户和最终用户需求的平庸产品。

更糟糕的是，许多客户陷入了一种“一口咬定”的心态。他们不会试图根据收集的数据来随着时间的推移改进产品。这浪费了更快实现目标的宝贵机会。为什么不从一开始就努力争取最好的结果呢？在保持截止日期和预算的同时，是否可以采用协作、创造性的方法？

如果项目利益相关者愿意共同承担一些风险的话。

不确定性的锥

预算和时间表通常基于特定的项目要素。因此，客户编写详尽的RFP，概述尽可能多的项目变量。数字机构——或者任何从事大型项目的服务提供商——通过提前估算来补偿。

这次谈判耗费大量时间和资源。此外，它通常会产生不准确的结果。招标机构的报价通常是实际实施成本的四倍。

不确定性锥概念起源于20世纪50年代工程和施工管理的互联网之前。然而，它在很大程度上适用于今天的数字项目。前提是任何项目在其生命周期中都有一个不断减少的不确定性范围。

An example from Agile in a Nutshell shows how the Cone of Uncertainty impacts estimate variability and project workflow.

不幸的是，在大多数数字项目中，团队都是在最不确定的时期提前完成所有财务和合同谈判的。这是一个重大问题。

虽然详细的规格很有帮助，但提前完成每一项任务、功能和设计要求会限制构建更好产品的自由度。事实上，一旦每个人都卷起袖子投入到这个项目中，伟大的想法就会出现。探索研讨会带来了精彩的新功能请求。用户体验会议会带来在谈判过程中可能没有考虑到的客户见解。但是双方已经同意了他们的条件。

“不”的文化

这种固定出价固定范围的方法建立了一种基于消极性的项目文化。通过不断努力维持范围、预算和法律义务，各机构别无选择，只能温和地拒绝未事先讨论的请求。建立信任和相互尊重的关系将更好地为项目利益相关者服务。

当然，您可以将所有的新想法放在“第二阶段”列表中，以便以后实施。但是，如果其中一些想法能够从根本上改变项目的成功潜力呢？为什么不接受不确定性的锥，承认你并不了解一切呢？双方可以重新确定功能和预算的优先级，然后共同努力，追求对所有人都有更高价值的东西。不幸的是，这不是很多项目的运作方式。

如果你接受在学习新事物时产生的所有伟大想法，你也必须接受一个不断变化的范围。不确定性的锥决定了这一点。否则，你会有一个大象大小的项目，范围膨胀，但盈利能力下降。此外，每当有新的想法出现“对不起”时，人际关系就会变得紧张

用敏捷拥抱不确定性

多年来，我们一直在写关于敏捷方法的文章。当项目有意义时，我们会雇佣他们。它们并不完全适合每个项目，但在范围更大的项目中效果良好。敏捷方法要求通过不断的用户研究和测试，将项目分解为可管理的小块，并内置反馈和协作机制。

敏捷公司权衡可变性，并致力于首先实现最高价值的可交付成果，而不是提前做出承诺。这些高价值的可交付成果首先解决了最大的问题，并在项目后期留下较小的挑战，因为时间和预算可能会更紧。这有助于他们降低风险，并利用不确定性对每个人都有利。

更重要的是，敏捷项目要求业务关系建立在相互信任和承诺提供最佳解决方案的基础上。

具体来说，敏捷要求团队致力于：

• 定期沟通时间、预算、反馈等。
• 持续的动手协作和共享学习
• 在最有意义的时候延长或停止项目的自由

让敏捷在商业中发挥作用

时间构成了你的生命，所以浪费时间实际上是一种缓慢的自杀。

--Jeff Sutherland，《Scrum:在一半时间内完成双倍工作的艺术》一书的作者

Jeff Sutherland在其著作《Scrum:The Art of Doing Twice The Work In Half Time》中，告诉了一家软件公司的故事。（Scrum是一个用于管理产品开发的迭代和增量敏捷软件开发框架。）在萨瑟兰的故事中，软件公司和客户都是赢家。项目详情如下：

软件公司提供了一份项目估算，明确表示他们包含了所有的未知因素，这些未知因素可能会影响时间和可交付成果。

该公司将首先使用敏捷方法来关注最高价值的交付成果。这些可交付成果将以商定的增量提供，以推动计费。

客户可以随时取消项目，只需支付相当于整个项目一小部分的终止费。

在萨瑟兰的故事中，该公司估计该项目可能需要20个月的时间。通过专注于最高价值的可交付成果，该公司仅用了四个月就交付了客户所需的东西，因此客户停止了该项目。客户采用这种方法节省了大量资金。软件团队只完成了他们预期的一小部分工作。该公司收到了20%的项目终止费。这增加了利润率，并打开了团队进行其他项目的时间表。

在下一个项目中拥抱灵活性

采用这些做法的公司将建立长期的客户关系。预算、时间和期望都得到了管理。然而，两党都没有做出最终无法兑现的承诺。客户可以更快地获得更高质量的数字解决方案。用户很高兴。数字机构更好地管理盈利能力。信任得以维持，人际关系得以发展。每个人都赢。

如果你想了解更多关于迈向敏捷过程的信息，请阅读我们的文章《如何从瀑布过渡到敏捷方法》。更好的是，下载下面的敏捷白皮书。

信仰的飞跃

当我们开始构建能取悦客户的SaaS产品时，我们的信心正在飞跃。我们常常甚至不知道目标结果是否可能。投资和构建软件通常是有风险的，原因有几个：

• 我们不知道市场想要什么。
• 我们周围的市场正在发生变化。
• 竞争总是在提高他们的上市时间（TTM），发布有竞争力的产品和服务。

我们必须假设会有错误的项目假设，并且我们用于构建产品的底层开发技术正在不断变化和发展。SaaS之旅中有一件事是明确的——未来总是模糊不清！

SaaS开发充满不确定性的历程在整个行业都可以看到，从Facebook到苹果，从Salesforce到谷歌，大小公司的成功和失败都证明了这一点。谷歌是众多创新的B2C公司之一，它们利用不确定性锥来帮助告知如何进入市场以及是否终止服务。该公司意识到，除了创新，他们还需要迅速减少不确定性。

例如，谷歌笔记本，一个基于浏览器的笔记和信息共享服务，作为谷歌文档的一部分被杀并复活，它有一个名为Keep的移动衍生产品。2011年，谷歌首次尝试建立社交网络的谷歌Buzz在一年多后很快就被扼杀了。这些只是谷歌的几个B2C例子。所有这些都是面临不确定性锥的投资的例子。长期预测成功的结果并锁定产品的细节只会浪费和冒险。

不确定性锥描述了软件项目投资时存在的不确定性和风险。锥体描绘了风险的大小和通过锥体确定的精确程度。我们越是试图预测功能、能力和采用率，我们必须承担的风险和不确定性就越大。对于我们试图定义的待交付产品以及我们将其交付给市场的时间来说，这是正确的。随着时间的推移，企业必须对沿途的计划路径进行调整，以捕捉和接受不断变化的市场需求。

在今天的市场中，我们必须迅速检验我们的假设，并推动创新以具有竞争力。一个敏捷的产品开发生命周期（PDLC）和适当的组织可以帮助我们做到这一点。为了解决圆锥体所代表的挑战，我们必须了解敏捷PDLC能为公司做什么，不能为公司做些什么。

解决圆锥体的不确定性

当我们使用敏捷方法时，我们必须确定产品开发和交付的时间和成本，但我们允许对范围进行调整和更改，以满足固定的日期。团队可以在项目后期延长时间，但承诺的交付日期不会改变。我们也不增加人员，因为Brooks Law告诉我们，在后期软件项目中增加人力资源只会进一步拖延。相反，我们通过频繁部署到市场来加快我们的学习能力，从而减少了不确定性。在整个过程中，发现了成功结果所需的功能集以及应该如何工作。

敏捷允许频繁的迭代，使我们能够通过数据接近市场。部署后，如果我们的系统被设计为受监控的，我们可以捕获丰富的信息，这些信息将有助于为未来的优先级排序、关于功能的新想法以及可能需要对现有功能集进行的修改提供信息。敏捷迫使我们经常进行评估，并因此为我们的业务产生有价值的数据。由此产生的冲刺速度可用于修改未来交付范围预测，包括交付内容和交付时间。数据也将在我们的冲刺过程中产生，这将有助于确定哪些因素可能会减慢我们的速度，最终影响我们的上市时间。由于短期内可以观察和感受到结果，积极的士气将注入tam。

什么不是敏捷，我们必须如何调整？

虽然使用敏捷方法可以帮助解决不确定性，但它并不是所有挑战的答案。敏捷并不能帮助提供一个特性或范围交付的具体日期。相反，我们的工作范围。它也不能仅仅因为我们的团队开始实践TTM而改善TTM。公司的哲学、原则和规则并不是通过敏捷PDLC来定义的。这些由公司来定义。一旦确定，团队就可以在边界内进行创新。这个边界定义的一部分需要从顶部开始。高管们需要描绘出一幅生动的画面，描绘出能够激起情绪并可衡量的预期结果。视野在圆锥体的开口处。高管们为实现成果而定义的可衡量的关键结果允许团队在实现愿景的过程中进行创新和权衡。仅靠敏捷并不能增强团队的能力，也不能帮助团队进行创新。结果和关键结果（OKR）级联到我们的组织中，再加上敏捷PDLC，可以是一个很好的组合，它将使团队有更好的机会进行创新，并实现理想的上市时间。实现OKR框架有助于消除编写代码以达到日期的焦点，并将所需的注意力转向创新和权衡以实现预期结果。

敏捷不能很好地与年度预算周期保持一致。虽然很多时候，股东需要年度视角，但敏捷方法与年度预算相冲突。由于敏捷看到了不断变化的市场需求，因此需要频繁的预算迭代，因为团队可能会要求额外的资金来寻找机会。关键是财务领导者要接受调整预算方法以与敏捷PDLC保持一致的重要性。否则，所产生的冲突可能具有破坏性，并对公司的预期结果造成障碍。

适当地应用敏捷有助于解决锥形问题，减少不确定性，使团队能够实现成果，并最终在全球市场上变得更有竞争力，从而使公司受益。对于那些想要成为世界级的公司来说，敏捷正处于成为赌注的边缘。正如我们在上文中所描述的，注意到预算等不同方法的重要性，这不仅仅是针对软件，而是整个业务。

让我们帮忙

AKF在向组织过渡时帮助了许多各种规模的公司，重新定义了PDLC以符合所需的市场速度，以及SaaS迁移。这三者紧密相连，如果做得好，可以帮助企业更有效地竞争。联系我们免费咨询。我们很乐意提供帮助！

随着时间的推移，项目的细节越来越为人所知，不确定性锥是对估计准确性不断提高的图形描述。

在项目管理中，不确定性锥描述了项目期间最佳情况下不确定性的数量的演变。在项目开始时，对产品或工作结果的了解相对较少，因此估计存在很大的不确定性。随着越来越多的研究和开发工作的进行，人们了解到了更多关于该项目的信息，然后不确定性趋于降低，当所有剩余风险都已终止或转移时，不确定性达到0%。这通常发生在项目结束时，即通过将责任转移到单独的维护小组。

术语“不确定性锥”用于软件开发，其中技术和业务环境变化非常快。然而，这个概念，以不同的名称，是成本工程的一个公认的基本原则。大多数环境变化如此缓慢，以至于在典型项目的持续时间内，它们可以被视为静态的，因此，传统的项目管理方法侧重于通过仔细的分析和规划来充分了解环境。在进行任何重大投资之前，不确定性就已经降低到可以轻松承担风险的水平。在这种环境中，不确定性水平在开始时迅速下降，锥形不太明显。然而，软件业务的波动性很大，随着时间的推移，存在着降低不确定性水平的外部压力。项目必须积极、持续地工作，以降低不确定性水平。

通过研究和从项目中消除可变性来源的决策，缩小了不确定性的范围。这些决定是关于项目的范围、项目中包含的内容和不包含的内容。如果这些决定在项目后期发生变化，那么圆锥体将变宽。

对化工行业工程和施工的原始研究表明，实际最终成本往往比最早的“基本”估计高出100%（或低出50%）。软件行业对不确定性锥的研究表明，在项目生命周期开始时（即在收集需求之前），估计值通常在高端和低端都具有因子4的不确定性。这意味着实际的努力或范围可以是最初估计的4倍或1/4。这种不确定性往往会在项目过程中减少，尽管这种减少并不能保证。

External links:

• http://www.agilenutshell.com/cone_of_uncertainty

• https://www.farreachinc.com/blog/cone-of-uncertainty-in-software-development

本博客页面专门介绍在使用传统和敏捷方法开发和部署复杂系统时，在存在不确定性的情况下用于管理的资源，这些不确定性会带来风险。

让我们从对项目风险管理目的的批判性理解开始。

风险管理对于开发和生产计划至关重要。关于关键项目成本、（技术）性能和进度属性的信息通常是不确定或未知的，直到项目后期。

可以在项目早期发现的风险问题，这些问题可能会在以后影响项目，称为已知未知问题，可以通过良好的风险管理来缓解。

有效风险管理第2版，Edmund Conrow，AIAA，2003

在项目管理和产品开发的各个方面，成功的风险都是由认识（可减少）和解释（不可减少）的不确定性造成的。

在存在这些不确定性的情况下做出任何可信的决定，都需要我们估计这些风险对按时出现的成功概率的影响，并为所需成本提供正确的技术和运营结果。

正如Tim Lister所说。。。

风险管理是针对成年人的项目管理

几乎所有这些论文都可以在谷歌的网页上找到。有些人可能需要会员资格。但任何可信的风险管理者都属于IEEE、AACE、ICEAA、ACM和其他风险管理、项目管理和技术组织。

项目管理的目的是确保对这些活动进行有效管理，以便在最短的时间内带来预期的结果和效益。因此，项目管理是将知识、技能、活动、工具和技术应用于项目，以使项目满足强加给它的要求并实现其目标。风险管理也是项目管理的一个组成部分，应纳入其中。

目录

风险管理从根本原因分析开始。每一个原因都来自于不确定性。不确定性会产生风险，并在项目中以两种形式出现——认知不确定性，它是可减少的，并通过通常由项目支付的回购行动来处理；而Aleory不确定性，是不可减少的并通过保证金-成本、进度和技术保证金来处理。对于每一个可能产生风险的不确定性。

最好在工作开始前发现这些原因。这是一个验尸过程。在问题变成问题后，可以进行尸检以确定原因，但此时，风险已经变成了问题。

• Risk Management Guides and Handbooks (#RMGuide)
• Books on Risk Management in My Library (#RMBooks)
• Risk Management Web Sites (#RMWebs)
• Risk Management Papers(#RMPapers)
  • What is Risk? (#WhatisRisk)
  • Risk-Informed Decision Making (#RIDM)
  • Quantitative and Qualitative Risk Processes (#QuanQualRisk)
  • Aleatory and Epistemic Uncertainties (#AleaEpist)
  • Bayesian Belief Networks (#BBN)
  • Risk Analysis and Assessment (#Analysis)
  • Black Swans and Unknown Unknowns (#BSUnk)
  • Risk Management Techniques (#RMTech)
• Risk Management Thesis (#RMThesis)

Risk Management Guides and Handbooks (#RMGuide)

---

• FAA Flight Safety Analysis Handbook, Version 1.0
• The National Academies of Science Engineering and Medicine Risk Management Content
• IDA's Integrated Risk Assessment and Management Model, James S. Thompson, Institute for Defense Analyses, June 2009.
• Probabilistic Risk Assessment Procedures Guide for NASA Managers and Practitioners, Second Edition, December 2011, Office of Safety and Mission Assurance, NASA Headquarters, August 2002.
• Agency Risk Management Procedural Requirements, NPR 8000.48B, December 2017.
• NASA Risk Management Handbook, NASA/SP-2011-3422, Version 1.0, November 2011.
• Risk Management Guide, DOE G 413.3-7A, Chg 1 2015
• Procedure: Risk Management, SAP-OCE&PMS-413.3B-B-05, 2/16/2015
• Department of Defense Risk, Issue, and Opportunity Management Guide for Defense Acquisition Programs, January 2017.
• Project Risk Management Handbook: A Scaleable Approach, Version 1, June 2012, CalTrans
• Risk-Based Safety and Mission Assurance, Dr. Jesse Leitner, Chief Safety & Mission Assurance Engineering, NASA, 2020
• Risk-Informed Decision Making Handbook, NASA/SP-2010-576

Books on Risk Management in My Library (Hardcopy and Softcopy) (#RMBooks)

---

这些书在我的书架上或我的电子图书馆里，重点是项目、程序、软件开发、成本和技术性能风险对项目成功概率的影响。

A

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• Agile Risk Management and DSDM Pocketbook, Alan Moran, IARM.
• Applied Software Risk Management: A Guide for Software Project Managers, Ravindranath Pandian, Auerbach Publications, 2007.
• A Beginners Guide to Uncertainty of Measurement, Stephanie Bell, National Physics Laboratory, 1999.
• Against the Gods: The Remarkable Story of Risk, Peter L. Bernstein, John Wiley & Sons, 1996.
• Agile Risk Management and Scrum, Alan Moran, Institute for Agile Risk Management, 2014.
• Applied Reliability Engineering and Risk Analysis - Probabilistic Models and Statistical Inference, Editors, Llia B. Frenkel, Alex Karagrigoriou, Anatoly Lisnianski, and Andre Kleyner, Wiley, 2014.
• Analytical Methods for Risk Management - A Systems Engineering Perspective, Paul R. Garvey, CRC Press, 2009.
• Applied Reliability Engineering and Risk Analysis: Probabilistic and Statistical Inference, Edited by LLia B. Frenkel, Alex, Karagrigoriou, Anatoly Lisnianski, and Andre Kleyner, Wiley, 2014.
• Assessment and Control of Software Risks, Capers Jones, Prentice-Hall, 1993.

B

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• Better Decision, Fewer Regrets, Andy Stanley, Zondervan, October 20, 2020
• Beyond Safety Training: Embedded Safety in Professional Skills, Corinne Bieder, Claude Gilbert, Benoît, and Hervé Laroche, Editors, Springer Briefs in Safety Management, Springer Open, 2018.

C

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• Complete Guide to the Basics of Project Risk Management: Improve Your Chances for Success, Dmitry Nizhebetskiy, Project Management Basics, 2017.
• Critical Code, Software Producibility for Defense, Committee for Advancing Software-Intensive Systems Producibility Computer Science and Telecommunications Board Division on Engineering and Physical Sciences, National Research Council of the National Academies, 2010.
• Contingency Guide, 2nd Edition, Risk Engineering Society (RES), 2019.
• Continuous Risk Management Guide, Audrey J. Dorofee, Julie A. Walker, and Christopher J. Alberts, Software Engineering Institute, 1996.
• Critical Uncertainties: The Theory and Practices of System Safety, Matthew Squair, Nonsuch Publishing, June 3, 2022

D

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• Decisions Over Decimals: Striking the Balance Between Intuition and Information 1st Edition, Paul F. Magnone, Christopher J. Frank, and Oded Netzer, Wiley, Ocrober 4, 2022.
• Decision Making Under Uncertainty: Theory and Application, Mykel J. Kocjenderfer, MIT Lincoln Laboratory Series, 2015.
• Decision Analysis for the Professional, 4th Edition Peter McNamee and John Celona, Smart Org, 2008.
• Decision Making Under Uncertainty - Theory and Application, Mykel J. Kochenderfer, MIT Lincoln Laboratory Series, 2015.
• Decision Making under Deep Uncertainty - From Theory to Practice, Vincent A. W. J. Marchau, Warren E. Walker, Pieter J. T. M. Bloemen, and Steve W. Popper, editors, Springer, 2019.
• Decision Making Under Uncertainty: Theory and Application, Mykel J. Kochenderfer, MIT Lincoln Laboratory Series, 2015.
• Decision Quality: Value Creation from Better Business Decisions, Carl Spetzler, Hannah Winter, and Jennifer Meyer, Wiley, 2016.
• Department of Defense Risk, Issue, and Opportunity Management Guide for Defense Acquisition Programs, June 2015, Office of the Deputy Assistant Secretary of Defense for Systems Engineering Washington, D.C.
• Drift into Failure: From Hunting Broken Components to Understanding Complex Systems, Sidney Dekker, Ashgate e-Book, 2011.
• Effective Risk Management: Some Keys to Success, 2nd Edition, Edmund Conrow, AIAA, 2003.

E

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• Engineering a Safer World: Systems Thinking Applied to Safety, Nancy G. Leveson, The MIT Press, 2011.

F

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• Foundations of Risk Management: A Knowledge and Decision-Oriented Perspective, 1^st Edition, Terje Aven, John Wiley & Sons, 2003.
• Foundations of Risk Management, 2^nd Edition, Terje Aven, John Wiley & Sons, 2012.
• Fundamentals of Risk Analysis and Risk Management, Vlasta Molak, CRC Press, 1997.
• FISMA and the Risk Management Framework: The New Practice of Federal Cyber Security, Stephen D. Gantz and Daniel R. Philpott, Elsevier, 2013.
• Failure is Not an Option: Mission Control from Mercury to Apollo 13 and Beyond, Gene Kranz, Simon & Schuster, 2000.
• Foundations of Risk Analysis: A Knowledge and Decision-Oriented Perspective, Terje Aven, John Wiley & Sons, 2003.
• Fundamentals of Risk Management: Understanding, Evaluating and Implementing Effective Risk Management, Paul Hopkins, IRM, 2010
• FISMA and the Risk Management Framework: The New Practice for Federal Cyber Security, Stephen D. Gantz and Daniel R. Philpott, Technical Editor, Elsevier, 2013.

G

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• Guide to Effective Risk Management 3.0, Alex Sidorenko and Elena Demidenko, CreateSpace Independent Publishing Platform, 2017.

H

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• How to Decide: Simple Tools for Making Better Choices, Annie Duke, Portfolio, October 13, 2020.
• Handbook for Integrating Risk Analysis in the Economic Analysis of Projects, Asian Development Bank, 2002.
• How to Live with Uncertainty: Risk Intelligence, Dylan Evans, Free Press, September 29, 2015.
• Handbook of Risk Theory: Epistemology, Decision Theory, Ethics, and Social Implications of Risk, Sabine Roeser, Rafaela Hillerbrand, Per Sandin, Martin Peterson (Editors), Springer, 2011.
• How to Measure Anything - Finding the Value on Intangible in Business, 3rd Edition, Douglas W. Hubbard, John Wiley, 2014.
• How Risky Is It, Really? Why Our Fears Don't Always Match the Facts, David Ropeik, McGraw Hill, 2010.

I

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• Identifying and Managing Project Risk: Essential Tools for Failure-Proofing Your Project, 3^rd Edition, Tom Kendrick, AMACOM, March 25, 2015.
• Integrated Cost and Schedule Control in Project Management, 2^nd Edition, Ursula Kuehn, Management Concepts, 2010.
• Implementing Enterprise Risk Management: Case Studies and Best Practices, John R. S. Fraser, Betty J. Simkins, Kristina Narvaez Editors, Wiley, 2015.
• Integrated Project Risk Management: A System for Adding Value to Your Organization, Gavin Halling, Risk Tools, 2020.
• IT Risk Management Guide: Risk Management Implementation Guide, Gerard Blokdijk, Claire Engle & Jackie Brewster, 2008.
• Chapter 12: Integrated Risk Management System - Key Factor of Management System of the Organization, in Risk Management - Current Issues and Challanges, Emilia Vasile and Ion Croitoru, INTECH 2012.

M

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• Managing Risk: Methods for Software Systems Development, Elaine Hall, Addison Wesley, 1998.
• Managing Project Risk and Uncertainty: A Constructively Simple Approach to Decision Making, Chris Chapman and Stephen Ward, John Wiley & Sons, 2002.
• Managing Risk: Methods for Software Systems Development, Elaine Hall, Software Engineering Institute, Addison Wesley, 1998.
• Managing Complex, High-Risk Projects: A Guide to Basic and Advanced Project Management, Franck Marle and Ludovic‒Alexandre Vidal, Springer‒Verlag, 2016.
• Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference, Dani Gamerman, and Hedibert Freitas Lopes
• Misconceptions of Risk, Terje Aven, John Wiley & Sons, 2010.
• Managing Project Risk and Uncertainty: A Constructively Simple Approach to Decision Making, Chris Chapman and Stephen Ward, John Wiley & Sons, 2002.
• Managing the Unknown: A New Approach to Managing High Uncertainty and Risk in Projects 1^st Edition, Christoph H. Loch, Arnoud DeMeyer, and Michael Pich, Wiley, 2006.
• Modeling Risk: Applying Monte Carlo Simulation, Real Options Analysis, Forecasting, and Optimization, Johnathan Mun, Wiley Finance, 2006.
• Making Essential Choices with Scant Information: Front-End Decision Making in Major Projects, edited by Terry Williams, Kjell Sunnevåg, and Kurt Samset, Palgrave, MacMillan, 2009.
• Managing the Unexpected: Sustained Performance in a Complex World, Third Edition, Karl E. Weick and Kathleen M. Sutcliffe, Wiley, 2015.

N

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• NASA Risk Management Handbook, NASA/SP-2011-3422, Version 1.0, November 2011

O

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• On Making Smart Decisions, Daniel Kahneman, Dan Lovallo, and Oliver Sibony, Harvard Business Review, 2013.

P

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• Project Risk Management Guidelines: Managing Risk in Large Projects and Complex Procurements, Dale Cooper, Stephen Grey, Geoffrey Raymond, and Phil Walker, John Wiley & Sons, 2005.
• Probabilistic Logic Networks: A Comprehensive Framework for Uncertain Inference, Ben Goertzel, Matthew Ikle, Izabela Freire Goertzel, and Ari Jakakka, Springer, 2008
• Project Risk Quantification: A Practitioner’s Guide to Realistic Cost and Schedule Risk Management, John Hollmann, Probabilistic Publishing 2016.
• Perspectives on Thinking, Judging & Decision Making, Wibecke Brun (Editor),‎ Gideon Keren (Editor),‎ Geir Kirkeboen (Editor),‎ Henry Montgomery (Editor), Universitetsforlaget; UK Edition, November 28, 2011.
• Probabilistic Risk Assessment Procedure Guide for NASA Managers and Practitioners, NASA/SP‒2011‒3421, Second Edition, December 2011.
• Probability Methods for Cost Uncertainty Analysis: A Systems Engineering Perspective, Paul Garvey, Marcel Dekker, 2000.
• Probabilistic Risk Assessment and Management for Engineers and Scientist 2^nd Edition, Ernest J. Henley and Hiromitsu Kumamoto, IEEE Press, 2000.
• Project Risk Management: Process, Techniques, and Insights, 2^nd Edition, Chris Chapman and Stephen Ward, John Wiley & Sons, 2003.
• PM Sourcebook Integrated Project Management Sourcebook A Technical Guide to Project Scheduling, Risk and Control, Mario Vanhoucke, Springer.
• Project Risk Management Guidelines, Managing Risk in Large Projects and Complex Procurements, Dale F. Cooper, Stephen Grey, Geoffrey Raymond, and Phil Walker, John Wiley & Sons, 2005.
• Prioritizing Project Risks: A Short Guide to Useful Techniques, edited by Martin Hopkinson, Paul Close, David Hillson, and Stephen Ward, Association for Project Management, 2008.
• Probability for Risk Management, Mathew J. Hassett and Donald G. Stewart, ACTEX Publications, 2006.
• Probabilistic Risk Analysis: Foundations and Methods, Tim Bedford and Roger Cooke, Cambridge University Press, 2009.
• Prioritizing Project Risks, edited by Martin Hopkinson, Paul Close, David Hillson, and Stephen Ward, Association for Project Management, 2008.
• Project Decisions: The Art and Science, Second Edition, Lev Virine, and Michael Trumper, Berrett-Koehler Publishers, October 1, 2007.
• Probability Methods for Cost Uncertainty Analysis: A Systems Engineering Perspective, Paul Garvey, CRC Press, 2000.
• Practical Risk Assessment for Project Management, Stephen Grey,
• Practical Project Risk Management: The ATOM Methodology, David Hillson, Management Concepts Press, 2012.
• Practical Spreadsheet Risk Modeling for Management, Dale Lehman, Huybert Groenendaal, and Greg Nolder, CRC Press, 2012.
• Project Risk Management Guide, Washington State Department of Transportation, February 2018.
• Principles of Risk Analysis: Decision Making Under Uncertainty, Charles Yoe, CRC Press, 2012.
• Project Risk Management: A Practical Implementation Approach, Michael M. Bissonette, PMI Book Service Center, 2016.
• Project Management with Dynamic Scheduling: Baseline Scheduling, Risk Analysis, and Project Control, 2^nd Edition, Mario Vanhoucke, Springer, 2013.
• Project Risk Management Handbook: A Scalable Approach, Version 1, CalTrans, June 2012.
• Principles of Risk Analysis: Making Decisions under Uncertainty, Charles Yoe, CRC Press, 2012.
• Proactive Risk Management in a Dynamic Society, Raddningsverket, Jebs Rasmussen and Inge Svedung, Swedish Rescue Services Agency, 2000.

Q

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• Quantitative Risk Analysis: Guide to Monte Carlo Simulation Modeling, David Vose, John Wiley & Sons, 1996.
• Quantitative Risk Assessment: The Scientific Platform, Terje Aven, Cambridge University Press, 2001

R

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• Reality Charting© Seven Steps to Effective Problem-Solving And Strategies For Personal Success, Dean L. Gano, Apollonian Press, 2011.
• Reliability Engineering and Risk Analysis, Mohammad Modarres, Mark Kaminsky, and Vasiliy Krivtsov, Marcel Dekker, 1999.
• Risk Playbook - Rethinking Risk: Detect, Assess, Respond, and Learn, McChrystal Group, 
• Risk Analysis: A Quantitative Guide, David Vose, John Wiley & Sons 2008.
• Risk Analysis in Project Management, John Raftery, E & FN SPON, an imprint of Routledge, 1994.
• Risk Management and Governance: Concepts, Guidelines, and Applications (Risk, Governance, and Society), Terje Aven, Springer, September 29, 2010
• Risk, Uncertainty and Profit, Frank Knight, Reprints of Economic Classics, 1964.
• Risk Analysis in Theory and Practice, Jean-Paul Chavas, Elsevier Academic Press, 2004.
• Risk Analysis Foundations, Models, and Methods, Louis Anthony Cox, Springer Science+Business Media, 2002.
• Risk Analysis in Engineering: Techniques, Tools, and Trends, Mohammad Modarres, CRC Press, 2006.
• Risk Management in Software Development Projects, John McManus, Routledge, 2003.
• Risk Happens: Managing Risk and Avoiding Failure in Business Projects, Mike Clayton, Marshall Cavendish, 2011.
• Risk Analysis – A Quantitative Guide, Third Edition, David Vose, John Wiley & Sons April 2008.
• Risk Assessment: A Practical Guide to Assessing Operational Risks, edited by Georgi Popov, Bruce K. Lyon, and Bruce Hollcroft, Wiley, 2016.
• Risk Management Concepts and Guidance, Fifth Edition, Carl L. Pritchard, CRC Press, 2015.
• Risk Management ‒ Current Issues and Challenges, edited by Nerija Banaitene, InTech, September 9, 2012.
• Risk Management and Simulation, Aparna Gupta, CRC Press, 2014.
• Risk: A Multidisciplinary Introduction, Claudia Klüppelberg, Daniel Straub, and Isabell M. Welpe, Editors, Springer, 2014.
• Risk Assessment: Theory, Methods, and Applications, Marvin Rausand, Wiley, 2011.
• Risk Analysis - Assessing Uncertainties Beyond Expected Values and Probabilities, Terje Aven, Wiley, 2008.
• Risk Happens!: Managing Risk and Avoiding Failure in Business Projects, Mike Clayton, 2011.
• Risk in Extreme Environments: Preparing, Avoiding Mitigating, and Managing, Vicki Bier, Routledge, 2017
• Risk Modeling, Assessment, and Management, Yacov Y. Haimes (Editor) and Andrew P. Stage (Series Editor) Wiley Series on Systems Engineering and Management, 4th, 2015.

S

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• Software Risk Management, Barry W. Boehm, IEEE Computer Society Press 1989.
• System Reliability Theory, Second Edition, Marvin Rausand and Arnljot Høyland, Wiley Interscience, 2004.
• Strategic Risk Taking: A Framework for Risk Management, Aswath Damodaran, FT Press, August 12, 2007.
• Strategic Project Risk Appraisal and Management, Elaine Harris, Gower, 2009.
• Satisfying Safety Goals with Probabilistic Risk Assessment, Hiromitsu Kumamoto, Springer Series in Reliability Engineering, 2007
• Software Engineering Risk Management: Finding Your Path Through the Jungle, Version 1.0, Dale Karolak, IEEE Computer Society, 1998.
• Software Engineering Risk Management, Dale Karolak, IEEE Computer Society Press, 1996.
• Technical Risk Management, Jack Michaels, Prentice-Hall, 1996.
• Software Engineering Risk Management, Dale Walter Karolak, IEEE Computer Society, 1996.
• Software Engineering Risk Management: Finding Your Path Through the Jungle, Version 1.0, Dale Walter Karolak, IEEE Computer Society, 1998.
• Software Project Survival Guide: How to Be Sure Your First Important Project Isn’t Your Last, Steve McConnell, Microsoft Press, 1998.
• Science and Judgement in Risk Assessment, National Academies Press, 1994
• Stochastic Models in Reliability, Terje Aven and Uwe Jensen, Springer, 1999
• Stochastic Simulation and Monte Carlo Methods: Mathematical Foundations of Stochastic Simulation, Cark Graham and Denis Talay, Springer 2013.
• STPA (System Theoretic Process Analysis Handbook, Nancy Leveson, 2018.
• Science and Judgement in Risk Assessment, National Academy Press, 1994
• Safety, Reliability and Risk Analysis: Theory, Methods, and Applications, Volume 1, S. Martorell, C. Guedes Soares & J. Barnett, Editors, Proceedings of the European Safety and Reliability Conference, 2008.
• Solving for Project Risk Management: Understanding the Critical Role of Uncertainty in Project Management, Christian B. Smart, McGraw-Hill, 2020.

T

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• Time Prediction: Understanding and Avoiding Unrelalism in Project Planning and Everyday Life, Torleif Halkjelsvik and Magne Jørgensen, Simular Springer Briefs on Computing, Volume 5, Springer Open, 2018.
• Thinking in Bets: Making Smarter Decisions When YOu Don't Have All The Facts, Annie Duke, Portfolio, May 7, 2019.
• The Art of Strategic Decision Making: How To Make Tough Decision Quickly, Intelligently, and Safely (Think Smarter, Not Harder), Peter Hollins, Independently published, May 24, 2021.
• The Silver Lining of Project Uncertainties, Thomas G. Lechler, Ting Gao, and Barbara Edington, Project Management Institute, 2013.
• The Economic Foundations of Risk Management: Theory, Practice, and Applications 1^st Edition, Robert Jarrod, World Scientific, 2016.
• Technical Risk Management, Jack Michaels, Prentice-Hall, 1996.
• The Flaw of Averages, Why We Underestimate Risk in the Face of Uncertainty, Sam L. Savage, John Wiley, 2012.
• The Illusion of Risk Control – What Does it Take to Live With Uncertainty?, Gilles Motet Corinne Bieder, Editors, Springer Open, 2016.
• The Failure of Risk Management: Why It’s Broken and How to Fix It, Douglas Hubbard, John Wiley & Sons, 2009.
• Thinking, Fast and Slow, Daniel Kahneman, Farrar, Straus and Giroux, 
• Technical Risk Assessment Analysis, DSTO, Commonwealth of Australia, 2010.

U

---

• Understanding Uncertainty, Dennis V. Lindley, John Wiley & Sons, 2006.
• Uncertainty in Risk Assessment: The Representation and Treatment of Uncertainties by Probabilistic and Non-Probabilistic Methods, 
  Terje Aven, Piero Baraldi, Roger Flage, Enrico Zio, Wiley Feb 2014

W

---

• World-Class Risk Management, Norman Marks, Create Space, 2015.
• Waltzing with Bears: Managing Risk on Software Projects, Tom Demarco and Timothy, Lister, Dorset House, 2003.

Risk Management Web Sites (#RMWebs)

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有许多网站专注于风险管理。这些网站提供了适用于我们复杂系统领域的项目和项目风险信息。

• The Risk Doctor and His Briefings
• Dr. Edmund Conrow, who I've worked with on several NASA Proposal and Execution programs
• David Hulett
• Intaver's Risk Management Blog
• Center for Risk Management of Engineering Systems
• MITRE, Risk Management Approach, and Plan
• Risk Management - Mission and Objectives, INCOSE
• Risk Management - Guide to the Systems Engineering Body of Knowledge

Risk Management Papers (#RMPapers)

---

What is Risk? (#WhatisRisk)

---

• "What is Risk?" Glen Alleman, Thomas Coonce, and Rick Prices, The Measurable News, 2018.01

Risk-Informed Decision Making (#RIDM)

---

所有非微量项目都是在存在产生风险的不确定性的情况下运营的。在存在不确定性和由此产生的风险的情况下做出决策是管理层的职责。以下是我们领域中使用的一些指南，用于就成本、进度和技术性能做出风险知情的决策

• "Risk-Informed Decision Making in the Context of NASA Risk Management," Homayoon Dezfuli, Michael Stamatelatosa, Gaspare Maggio band Christopher Everett, 
• "Quantitative Risk Analysis Support for Decision-Making for New Systems," R. Youngblood and H. Dezfuli, PSA 2019.
• "Known, Unknown, and Unknowable Uncertainties," Carle Chua and Rakesh K. Sarin, The Anderson School at UCLA, May 2000.
• "Uncertainty Characterization in Risk Analysis for Decision-Making Practice," Enrico Zio and Nicola Pedrono, FonCSI, 2012-07.
• "Forecasting the Project Duration Average and Standard Deviation from Deterministic Schedule Information," Pablo Ballesteros-Pérez, Andrés Pastor-Fernández, Alberto Cerezo-Narváez 1, Manuel Otero-Mateo, Jingxiao Zhang, and Mario Vanhoucke, Applied Sciences, 10, 2020.
• "A Process for Risk-Informed Decision-Making," Gareth W. Parry, Michele Laur, and Michael D. Tschiltz, Office of Nuclear Reactor Regulation, U. S. Nuclear Regulatory Commission.
• Informing Program Performance with Programmatic and Technical Risk, This guidebook provides hands-on guidance to program managers for identifying, managing, assessing, and suggesting corrective actions for program performance and programmatic risk impacts,  PARCA, 8 June 2015.
• "Risk-informed decision-making in asset management as a complex adaptive system of systems," Dragan Komljenovic, Georges Abdul-Nour, and Jean-François Boudreau, International Journal Strategic Engineering Asset Management, Vol. 3, No. 3, 2019
• "A Tutorial on Probabilistic Risk Assessment and its Role in Risk-informed Decision Making," Homayoon Dezfuli, Ph.D.
  NASA System Safety Technical Fellow, Eighth Space Systems Risk Management Symposium, April 6, 2010, The Aerospace Corporation, EI Segundo, GA (Dr. Dezfuli is the primary author of NASA's Risk-Informed Decision Making Handbook.
• "Strength of Knowledge Assessment for Risk-Informed Decision Making," Tasneem Bani-Mustafa, Zhiguo Zeng, Enrico Zio, Dominique Vasseur, ESREL 2018, Jun 2018, Trondheim, Norway
• "Contributions to risk-informed decision making," Michael A. Elliot, MIT, June 2010.
• "An Expert Panel Approach to Support Risk-Informed Decision Making," Urho Pulkkinen and Kaisa Simola, VTT Automation, 2000
• "A Proposed Risk-Informed Decision-Making Framework for NASA," Michael Stamatelatos, Homayoon Dezfuli, and George Apostolakis, Eighth International Conference of Probabilistic Safety Assessment and Management (PSAM 8), New Orleans, USA, May 14-18, 2006
• “A Tutorial on Probabilistic Risk Assessment and its Role in Risk-Informed Decision Making,” Homayoon Dezfuli, Eight Space Systems Risk Management Symposium, April 6, 2010.
• “Achieving a Risk-informed Decision-making Environment at NASA: The Emphasis of NASA's Risk Management Policy,” Womayoon Dezbul, Eighth Space Systems Risk Management Symposium, April 7-8, 2010.
• “Risk-Informed Decision-Making Processes: An Overview,” Enrico Zio and Nicola Pedroni, Foundation for an Industrial Safety Culture, 2012‒10.
• "Probabilistic Risk Assessment Procedures Guide for NASA Managers and Practitioners," Version 1.1, August 2002
• “Implementation of Risk-Informed Decision Making at NASA,” Dr. Michael Stamatelatos, NASA Risk Management Conference 2005 RMC-VI, December 7, 2005.
• “Risk-Informed Decision Making: Success and Current Challenges,” George E. Apostolakis, NASA Risk Management Conference 2005 (RMC IV), December 6, 2005.
• Risk-Informed Decision Making Handbook, NASA/SP-2010-576, April 2010.

Quantitative and Qualitative Risk Processes (#QuanQualRisk)

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定量风险管理流程提供了制定风险处理计划所需的信息。定性风险管理提供了风险的相对排名，但没有提供风险如何影响成功概率的信息。

• "Three influential risk foundation papers from the 80s and 90s: Are they still state-of-the-art?," Terje Aven, Reliability Engineering & System Safety
  Volume 193, January 2020, 106680.
• "Monte Carlo: Concepts, Algorithms, and Applications," N. Metropolis, Los Alamos Science, Special Issues, 1987
• "On The Quantitative Definition of Risk," Stanley Kaplan and B. John Garrick, Risk Analysis, Vol 1, No. 1, 1981
• "Modeling Uncertainty in Risk Assessment Using Double Monte Carlo Method," Tazid Ali, Hrishikesh Boruah, Palash Dutta, International Journal of Engineering and Innovative Technology (IJEIT)Volume 1, Issue 4, April 2012.
• "Logic and risk as qualitative and quantitative dimensions of decision-making processes," Tadeusz Galanc, Wiktor Kolwzan, Jerzy Pieronek, Agnieszka Skoronek-Gradziel, Operations Research and Decisions, No. 3, 2016.
• "Fault Tree Handbook with Aerospace Applications," Version 1.1, August 2002.
• “A Quantitative Framework for Managing Project Value, Risk, and Opportunity,” Tyson Browning, IEEE Transactions on Engineering Management, Vol. 61, No. 4, pp. 583-598, 2014.
• “Quantitative Risk Analysis Support to Decision-Making for New Systems,” Robert W Youngblood III, Homayoon Dezfuli, Idaho National Laboratory, INL/CON-18-52121-Revision, May 2019.
• “The Case for Quantitative Project Risk Analysis,” John P. Kindinger, Tenth Annual Conference on Quality in the Space and Defense Industries, March 4-5, 2002.
• “A New Approach for Quantitative Risk Analysis,” Stefan Creemers, Erik Demeulemeester and Stijn Van de Vonder, Department of Decision Science and Information Management (KBI), Katholieke Universiteit, October 5, 2010.
• "A Quantitative Measure For Evaluating Project Uncertainty Under Variation And Risk Effects," Ali Chenarani and E. A. Druzhinin, Engineering, Technology & Applied Science Research Vol. 7, No. 5, 2017, 2083-2088.
• “Advantages and Disadvantages of Quantitative and Qualitative Information Risk Approaches,” Stroie Elena Ramona, Chinese Business Review, Vol. 10, No. 12, pp. 1160-111-, December 2011.
• “Quantitative Risks,” Technical Reports SERC‒2015‒TR‒040‒4, February 24, 2015.
• “The Benefits of Integrated, Quantitative Risk Management,” Barney Roberts, 12^th Annual International Symposium of the International Council of Systems Engineering, Melbourne Australia July 1‒5, 2001.
• “Quantitative Risk ‒ Phases 1 & 2: A013 ‒ Final Technical Report SERC‒2013‒TR‒040‒3,” Walt Bryzik and Gary Witus, November 12, 2013, Stevens Institute of Technology.
• “Combining the Best Attributes of Qualitative and Quantitative Risk Management Tool Support,” Martin S. Feather, Steven L. Cornford, and Timothy W. Larson, Jet Propulsion Laboratory, IEEE Explorer, January 2000.
• “Logic and Risk as Qualitative and Quantitative Dimensions of Decision‒Making,” Tadeusz Galanc, Wiktor Kołwzan, Jerzy Pieronek, And Agnieszka Skowronek-Grądziel, Operations Research and Decisions, No. 3, 2016.
• “Quantitative Cost & Schedule Risk Assessment,” Greg Smith, NASA Marshall Space Flight Center, Office of Strategic Analysis & Communication, Capabilities & Performance Management Office, Capabilities & Development Support Team.
• “Expert judgment models in quantitative risk assessment,” Tony Rosqvist and Risto Tuominen, Proceedings of Values in Decisions on Risk, International Nuclear Information Systems, NEI-SE-308, Kjell Anderson.
• “Uncertainty in Quantitative Risk Analysis – Characterization and Methods of Treatment,” Marcus Abrahamsson, Lund University, Sweden, 2002
• “Quantitative Risk Analysis for Project Management: A Critical Review,” Lionel Galway, WR‒112‒RC, Rand Corporation, February 2004.
• “Quantitative Risk Assessment for Projects Schedules,” Sherif S. Hassanien and Jason B. Skow, Proceedings of the 2012 9^th International Pipeline Conference, IPC2012, September 24‒28, 2012, Calgary, Alberta, Canada.

Aleatory and Epistemic Uncertainties When Managing Project (#AleaEpist)

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不确定性是指现有知识的所有类型的局限性。不确定性与评估人员在评估时的有限知识有关。区分来自固有随机性、自然可变性和随机不确定性（即偶然不确定性）的不确定性和与基于知识的不确定性相关的不确定性，例如由于某种程度的无知或对所研究系统的不完整知识（即知识不确定性）。推理的不确定性不能随着进一步的知识而减少，而认识的不确定性可以随着理解的提高而减少. [Cruz 2021]

项目管理中的不确定性可分为两种类型，即认知不确定性和意向不确定性。认知不确定性源于对参数、现象或过程缺乏了解，而认知不确定性是指随机事件中概率变化引起的不确定性。

解释性不确定性归因于出于实际目的无法预测的结果，因此被视为随机的（例如，硬币翻转的结果），而认识性不确定性则归因于信息或专业知识的缺失。认知不确定性的来源通常使用概率框架来表示，在该框架下，认知不确定性可以由有限数量的具有某些已知分布的随机变量来表示

. [Fox and Ulkumen]

Before reading these resources, here's a clear and concise description from The Department of Management Engineering's definition of aleatory and epistemic uncertainties that create risk in the project and program management domain. There are other voices with different definitions and different messages in different domains. 

• "The Cost of Imperfect Knowledge: How Epistemic Uncertainties Influence Flood Hazard Assessments," Mariano Balbi and David Lallemant, University of Buenos Aires, Earth Observatory of Singapore
• "Epistemic uncertainties and natural hazard risk assessment – Part 2: A Review of Different Natural Hazard Areas?" Keith J. Beven et al., Nat. Hazards Earth Syst. Sci., 18, 2741-2768, 2018.
• "Epistemic uncertainties and natural hazard risk assessment – Part 1: What should constitute good practice?" Keith J. Beven et al., Nat. Hazards Earth Syst. Sci., 18, 2769–2783, 2018
• "Understanding Aleatory and Epistemic Parameter Uncertainty in Statistical Models," N. W. Porter and V. A. Mousseau, Sandia National Laboratory, 2020.
• "Stochastic analysis and validation under aleatory and epistemic uncertainties," Austin K. McKeand, Recep M. Gorguluarslan, and Seung-Kyum, Reliability Engineering & System Safety, Volume 205, January 2021, 107258
• "Computing with Epistemic Uncertainty," Lewis Warren, National Security and ISR Division, Defense Science and Technology Organization, Australian Government
• "Uncertainty Estimation Cheat Sheet for Probabilistic Risk Assessment," Paul Britton, Mohannas Al Hassan, and Robert Ring, NASA Technical Reports Server, 2017-10-18
• "De Finetti on Uncertainty," A. Feduzi, J. Runde and C. Zappia, Cambridge Journal of Econimics 38 (1) pp. 1-21, 2014.
• "Robust analysis of uncertainty in Scientific Assessment," Ivette Raices Cruz, Lund University, 2021.
• "Epistemic versus Aleatory Judgment Under Uncertainty," 
• "Literature Review of Methods for Representing Uncertainty," Enrico Zio, FONCSI, 2013.
• "Alternative representations of epistemic uncertainty," J. C. Helton, Reliability Engineering & Safety Systems, 85, 2004.
• "Representation of Analysis Results Involving Aleatory and Epistemic Uncertainty," J. C. Helton, J. D. Johnson, W. L. Oberkampf, C.J. Sallaberry, Sandia Report, SAND2008-4370, August 2008.
• "Numerical approach for quantification of epistemic uncertainty," John Jakeman, Michael Eldred, and Dongbin Xiu, Journal of Computational Physics, 229, 2010
• "Uncertainty Assessment in High-Risk Environments Using Probability, Evidence Theory and Expert Judgment Elicitation," Stella Barberis Bondi, Old Dominion University, 1 April 2007.
• "Mitigating versus Managing Epistemic and Aleatory Uncertainty," Mark D. Packard and Brent B. Clark, Academy of Management Review, October 2020.
• Matthew Squire's Critical Uncertainty Blog speaks to Aleatory and Epistemic Uncertainty that creates risk.
• "Epistemic Uncertainty," a collection of engineering topics from Science Direct
• "Uncertainty, Ignorance, and Decision-Making: Looking Through the Lens of Modelling the Covid-19 Pandemic," Ting Xu, Amicus Curiae, Series 2, Vol. 3, No. 1, 10-32, Autumn, 2021
• "Reflections on Epistemic-Ontological Alignmentin Theorizing Process: the Case of RBV," Júlio César da Costa Júnior, Leandro da Silva Nascimento, Taciana de Barros Jerônimo, Jackeline Amantino de Andrade, and Marcos André Mendes Primo, Philosophy of Management, 
• "On the Aggregation and Extrapolation of Uncertainty from Component to System Level Models," Angel Urbina, Richard G. Hills, and Adam C. Hetzler, Sandia National Laboratories, 2013.
• "Quantification of Aleatoric and Epistemic Uncertainty in Computational Models of Complex Systems," Angle Urbina and Sankaran Mahadevan, Proceedings of the IMAC-XXVII, Feb 1-4, 2010.
• "Propositional Epistemic Luck, Epistemic Risk, and Epistemic Justification," Patrick Bondy and Duncan Pritchard, Synthese volume 195, pages3811–3820, 2018.
• "On Dual Nature of Uncertainty - Cues from Natural Language," Craig R. Fix and Gülden Ülkümen, UCLA Anderson School and Department of Psychology
• "Modeling and Management of Epistemic Uncertainty for Multidisciplinary System Analysis and Design," A.K.M. Kais Zaman, Vanderbilt University, August 2010.
• "Dicing with the Unknown," Tony O'Hagan, Significance, Royal Statistical Society, September 2004.
• "An Integrated and Efficient Numerical Framework for Uncertainty Quantification: Application to the NASA Langley Multidisciplinary Uncertainty Quantification Challenge," Edorado Patelli, Matteo Broggi, and Marco de Angelis, American Institute of Aeronautics and Astronautics, 2014.
• "A Stochastic Risk Analysis Through Monte Carlo Simulation Applied to the Construction Phase of a 600MW Gas Turbine Plant," Fahimeh Allahi, Lucia Cassettri, and Marco Mosca, Proceedings on Modeling and Applied Simulation, 2017.
• "A Unifying Framework to Uncertainty Quantification of Polynomial Systems Subject to Aleatory and Epistemic Uncertainty," Luis G. Crespo, Daniel P. Giesy, and Sean P. Kenny, in Reliable Computing, 17(2), 2012
• "A novel method for importance measurement analysis in the presence of epistemic and aleatory uncertainties," Ren Bo, Lu Zhenzhou, and Zhou Changcong, Chinese Journal of Aeronautics, 2014.
• "A global nonprobabilistic reliability sensitivity analysis in the mixed aleatory- epistemic uncertain structures," Yishang Zhang, Yongshou, Xufeng Yang, and Zhufeng Yue, Journal Aerospace Engineering, 2014, Vol. 228 (10), 1802-1814
• "A Gentle Introduction to Uncertainty Quantification," Ilias Bilionis, Assistant Professor, Predictive Science Laboratory, School of Mechanical Engineering, Purdue University
• "Approaches to Mitigate the Impact of Uncertainty in Development Processes," Marel J. Chalupnik, David C. Wynn, and P. John Clarkson, International Conference on Engineering Design, ICED' 09, 24-27 August 2009.
• "Aleatory and epistemic uncertainties analysis based on non-probabilistic reliability and its kriging solution," Guijie Li, Zhenzhou Lu, Luyi Li, and Bo Ren, Applied Mathematical Modeling, 40, (2016) 5703-5716
• "Impact Probability Under Aleatory And Epistemic Uncertainties," Chiara Tardioli, Davide Farnocchia, Massimiliano Vasile, and Steve R. Chesley, November 30,2020.
• “Distinguishing Two Dimensions of Uncertainty,” Craig Fox and Gülden Ülkumen, in Perspectives of Thinking, Judging, and Decision Making, Editors, Wibecke Brun,‎ Gideon Keren,‎ Geir Kirkeboen,‎ Henry Montgomery, Universitetsforlaget; UK ed. Edition, November 28, 2011.
• "On the project risk baseline: Integrating aleatory uncertainty into project scheduling," F. Acebes, D. Poza, J. M. González-Varona, J. Pajares, and A. López-Paredes, Computers & Industrial Engineering 160 (2021).
• "Epistemic uncertainty quantification tutorial," Thomas Paez, Proceedings of the IMAC-XXVII, February 9-12, 2009.
• "Testing for ontological errors in probabilistic forecasting models of natural systems," Warner Marzocchia and Thomas H. Jordan, PNAS August 19.2014, Vol. 111, No. 33
• "Characterizing ambiguity attitudes using model uncertainty," Loïc Berger and Valentina Bosetti, Journal of Economic Behavior and Organization, Elsevier, 2020, 180, pp.621-637.
• "Modeling of Epistemic Uncertainty in Reliability Analysis of Structures Using a Robust Genetic Algorithm," M. Bagheri, M. Miri, and N. Shabakhty, Iranian Journal of Fuzzy Systems, Vol. 12, No. 2, pp. 23-40, 2015.
• "Ambiguity Aversion and Epistemic Uncertainty," Craig R. Fox, Michael Goedde-Menke, and David Tannenbaum, September 8, 2021.
• "A critical discussion and practical recommendations on some issues relevant to the non-probabilistic treatment of uncertainty in engineering risk assessment," Nicola Pedroni, Enrico Zio, Alberto Pasanisi, Mathieu Couplet, Risk Analysis, Wiley, 2017, 37 (7), pp.1315 - 1340.
• "This Is What We Don't Know”: Treating Epistemic Uncertainty in Bayesian Networks for Risk Assessment," Ullrika Sahlin, Inari Helle, and Dmytro Perepolkin, Integrated Environmental Assessment and Management, Volume 17, Number 1—pp. 221–232, 2 November 2020.
• "Quantification of epistemic and aleatory uncertainties in level-1 probabilistic safety assessment studies," K. Durga Raoa, H.S. Kushwahaa, A.K. Vermab, and A. Srividyab, Engineering, Computer Science, 2007.
• "Inclusion of Aleatory and Epistemic Uncertainty in Design Optimization," Sirisha Rangavajhala, Chen Liang, and Sankaran Mahadevan, 10^th World Congress on Structural and Multidisciplinary Optimization, May 19 -24, 2013, Orlando, Florida, USA
• "Epistemic Uncertainty Propagation in Reliability Assessment of Complex Systems," K. Durga Rao, H.S. Kushwaha, A.K. Verma and A. Srividya International Journal of Performability Engineering, Vol. 3, No. 4, October 2007, pp. 71 – 84 
• "Reasoning About Confidence and Uncertainty in Assurance Cases: A Survey," Lian Duan, Sanjai Rayadurgam, Mats P.E. Heimdahl, Anaheed Ayoub, Oleg Sokolsky, Insup Lee, International Symposium on Foundations of Health Informatics Engineering and Systems International Workshop on Software Engineering in Health Care, July 2017, Lecture Notes in Computer Science.
• "Some Statistical Procedures to Refine Estimates of Uncertainty when Sparse Data are Available for Model Validation and Calibration," Vicente Romero, Brian Rutherford, Justin Newcomer Sandia National Laboratories, Albuquerque, NM
• "Uncertainty Classification for the Design and Development of Complex Systems," Daniel P. Thunnissen, California Institute of Technology, 2003.
• "Testing for ontological errors in probabilistic forecasting models of natural systems," Warner Marzocchi and Thomas H. Jordan, PNAS, vol. 111, no. 33, August 19, 2014. 
• "Robustness of RISMC Insights under Alternative Aleatory/Epistemic Uncertainty Classifications," Stephen D. Unwin Paul W. Eslinger Kenneth I. Johnson, PNNL-21767, U.S. Department of Energy, September 2012
• "Epistemic uncertainty quantification tutorial," Laura P. Swiler, Thomas L. Paez, Randall L. Mayes, Proceedings of the IMAC-XXVII, February 9-12, 2009 Orlando, Florida USA.
• "Reliable classification: Learning classifiers that distinguish aleatoric and epistemic uncertainty," Oliver Hirsch, Information Sciences 255 (2014) 16–29.
• "Deterministic Neural Networks with Inductive Biases Capture Epistemic and Aleatoric Uncertainty," Jishnu Mukhoti, Andreas Kirsch, Joost van Amersfoort, Philip H.S. Torr, and Yarin Gal, preprint, arXiv:2102.11582v2
• "A Quantitative, Model-Driven Approach To Technology Selection And Development Through Epistemic Uncertainty Reduction," Katherine N. Gatian, School of Aerospace Engineering, Georgia Institute of Technology May 2015.
• "Epistemic and aleatory uncertainty in decisions from experience," Joel Holwerda and Ben Newell, Proceedings of the Annual Meeting of the Cognitive Science Society, 43(43), 2021.
• "Communicating Uncertainty about facts, numbers, and science," Anne Marthe van der Bls, Sander van der Linden, Alexandra L. J. Freeman, James Mitchell, Ana B. Glavao, Lisa Zaval, and David J. Spiegelhalter, Royce Society Open Science, 2009.
• "Dealing with Uncertainty: A Survey of Theories and Practice,"
• "A Review on Methods to Handle Uncertainty," Sonika Malik and Sarika Jain, ICCVBIC 2018, Coimbatore, India, September 2020.
• "Combined Approach to Propagate Aleatory and Epistemic Uncertainty in Risk Assessment," Palash Dutta, International Journals of Mathematics and Computer Applications Research, Vol. 3, Issue 5, Dec 2013, pp. 29-36
• "This Is What We Don't Know”: Treating Epistemic Uncertainty in Bayesian Networks for Risk Assessment," Ullrika Sahlin, Inari Helle, and Dmytro Perepolkin, Integrated Environmental Assessment and Management, Volume 17, Number 1, pp. 221-232, May 2020.
• “Risk-Informed Decision-Making In The Presence Of Epistemic Uncertainty,” Didier Dubois and Dominique Guyonnet, International Journal of General Systems, 40, 2, pp. 145-167, 2011.
• "Communicating uncertainty about facts, numbers, and science," Anne Marthe van der Bles, Sander van der Linden1, Alexandra L. J. Freeman, and James Mitchell, Royal Society Publishing, 11 April 2015.
• "Model Validation under Both Aleatory and Epistemic Uncertainty," William L. Oberkampf and Scott Ferson, NATO/RTO Applied Vehicle Panel, Symposium on Computational Uncertainty in Military Vehicle Design, Athens, Greece, 3-6 December 2007.
• "Three Layers of Uncertainty: an Experiment," Ilke Aydogan, Loϊc Berger, Valentina Bosetti, Ning Liu, Working Paper, June 2018. 
• “Epistemic Uncertainty Analysis: An Approach Using Expert Judgment and Evidential Credibility,” Patrick Hester, International Journal of Quality, Statistics, and Reliability, Volume 2012, Article ID 617481.
• “Importance measure analysis with epistemic uncertainty and its moving least-squares solution,” Pan Wang, Zhenzhou Lu, and Zhangchun Tang, Computers and Mathematics with Applications, 66 (103), pp. 460‒471.
• “Two Dimensions of Subjective Uncertainty: Clues from Natural Language,” Gülden Ülkümen, Craig Fox, and Bertram Malle, University of Southern California, Marshall School of Business.
• "Uncertainties in SPRA: Sources and Treatment," Annie Kammerer, U.S. Nuclear Regulatory Commission, 
• "Judgment Extremity and Accuracy Under Epistemic vs. Aleatory Uncertainty," David Tannenbaum, Craig R. Fox, Gülden Ülkümen, Management Science, Vol. 63, No. 2 2017
• "A Commentary of Model Uncertainty," in Proceedings of Workshop on Model Uncertainty: Its Characterization and Quantification, A. Mosleh, N. Shu, C. Smidts, Editors, October 20-22, 1993.
• "Uncertainty and Uncertainties, Lecture 3-2, Probabilistic Risk Assessment," U.S. Nuclear Regulatory Commission.
• "The Concept of Ignorance in a Risk Assessment and Risk Management Context," Terje Aven and Riana Steen, Reliability Engineering & Safety Systems, November 2010
• "Different Methods are Needed to Propagate Ignorance and Variability," Scott Ferson and Lev. R. Ginzberg, Reliability Engineering & System Safety, Volume 54, Issues 1-2, November-December 1996
• "Characterizing Epistemic Uncertainty for Launch Vehicle Designs," Steven D. Novack, Jim Ridgers, Frank Hark, and Mohammad Al Hassan, 
• "Uncertainty - How Human Factors Fail Complex Projects," Lavagnon Ika and Jovica Riznic, PMI National Capital Project Management Symposium, October 14-16, 2014. 
• "Treatment of Epistemic and Aleatory Uncertainties with OpenCOSSAN and COSSAN-X," Matteo Brogg, Institut für Risiko und Zuverlässigkeit
  Leibniz Universität Hannover.
• "Aleatory vs. Epistemic Uncertainties: Principles and Challenges," Nathan Siu and Julie Marble, Office of Nuclear Regulatory Research, U.S. Nuclear Regulatory Commission, Presentation at ASME PVP Conference, Baltimore, MD, July 20, 2011
• “On the project risk baseline: Integrating aleatory uncertainty into project scheduling,” F. Acebes, D. Poza, J.M. Gonzalez-Varona, J. Pajares, A. Lopez-Paredes, Computers & Industrial Engineering, 160, 2021,
• “Aleatory and Epistemic Uncertainties,” Shahram Pezeshk, Ph.D., P.E., The University of Memphis
• “Judgment Extremity and Accuracy Under Epistemic vs. Aleatory Uncertainty," David Tannenbaum, Craig R. Fox, and Gülden Ülkümen, Management Science, Vol. 63, No. 2, pp. 497-518, February 2017
• “A Model-Based Reliability Metric Considering Aleatory and Epistemic Uncertainty,” Zhiguo Zeng, Rui Kang, Meilin Wen, and Enrico Zio, IEEE Access, 9 Nov 2017.
• “A Hybrid Method to Deal with Aleatory and Epistemic Uncertainty in Risk Assessment,” Palash Dutta and Tazid Ali, International Journal of Computer Applications Volume 42, No. 11, March 2012.
• “An Approach to Deal with Aleatory and Epistemic Uncertainty within the Same Framework: Case Study in Risk Assessment,” Palash Dutta, International Journal of Computer Applications, (0975‒8887), Volume 80 ‒ No. 12, October 2013.
• “Approximate Propagation of both Epistemic and Aleatory Uncertainty through Dynamic Systems,” Gabriel Terejanu, Puneet Singla, Tarunraj Singh, and Peter D. Scott
• “Aleatory and Epistemic Uncertainty Quantification for Engineering Applications,” L. P. Swiler and, A. A. Giunta, Sandia Technical Report, SAND2007-2670C
• “Separation of Aleatory and Epistemic Uncertainty in Probabilistic Model Validation,” Joshua Mullins, You Ling, Sankaran Mahadevan, Lin Sun, Alejandro Strachan, Reliability Engineering and System Safety, 147, 2016, pp. 49‒59.
• “Judgment Extremity and Accuracy Under Epistemic vs. Aleatory Uncertainty,” David Tannenbaum, Craig R. Fox, and Gülden Ülkümen, Management Science, Vol. 63, No. 2, February 2017, pp. 497‒518.
• “Extended Component Importance Measures Considering Aleatory and Epistemic Uncertainties,” Mohamed Sallak, Walter Schön, and Felipe Aguirre. IEEE Transactions on Reliability, 2013.
• "Epistemic versus Aleatory Judgment Under Uncertainty," Gülden Ülkümen, University of Southern California, Marshall School of Business.
• “Design Optimization under Aleatory and Epistemic Uncertainties,” Sirisha Rangavajhala, Chen Liang and Sankaran Mahadevan, 12^th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference and 14^th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, 2012.
• "Inclusion of Aleatory and Epistemic Uncertainty in Design Optimization" Sirisha Rangavajhala, Chen Liang Chen, and Sankaran Mahadevan, 10th World Congress on Structural and Multidisciplinary Optimization.
• “Aleatory or epistemic? Does it matter?,” Armen Der Kiureghian, Special Workshop on Risk Acceptance and Risk Communication, March 26‒27, 2007, Stanford University.
• “Special Workshop on Risk Acceptance and Risk Communication, Aleatory, or Epistemic? Does it matter?” Armen Der Kiureghian, March 26‒27, 2007, Stanford University.
• “Aleatory Variability and Epistemic Uncertainty,” from Seismic Hazard Assessment: Problems With Current Practice And Future Developments, Norman Abrahamson, Pacific Gas & Electric Company, 245 Market Street, San Francisco, CA, 94105, USA, in First European Conference on Earthquake Engineering and Seismology (a joint event of the 13^th ECEE & 30^th General Assembly of the ESC) Geneva, Switzerland, 3‒8 September 2006.
• “Representation of Analysis Results Involving Aleatory and Epistemic Uncertainty,” Jon. C. Helton, Jay. D. Johnson, William. L. Oberkampf, Cedric. J. Sallaberry, Sandia Report SAND2008‒4379, 2008.
• "The Role of Epistemic Uncertainty in Risk Analysis," D. Dubois, IRIT-CNRS, Conference: Scalable Uncertainty Management - 4th International Conference, SUM 2010, Toulouse, France, September 27-29, 2010. 
• “Epistemic Uncertainty Quantification Tutorial,” Laura P. Swiler, Thomas L. Paez, and All L. Mayes, Sandia National Laboratory
• “Sensitivity Analysis Methods for Systems with Epistemic Uncertainties,” Lawrence Hale, Mayuresk Patil, and Christopher Roy, American Institute of Aeronautics and Astronautics, January 2015.
• “A Guide to Deal with Uncertainties in Software Project Management," Marcelo Marinho, Suzana Sampaio, Telma Lima, and Hermano de Moura, International Journal of Computer Science & Information Technology (IJCSIT) Vol 6, No 5, October 2014.
• “Quantifying Uncertainty in Early Lifecycle Cost Estimation (QUELCE),” Robert Ferguson, Dennis Goldenson, James McCurley, Robert Stoddard, David Zubrow, and Debra Anderson, Technical Report, CMU/SEI‒2011‒TR‒026 ESC‒TR‒2011‒026.

Bayesian Belief Networks (#BBN)

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A Bayesian network is a probabilistic graphical model that represents a set of variables and their conditional dependencies via a directed acyclic graph. The combination of probabilities and the impacts identifies the most significant risks.

• "Comparison of Evidence Theory and Bayesian Theory for Uncertainty Modeling," Prabhu Soundappan, Efstratios Nikolaidis, R. T. Haftka, Ramana Grandhi, and Robert Canfield, 
• "Disentangling Classical and Bayesian Approaches to Uncertainty Analysis," Robin Willink and Rod White, 
• "A Bayesian Belief Networks Approach to Risk Control in Construction Projects," Ibsen Chivatá Cárdenas, Saad SH Al-jibouri & Johannes IM Halman, University of the Netherlands
• “A Bayesian Belief Network Model And Tool To Evaluate Risk And Impact In Software Development Projects,” Anthony Kwok Tai Hui and Dar Biau Liu, IEEE, 2004.
• “Project Scheduling: Improved Approach to Incorporate Uncertainty Using Bayesian Networks,” Vahid Khodakarami, Norman Fenton, and Martin Neil, Project Management Journal, June 2007.
• “Large engineering project risk management using a Bayesian belief network,” Eunchang Lee, Yongtae Park, and Jong Gye Shin, Expert Systems and With Applications, 36, pp. 5880‒5887, 2009.
• “Software Delivery Risk Management: Application of Bayesian Networks in Agile Software Development,” Ieva Ancveire, Ilze Gailite, Made Gailite, and Janis Grabis, Information Technology, and Management Science, 2015/18.
• “Software project risk analysis using Bayesian networks with causality constraints,” Yong Hu, Xiangzhou Zhang, E. W. T. Ngai, Ruichu Cai, and Mei Liu, Decision Support Systems 56 (2013) 439–449.

Risk Analysis and Assessment (#Analysis)

一旦确定了风险，就必须对项目成功的概率进行分析和评估。该过程需要对风险来源——不确定性——进行建模，以确定产生风险的随机过程的发生概率或概率分布函数，对成功概率的影响，以及风险处理策略的预防或纠正措施的有效性。

• "The Future of Risk Management in an Uncertain World," Colin Coulson-Thomas, The Risk Management Society.
• "A method for project portfolio risk assessment considering risk interdependencies – a network perspective," Camilo Mican, Gabriela Fermandes, and Madalen Araújo, International Conference on ENTERprise Information Systems / ProjMAN - International Conference on Project MANagement / HCist - International Conference on Health
  and Social Care Information Systems and Technologies 2021
• "Value creation through project risk management," Pelle Willumsen, Josef Oehment, Verena Stingl, and Joana Geraldi, International Journal of Project Management, 15 Jan 2019
• "Transforming project risk management into project uncertainty management." Stephen Ward, Chris Chapman, International Journal of Project Management 21 (2003) 97–105
• "The Effect of Project Risk Management in Project Success: A Multidimensional Analysis," Fabrício Garcia Imbrizi and Emerson Antonio Maccari, XLIII Encontro da ANPAD - EnANPAD 2019.
• "A Handbook of Cost Risk Analysis Methods," Philip M. LuriePhilip M. LurieMatthew S. GoldbergMitchell S. Robinson, April 1993, Institute for Defense Analyses.
• "The Problems Behind the Problems:" Systems Engineering and Program Management Risk Factors in Acquisition Programs," Joshua McConnell, Jason Sickler, and Trent Yang, Institute for Defense Analyses, 
• “Mapping Risk Analysis Methods of Product Development Process,” Stanko Škec, Mario Štorga, and Dorian Marjanović, 3rd International Conference on Integration of Design, Engineering & Management for Innovation, A.A. Fernandes, R.M. Natal Jorge, L. Patrício, A. Medeiros (Eds)Porto, Portugal, 4-6th September 2013.
• "Standard Project Risk Analysis Approach,” Tena Žužek, Lidija Rihar, Tomaž Berlec, and Janez Kušar, Business and Systems Research, Vol. 11, No. 2, 2020.
• “Models and model uncertainty in the context of risk analysis,” T. Nilsen and Terje Aven, Reliability Engineering and System Safety, March 2003.
• “Methodology for risk analysis of projects,” Maria Magdalena Roșu, Rodica Rohan, and Elena - Cristina Juganaru, MATEC Web of Conferences, 112, 09012, 2017.
• “Risk Analysis and Management A Vital Key to Effective Project Management,” N. Lavanya and T. Malarvizhi, PMI Global Congress 2008.
• "A Review of Activity Time Distributions in Risk Analysis," Enobong Francis Udoumoh and Daniel W. Ebong, American Journal of Operations Research, pp. 356-371, 2017.
• "A Review of Activity Time Distributions in Risk Analysis," Enobong Francis Udoumoh and Daniel W. Ebong, American Journal of Operations Research, pp. 356-371, 2017.
• “Risk Assessment Forum White Paper: Probabilistic Risk Assessment Methods and Case Studies,” EPA/100/R-14/004, July 2014, www.epa.gov/raf
  2018.
• “Integrated Cost-Schedule Risk Analysis," Dr. David T. Hulett and Michael R. Nosbisch, Cost Engineering, November/December 2012.
• “Journey Map to a More Mature Schedule Risk Analysis Process,” David Hulett, Long International, Littleton, Colorado, 2018.
• “Risk Analysis and Management,” Lester B. Lave, The Science of the Total Environment, 99, pp. 235‒242, 1990.
• “Statistical Dependence In Risk Analysis for Project Networks using Monte Carlo Methods,” J. R. van Dorp and M. R. Duffey, International Journal of Production Economics, 55, pp. 17‒29, 1999.
• “Risk as Analysis and Risk as Feelings: Some Thoughts about Affect, Reason, Risk, and Rationality,” Paul Slovic, Melissa L. Finucane, Ellen Peters, and Donald MacGregor, Risk Analysis, Vol.24, No. 2, 2004.
• “Evaluation of the Risk Analysis and Cost Management (RACM) Model,” Matthew S. Goldberg, Institute for Defense Analyses, August 1998.
• “Risk Analysis & Estimating Uncertainty … and what this has to do with the price of milk in McLean,” Phil Beenhouwer, The Society of Cost Estimating & Analysis (SCEA), May 17, 2006
• “Strategic Risk Analysis ‒ A Field Version,” Jan Emblemsvåg and Lars Endre Kjølstad, Management Decision, 40/9, pp. 842‒852, 2002.
• “Advanced Risk Analysis for High‒Performing Organization,” Christopher Alberts and Audrey Dorfee, Software Engineering Institute, 2006
• “Quantifying uncertainty under a predictive, epistemic approach to risk analysis,” S. Apeland, T. Aven, and T. Nilsen, Reliability Engineering & System Safety, Volume 5, Number 1, January 2002, pp. 93‒102.
• “Schedule Risk Analysis: Why It Is Important and How to Do It,” Stephen A. Book, Business Cases, and Acquisition Strategies, Ground Systems Architectures Workshop (GSAW 2002), 13‒15 March 2002.
• “Dealing with project complexity by matrix-based propagation modeling for project risk analysis,” Chao Fang and Franck Marle,” Journal of Engineering Design, Taylor & Francis, Volume 24, Issue 4, 2013.
• “Expert Judgement, Meta-Analysis and Participatory Risk Analysis,” Simon French,
• “Expert Judgement in Risk Analysis,” Stephen C. Hora, Non-Published Research Report, Homeland Security Center, Paper 120
• “Risk Analysis of project time and cost through Monte Carlo Method ‒ A Case Study at Scania CV AB,” Catharina Danielson and Hamid Khan, Degree Projects, in Project Management of Operational Development, Stockholm, Sweden, May 2015.
• “Using Probabilistic Methods to Enhance the Role of Risk Analysis in Decision‒Making with Case Study Examples,” prepared by Risk Assessment Forum, PRA Technical Panel Working Groups, EPA/100/R‒09/001
• “Project Risk Analysis,” Jørn Vatn, Norwegian University of Science and Technology, 12 November 2013.
• “Natural Variation, Limited Knowledge, and the Nature of Uncertainty in Risk Analysis,” Gregory B. Baecher and John T. Christian, Risk-Based Decision‒Making in Water Resources IX’, Santa Barbara, CA (2000): 1‒16.
• “Final Report Briefing Analytic Methods for Cost and Schedule Risk Analysis,” Raymond Covert, March 4, 2013.
• “Risk assessment and risk management: Review of recent advances on their foundation,” Terje Aven, European Journal of Operational Research, 235, 2013, pp. 1‒13.
• “Tools for analyzing epistemic uncertainties in probabilistic risk analysis,” Janne Laitonom (Project Manager), EURPRA002 Final Report version 4, Mat‒2.4177 Seminar on Case Studies in Operation Research, Technical Research Centre of Finland.
• Enhanced Scenario-Based Method for Cost Risk Analysis: Theory, Application, and Implementation,” Brian Flynn, Journal of Cost Analysis and Parametrics, 11 December 2012.
• “Analytical Method for Probabilistic Cost and Schedule Risk Analysis: Final Report,” Prepared for NASA, 5 April 2013.

Black Swans and Unknown Unknowns (#BSUnk)

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黑天鹅是一个描述事件发生概率低，但影响大的术语。它也被滥用来描述像9/11这样的事件，而没有评估为什么我们没有看到它的到来。很少有原则上无法发现的事件。但我们可能没有时间发现它，没有足够的资源来发现它，也没有我们应该发现它的知识。

• "Black Swan Risk Management for Projects," Jurie Steyn, Owner Team Consultation, February 2022
• "On the meaning of and relationship between dragon-kings, black swans and related concepts," Ingrid Glette-Iversen and Terje Aven, Reliability Engineering and System Safety, 211, 2021.
• "Keys to Success in Managing a Black Swan Event," Nancy Green, Aon Risk Solutions
• "How to identify risky IT projects and avoid them turning into black swans,” Magne Jørgensen, Ernst & Young: Nordic Advisory Learning Weekend, Riga, 2016.
• "Implications of Black Swans to the Foundations and Practice of Risk Assessment and Management," Terje Aven, Reliability Engineering and System Safety, 134, pp. 83-91, 2015
• "On the meaning of a Black Swan in a Risk Context," Terje Aven, Safety Science, Volume 57, pp. 44-51, August 2013.
• "Adapting to the Entirely Unpredictable: Black Swans, Fat Tails, Aberrant Events, and Hubristic Models," P. Hammond, Warwick Bulletin of the Economics Research Institute, 1 November 2009.
• "Black Swans in a Risk Context," Terje Aven, JRC, ISPRA, 21 June 2013.

Risk Management Techniques (#RMTech)

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风险无法管理，只能处理。处理风险有很多技巧。以下是在存在不确定性的情况下运行的复杂系统领域中使用的一些

• "Playbook: Enterprise Risk Management for the U.S. Federal Government," Cheif Financial Officer Council, November 28, 2022
• "Managing Project Uncertainty by Using an Enhanced Risk Management Process," Rolf Olsson, Malardalen Univesity, 2006
• "The Risk of Using Risk Matrices," Philip Thomas, Reidar B. Bratvold, and J. Eric Bickel, SPE Economics and Management 6(02) September 2013.
• "Risk Management for Jet Propulsion Laboratory (JPL) Flight Projects," Dr. James R Rose.
• "Strategies to Detect, Prevent, and Correct the Causes of Complex Program Stress and Failure," Glen B. Alleman, Project and Program Management Symposium, University of New South Wales, 12 August 2021.
• "Deriving Certainty from Uncertainty," Gavin Halling, Project and Program Management Symposium, University of New South Wales, 12 August 2021.
• "Can We Improve Project Risk Management?, Yiding Hu, Weapons and Combat Systems Division, Defence Science and Technology Group, AustraliaProjects Governance & Control Symposium, 2016.
• "The Path from Good Project Scheduling to IMplementing Advanced Work Packaging," Chris Carlson,  Project and Program Management Symposium, University of New South Wales, 12 August 2021.
• "Joint Confidence Levels: Combining Project Control Artefacts to Inform Decision Making," Andrew Butts, Ships Divisional Controls, CASG, Australian Department of Defence, 8 August 2021.
• "Complexity and Uncertainty: Problem or Asset in Decision Making of Mega Infrastrcution Projects?" Willem Salet, Luca Bertolini, and Mendel Giezen, International Journal of Urban and Regional Reseach, 2012.
• "Post-Probabilistic Uncertainty Quantification: Discussion of Potential Use In Product Development Risk," M. Tegeltija, J. Oehmen, I. Kozine, and J. Geraldi, International Design Conference - Design 2016, May 16-19, 2016.
• "Modelling Risk Interactions to Re-Evaluate Risks in Project Management," Chao Fang, Franck Marle, and Ludovic-Alexandre Vidal, 12th International Dependency and Structure Modelling Conference, DSM' 10, 22-23 July 2010.
• "Foundations of Risk and Reliability Assessment and Management," Terje Aven, University of Stavanger, Norway
• "Model Output Uncertainty in Risk Assessment," Enrico Zio and Terje Aven, International Journal of Performability Engineering, 29(5), 2013. 
• "An Accident Precursor Analysis Tailored for NASA Space Systems," Frank Green, Michael Stamatelatos, Homayoon Dezfuli, and Gaspare Maggio, Office of Safety and Mission Assurance, January 2010.
• "Probability and Possibility Based Representations of Uncertainty in Fault Tree Analysis," Enrico Zio, Terje Aven, Piero Baraldi, and R. Flage, Risk Analysis 33(1), Wiley, 2013
• "Choosing project risk management techniques. A theoretical framework," Anna Corinna Cagliano and Carlo Rafele, Journal of Risk Research, 2014
• "Understanding project risk exposure using the two-dimensional risk breakdown matrix," Carlo Rafele, PMI Global Conference, 2005.
• "Managing Project Risks Using a Cross Risk Breakdown Matrix,” David Hillson, Sabrina Grimaldi, and Carlo Rafele, Risk Management, 8, 61-76, 2006.
• "Recommendations on the use and design of risk matrices," Duijm, Nijs Jan, Safety Science 2015-02-22.
• "Is risk listing a reliable risk management practice?" Federico Alistair D’Alessio, Academia Letters, Article2247. https://doi.org/10.20935/AL2247.
• "Understanding Risk and Uncertainty Management Practice in Complex Projects," Mundhir AL Hasani, European Journal of Economics, Law and Politics, ELP 
  December 2017 edition Vol.4, No.4
• "An Assistance to Project Risk Management Based on Complex Systems Theory and Agile Project Management," Franck Marle, Hindawi Complexity
  Volume 2020, Article ID 3739129, 20 pages Wiley, 2020.
• "The Comparison of Selected Risk Management Methods for Project Management," Vladěna Obrová and Lenka Smolíková, 10th International Symposium on Environmental Software Systems (ISESS), Oct 2013, Neusiedl am See, Austria. pp.517-524.
• "From differential to difference importance measures for Markov reliability models," Phuc Do Van, Anne Barros, and Christophe Bérenguer, European Journal of Operational Research, Elsevier, 2009, 204, (3), pp.513-521.
• "Managing Project Risks Using a Cross Risk Breakdown Matrix," David Hillson, Sabrina Grimaldi, and Carlo Rafele, Risk Management
  2006, 8, (61–76).
• "Understanding Project Risk Exposure Using the Two-Dimensional Risk Breakdown Matrix," Carlo Rafele, David Hillson, and Sabrina Grimaldi, 2005 PMI Global Congress Proceedings.
• "Risk Matrix User's Guide," Version 2.2, MITRE and the Download Page
• "RM3 - The Risk Management Maturity Model," Ian Prosser and Karen Russ, Office of Rail and Road, Health & Safety Laboratory.
• “Characterizing Representational Uncertainty in System Design and Operations,” Erica Gralla and Zoe Szajnfarber, Systems Engineering, 2016.
• "Risk Assessment of Plan Schedule by Monte Carlo Simulation," Zhaoni Kong, Jianping Zhang, Chao Li, Xiaoying Zheng, and Qingqing Guan, International Conference on Information Technology and Management Innovation (ICITMI 2015).
• “Requirements for an Effective Project Risk Management Process,” Stephan Ward, Project Management Journal, September 1999.
• “Manifestation of Uncertainty – A Classification,” Melanie E. Kreye, Yee Mey Goh and Linda B. Newnes, International Conference on Engineering Design, ICED11, 15-18 August, Technical University of Denmark.
• “Risk Scenes Of Managerial Decision-Making With Incomplete Information: An Assessment In Forecasting Models Based On Statistical And Neural Networks Approach,” Dusan Marcek, Journal of Risk Analysis and Crisis Response, Vol. 3, No. 1, pp. 13-21, May 2013.
• “A critical discussion and practical recommendations on some issues relevant to the non-probabilistic treatment of uncertainty in engineering risk assessment,” Nicola Pedroni, Enrico Zio, Alberto Pasanisi, Mathieu Couplet, Risk Analysis, 37(7), pp. 1315-1340, Wiley, 2017.
• “Three influential risk foundation papers from the 80s and 90s: Are they still state-of-the-art?”, Terje Aven, Reliability Engineering and System Safety, 28 September 2019.
• “The Essence of Risk Identification in Project Risk Management: An Overview,” Crispin George, International Journal of Science and Research, Volume 9, Issue 2, February 2020.
• "Implementing Risk Management on Software-Intensive Projects," Edmund H. Conrow and Patty S. Shishido, IEEE Software, Institute of Electrical and Electronics Engineers, special issue on software risk management, May/June 1997.
• "Development of Risk Management Defense Extensions to the PMI Project Management Body of Knowledge," Acquisition Review Quarterly, Special Issue on Risk Management, Defense Acquisition University (DAU), Spring 2003.
• "Risk Management for Systems of Systems." CrossTalk, The Journal of Defense Software Engineering, February 2005.

• “Increasing the Probability of Program Success with Continuous Risk Management,” Glen Alleman, Tom Coonce, and Rick Price, The Measurable News, College of Performance Management, pp. 24‒46, 2018.04.
• “Classifying Risk Uncertainty for Decision Making,” Don Port and Joel Wilf, Proceedings of the 52^nd Hawaii International Conference of System Sciences, 2019.
• "Risk Management Processes in Projects," Elena Doval, Review of General Management, Volume 30, Issue 3, the Year 2019.
• “An Index to Measure a System’s Performance Risk,” Paul R. Garvey and Chien-Ching Cho, Acquisition Review Quarterly, Spring 2003.
• "A Systems Approach to Project Risk Management," John P. Kindinger, Probabilistic Risk Analysis Group, Decision Application Group, Los Alamos National Laboratory, 6th International Conference on Probabilistic Safety Assessment and Management, June 23-28, 2002
• “How to manage the risks you didn’t know you were taking,” David Hillson, PMI Global Conference Proceedings, Phoenix AZ.
• “Probabilistic Risk Assessment to Inform Decision Making: Frequently Asked Question,” EPA/100/R-14/003, July 2014.
• “The Respective Roles of Risk and Decision Analyses in Decision Support,” M. Elisabeth Pate-Cornell and Robin L. Dillon, Decision Analysis, Vol. 3, No. 4, pp. 220-232, December 2006.
• “Risk and risk management in software projects: A Reassessment,” Paul L. Bannerman, Journal of Systems and Software, 81, pp. 2118-2133, 2008.
• “NASA Schedule Management Handbook,” NASA/SP-2010-34-3, January 2010.
• "Modeling Uncertainty in Risk Management Using Double Monte Carlo Method," Tazid Ali, Hrishikesh Boruah, Palash Dutta, International Journal of Engineering and Innovative Technology, Volume 1, Issue 4, April 2012.
• “Monte Carlo Simulation And Agile Methods in Project Risk Management,” Prof. Dr. Tysiak Wolfgang, University of Applied Sciences, Dortmund, Germany, Sixth International Scientific Conference on Project Management in the Baltic Countries, April 27-28, 2017, Riga, University of Latvia
• “Risk Management Guide for DOD Acquisition,” Fifth Edition, Version 2.0, June 2003.
• “Analysis Support Guide for Risk-Based Strategic Planning,” STO Technical Report, TR-SAS-093-Part-1 NATO, Science, and Technology Organization, March 2018.
• "Now, let’s make it really complex (complicated): A systematic review of the complexities of projects,” Joana Geraldi, Harvey Maylor, and Terry Williams, International Journal of Operations and Production Management, 31(9), 966-990, August 2011.
• "ESC Risk Management Process Facilitator Training," Version 1.0, Joe Duquette. Electronics Systems Center, 2002.
• “Risk Management Consolidated Training,” Joe Duquette and Mike Bloom, Mitre Corporation, January 2003.
• “A Statistical Method for Estimating Activity Uncertainty Parameters to Improve Project Forecasting,” Mario Vanhoucke and Jordy Batselier, Entropy 21(10) pp. 952 2019
• "An Entropy-Based Approach for Risk Factor Analysis in a Software Development Project,” Pradnya Purandare, International Journal of Applied Engineering Research, Volume 11, Number 4 (2016) pp 2258-2262.
• “On Evidence-Based Risk Management in Requirements Engineering,” Daniel Mendez Fernández, Michaela, TieBler, Marcos Kalinowski Michael Felderer, and Marco Kuhrmann, Software Quality Days, January 2018.
  2005. “Uncertainty in Risk Assessments: Concepts and Principles,” Tim Adams, May 11, 2017.
• “Uncertainty in Risk Assessments: Concepts and Principles,” Tim Adams, NASA, May 11, 2017
• “Risk Leading Indicators For DOD Acquisition Programs,” Gary Witus, Edward Umpfenbach, Walter Bryzik, Jerome Tzau, and Kadry Rizk, 2014 NDIA Ground Vehicle Systems Engineering and Technology Symposium, August 12-14, 2014.
• “Agile Software Development Risk Assessment,” John McCrillis, ICEAA Professional Development & Training Workshop, 2018.
• “Probabilistic Risk Assessment Procedures Guide for NASA Managers and Practitioners,” NASA/SP-2001-3421, December 2011.
• “A Framework for Reducing Impediments in Agile Projects,” Radha Shankarmani, S. S Mantha, and Vinaya Babu, International Journal of Computer Applications, Volume 102, No. 11, September 2014.
• “Can One Divide Uncertainty into Two Kinds?,” Keith Porter, Risk Basics I, SPA Risk LLC and University of Colorado of Boulder, 2016.
• “Risk Management during Requirements,” Tom DeMarco and Tim Lister, IEEE Software, Volume 20, Issue 5, Sept-Oct, 2003.
• “Proposed Risk Management Model to Handling Changing Requirements,” Mohammad D. AlJohani and Rizwan Qureshi, J. Education and Management Engineering, 2019, 5, pp. 18-25, September 2019.
• “Space and Missile Systems Center Tailoring Risk Management Supplement to IEEE 15288,” 15 May 2015.
• “Risk Management, Air Force Instruction 90-802,” 1 April 2019.
• “Quantifying Uncertainty in Early Lifecycle Cost Estimation (QUELCE),” Robert Ferguson, Dennis Goldenson, James McCurley, Robert Stoddard, David Zubrow, and Debra Anderson TECHNICAL REPORT CMU/SEI-2011-TR-026 ESC-TR-2011-026 December 2011.
• “Software Development Project Risk Management: A New Conceptual Framework,” Lazaros Sariginnidis and Prodromos D Chatzoglou, Journal of Software Engineering and Applications, 4(05), December 2010, pp. 293-305.
• “Ontology-Based Uncertainty Management Approach in Designing of Robust Decision Workflows,” Ru Wang, Anand Balu Nellippallil, Guoxin Wanga, Yan Yan, Janet K. Allen, and Farrokh Mistree, Journal of Computing and Information Science in Engineering, 18(1), September 2017.
• “Analysis of the Success factors and Failure Causes of Information & Communication Technology (ICT) Projects in Spain,” VR. Montequin, S. Cousillas, F. Ortega, and J. Villanueva, ProjMAN 2014 – International Conference on Project MANagement, Procedia Technology, 16, pp. 992‒999, 2014.
• “IT Project Failure: A Proposed Four-Phased Adaptive Multi-Method Approach,” Rosana Stocia and Peggy Brouse, Conference on Systems Engineering Research (CSER ’13), March 19‒22, 2013.
• “Risk Measurement: An Introduction to Value at Risk,” Thomas J. Linsmeier and Neil D. Pearson, Linsmeier, Thomas J. and Pearson, Neil D., Risk Measurement: An Introduction to Value at Risk (Undated). Working Paper 96-04. Available at SSRN: https://ssrn.com/abstract=7875
• "Software Development Project Risk Management: A New Conceptual Framework," Lazaros Sarigiannidis and Prodromos D. Chatzoglou, Journal of Software Engineering and Applications, Volume 4, No. 5, May 2011.
• "Interactions-Based Risk Network Simulation for Project Risk Prioritization," Franck Marle, PMI® Research Conference: Defining the Future of Project Management, Washington, DC, 14 July 2010.
• "Using DSM Approach to Manage Interactions Between Project Risks," Frank Marle, 12TH International Dependency and Structure Modelling Conference, DSM’10, 22 – 23 JULY 2010, Cambridge, UK 
• “Can Project Management Learn Anything from Studies of Failure of Complex Projects?,” Chris Ivory and Neil Alderman, Project Management Journal, pp. 5-16, September 2005.
• “Reducing Unwelcome Surprises in Project Management,” Tyson R. Browning and Ranga V. Ramasesh, MIT Sloan Management Review, Spring 2015.
• “Space and Missile Systems Center Risk Management Process Guide,” Ver 2.0, Dr. Edmund Conrow, United States Air Force, Space and Missile Systems Command, El Segundo, CA 5 September 2014.
• “Air Force Instruction 90-802, Risk Management,” 1 April 2019.
• “Risk Management is Project Management for Grown-Ups,” Tim Lister InfoQ Talk
• Risk Management is Project Management for Grown-Ups," Tim Lister, QCon London
• “Risk Management – The Basics,” Werner Gleißner, SSRN, 27 October 2018.
• “Propagation of Uncertainty in Risk Assessments: The Need to Distinguish Between Uncertainty Due to Lack of Knowledge and Uncertainty Due to Variability,” F. Owen Hoffman and Jana S. Hammonds, Risk Analysis, Vol. 4, No. 5, 1994
• “Risk Management for Systems of Systems,” Edmund Conrow, CrossTalk The Journal of Defense Software Engineering, February 2005.
• “The Mathematical Model for Project Risk Responses in Project Risk Management,” Hirokatsu Fukuda and Hiroaki Kuwano, Journal of the Operations Research Society of Japan, Vol. 60, No. 2, pp. 192-200, April 2017.
• “Systemic Risks: A New Challenge for Risk Management,” Andreas Klinke and Ortwin Renn, EMBO Reports, Science and Society, Vol. 5, Special Issue, 2004.
• “Review of the strengths and weaknesses of risk matrices,” Mustafa Elmontsri, Journal of Risk Analysis and Crisis Response, Vol. 4, No. 1, pp. 45-57, March 2014.
• “What’s wrong with risk matrices?,” L. Anthony Cox, Risk Analysis, Vol. 28, No. 2, 2008.
• “Some Limitations of Qualitative Risk Rating Systems,” Louis Anthony (Tony) Cox Jr., Djangir Babayev, William Huber, Risk Analysis, Volume 25, Issue 3, pp. 651-662, June 2005.
• “Systems-Based Guiding Principles for Risk Modeling, Planning, Assessment, Management, and Communication,” Risk Analysis, Vol. 32, No. 9, 2012.
• “Uncertainty Classification for the Design and Development of Complex Systems,” Daniel P. Thunnissen, California Institute of Technology, 3^rd Annual Predictive Methods Conference, 2003.
• “Opportunity Management: Be Careful What You Ask For,” Edmund H. Conrow and Robert N. Charette, pp. 16-19, Defense AT&L, March-April 2008.
• “How Risk in Software Project Management Affects Project Performance?,” Ali Haider Khan and Junaid Aftab, Durreesamin Journal, Vol. 2, Issue 2, July 2016.
• “Project Risk Management: Past, Present, and Future: Views from the Chair,” edited by Dr. David Hillson, June 2012.
• “Ignorance and uncertainty: the case of irrelevance in project risk management,” Elmar Kutsch, Jonathan Lupson, POMS 19^th Annual Conference, La Jolla, California, May 9 – May 12, 2008.
• “Deliberate ignorance in project risk management,” Elmar Kutsch and Mark Hall, International Journal of Project Management, 28, pp. 245-255, 2010.
• “A Taxonomy-based Model for Identifying Risks," Sebastian Maniasi, Paola Veronica Britos, and Ramon Garcia-Mertinez, JIISIC'06 - V Jornadas Iberoamericanas de Ingeniería de Software e Ingeniería del Conocimiento, Memoria Técnica, Proceedings, Puebla, Pue. México, 1 al 3 de Febrero de 2006.
• “Taxonomy-Based Risk Identification,” Marvin Carr, Suresh Konda, Ira Monarch, Carlo Ulrich, and Clay Walker, Technical Report, CMU/SEI-93-TR-6, Software Engineering Institute, June 1993.
• “Management of Novel Projects Under Conditions of High Uncertainty,” A. De Meyer, C. H. Loch, and M. T. Pich, Working Paper, 21/2006, Cambridge University, Judge Business School
• “A Taxonomy of Threats for Complex Risk Management,” Centre for Risk Studies, Research Programme of the Cambridge Centre for Risk Studies, University of Cambridge, Judge Business School, June 2014.
• “A Risk Management Methodology for Project Risk Dependencies,” Tak Wah Kwan and Hareton K.N., IEEE Transactions on Software Engineering, Vol. 37, No. 5, September/October 2011.
• “Project Risk Management: A Combined Analytic Hierarchy Process and Decision Tree Approach,” Prasanta Kumar Dey, Cost Engineering, Vol. 44, No. 3, March 2002.
• “The Effectiveness of Risk Management: An Analysis of Project Risk Planning Across Industries and Countries,” Ofer Zwikael and Mark Ahn, Risk Analysis, Vol. No. 1, 2011.
• “Risk management in ERP project introduction: Review of the literature,” Davide Aloini, Riccardo Dulmin, and Valeria Mininno, Information & Management, 44, pp. 547‒567, 2007.
• “Risk as Feelings,” George F. Loewenstein, Elke U. Weber, Christopher K. Hsee, and Ned Welch, Psychological Bulletin 27, No. 2, pp. 267‒286, 2001.
• “Deliberate Ignorance in Project Risk Management,” Elmar Kutsch and Mark Hall, International Journal of Project Management, Volume 28, Issue 3, pp. 245‒255, April 2010.
• “Project risk management: lessons learned from the software development environment,” Young Hoon Kwak and Jim Stoddard, Technovation, 24(11), pp. 915‒920, November 2004.
• “Use of Benefits of Tools for Project Risk Management,” T. Raz and E. Michael, International Journal of Project Management, 19, pp. 9‒17, 2001.
• “Lessons Learned from Real World Application of the Bow-Tie Method,” Steve Lewis and Kris Smith, 6^th Global Congress on Process Safety, American Institute of Chemical Engineers, March 22‒24, 2010.
• “A Simulation-Based Risk Network Model for Decision Support in Project Risk Management,” Chao Fang and Franck Marle, in Decision Support Systems, Elsevier, 2011.
• “Project Risk Identification Based on the Interaction of Risks from the Approach of Multi-Agent Simulation,” Peng Huang, Peng Di, and Jinrui Li, 4^th International Conference of Energy Equipment Science and Engineering, 2019.
• “Risk Management with Bowtie Diagrams,” I. Voicu, F. V. Panaitescu, M. Panaitescu, L. G. Dumitrescu, and M. Turof, IOP Conference Series: Materials Science and Engineering, 400, 2018.
• “A Critical Review Of Risk Management Support Tools,” Irem Dikmen, M. Talat Birgonul, and A. Erdem Arikan, 20^th Annual ARCOM Conference, 1‒3 September 2004, Heiot Watt University, Association of Researchers in Construction Management, Vol. 2, pp. 1145‒54
• “Predicting the Impact of Multiple Risks on Project Performance: A Scenario-Based Approach," Victor A. Bañuls, Turoff López, and F. Tejedor, Project Management Journal, 48(5), pp. 95‒114.
• “Statistical Methods for Eliciting Probability Distributions,” Paul H. Garthwaite, Joes B. Kadane, and Anthony O’Hagan, Carnegie Mellon University, Statistics & Data Science, January 5, 2005.
• “Risk Management: A Maturity Model Based on ISO 31000,” Diogo Proença, Joao Estevens, Ricardo Vieira, and José Borbinha, IEEE 19^th Conference on Business Informatics, 2017.
• “Assessing the Impacts of Uncertainty Propagation to System Requirements by Evaluating Requirement Connectivity,” Alejandro Salado and Roshanak Nilchiani, INCOSE,
• “Applying the Anticipatory Failure Determination at a Very Early Stage of a System’s Development: Overview and Case Study,” Leszek Chybowski and Katarzyna Gawdzlnska, Multidisciplinary Aspects of Production Engineering (MAPE), Volume 1, Issue 1, pp. 205‒251.
• “A Simulation-Based Risk Network Model for Decision Support in Project Risk Management,” Chao Fang and Franck Marle, Decision Support Systems, Elsevier, 2011.
• “A Complex Systems Perspective of Risk Mitigation and Modeling in Development and Acquisition Programs,” Roshanak Rose Nilchiani, Naval Postgraduate School, Thirtieth Annual Acquisition Research Symposium, April 30, 2016.
• “Planning, Tracking, and Reducing Complex Project’s Value at Risk,” Tyson R. Browning, Project Management Journal, 50, pp. 71‒85 1 Feb 2019.
• “An Approach to Technology Risk Management,” Ricardo Valerdi and Ron Kohl, Engineering Systems Division Symposium, 2004.
• “Causes of Risk Taking by Project Managers,” Michel N. Wakshull, Proceedings of the Project Management Institute Annual Seminars & Symposium, November 1‒10, 2001
• “Near-Critical Paths Create Violations in the P.E.R.T. Assumption of Normality,” Frank Pokladnik and Robert Hill, “Uncertainty In Probabilistic Risk Assessment,” A. R. Daneshkhah, University of Sheffield,
• “Joint Propagation and Exploitation of Probabilistic and Possibilistic Information in Risk Assessment,” Cedric Baudrit, Didier Dubois, Journal of Latex File, Vol. No. 11 November 2002.
• “Propagation of Uncertainty in Risk Assessments: The Need to Distinguish Between Uncertainty Due to Lack of Knowledge and Uncertainty Due to Variability,” F. Owen Hoffman and Jana S. Hammonds, Risk Analysis 14.5 (1994): 707-712.
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• Risk and Performance, F. J. Snyder, and D. M. P. D. Buede, International Council on Systems Engineering (INCOSE) (2000).
• Preference, Belief, and Similarity, Amos. Trevesky, and D. Kahneman, MIT Press, 2004.
• Advances in prospect theory: Cumulative representation of uncertainty, A. Tversky, and D. Kahneman, MIT Press, Cambridge Massachusetts, 2004.
• The Challenger Launch Decision; Risky Technology, Culture, and Deviance at NASA, D. Vaughan, The University of Chicago Press, 1996.
• Human Behavior in Originations, J. C. Williams, South-Western Publishing, 1978.
• “Quantifying Project Characteristics Related to Uncertainty,” David McLain, Project Management Journal, December 2009, 40(4), pp. 60‒73.
• “Allocating ‘Risk Dollars’ Back to Individual Cost Elements,” Stephen A. Book, 40^th Annual DoD Cost Analysis Symposium, 13‒16 February 2007
• “Conceptual and Computational Basis for the Quantification of Margins and Uncertainty,” Jon C. Helton, Sandia Report, SAND2009‒3055, June 2009
• “Uncertainty and Sensitivity Analysis,” Patrick Mattie, Sandia National Laboratories, 2012.
• “Uncertainty and Sensitivity Analysis for Models of Complex Systems,” Jon C. Helton, Reliability Engineering and System Safety. Vol. 54, no. 2‒. 3, pp. 91‒94. 12. Paté‒Cornell, M.E. 1996. 
• “Risk Assessment in ERP Projects,” Davide Aloini, Riccardo Dulmon, and Valeria Mininno, Information Systems 37, 2012, pp. 183‒199.
• “Risk Assessment in ERP Projects Using an Integrated Method,” Afshin Jamshidi, Samira Abbasgholizadeh Rahimi, Daoud Ait‒Kadi, Mohamed Larbi Rebaiaia, and Angel Ruiz, 3^rd International Conference on Control, Engineering & Information Technology, 2015.
• “The Seven Deadly Risks of Defence Projects,” Fred Bennett, Security Challenges, Vol. 6, No. 3, Spring 2010, pp. 97‒11,
• “Closed-Form Approach for Epistemic Uncertainty Propagation in Analytic Models,” Kesari Mishra and Kishor S. Trivedi, in Stochastic Reliability and Maintenance Models, Springer, 2013.
• “Error and Uncertainty in Modeling and Simulation,” William Oberkampf, Sharon DeLand, Brian Rutherford, Kathleen Diegert, and Kenneth Alvin, Reliability Engineering and System Safety, 75, 2002, pp. 333‒357.
• “Uncertainty Quantification Using Evidence Theory, William Oberkampf, Advanced Simulation & Computing Workshop, August 2005.
• “The Importance of Risk Assessment in the Context of Investment Project Management: A Case Study,” M. Bernadete Junkesa, Anabela P. Tereso, Paulo S.L.P. Afonso, Conference on ENTERprise Information Systems / International Conference on Project Management / Conference on Health and Social Care Information Systems and Technologies, CENTERIS / ProjMAN / HCist 2015 October 7‒9, 2015.
• “Considering Risk and Resilience in Decision‒Making,” Wilfredo Torres‒Pomales, NASA/TM‒2015‒218777, July 2015.
• “Integrated Uncertainty Quantification for Risk and Resource Management: Building Confidence in Design,” Eric Walker, Michael Hemsch, and Thomas West, 17^th AIAA Non‒Deterministic Approaches Conference, APA‒09/NDA‒01, January 5‒9, 2015.
• “Ontological Uncertainty and Innovation,” David Lane and Robert Maxfield, SFI Working Paper: 2004‒06‒014, Santa Fe Institute.
• “Chapter 2 ‒ Levels of Uncertainty,” Hauke Riesch,” in Essentials of Risk Theory, edited by Sabine Roeser, Rafaela Hillerbrand, Per Sandin, Martin Peterson, Springer Science & Business Media, 2012.
• “Implementing Risk Management on Software Intensive Projects,” Edmund Conrow and Patricia Shishido, IEEE Software, May/June 1997.
• “A Simulation-Based Risk Network Model for Decision Support in Project Risk Management,” Chao Fang, Franck Marle, Decision Support Systems, Elsevier, 2011
• “Uncertainty Classification for the Design and Development of Complex Systems,” Daniel P. Thunnissen, 3^rd Annual Predictive Methods Conference, Newport Beach, California, June 2003.
• “Levels of Uncertainty,” Hauke Riesch, in Chapter 2, Essentials of Risk Theory, SpringerBriefs in Philosophy, 2013.
• “Project Risk Management Incorporating Knight, Ellsberg & Kahneman,” John Prpić, IEEE 49^th Hawaii International Conference on System Sciences, 2016
• “Why Your IT Project May Be Riskier Than You Think,” Bent Flyvbjerg and Alexander Budzier, Harvard Business Review, Vol. 89, No. 9, pp. 601‒603
• “A review of techniques for risk management in projects,” Ammar Ahmed, Berman Kayis and Sataporn Amornsawadwatana, Benchmarking: An International Journal, Vol. 14 No. 1, 2007, pp. 22‒36
• “Agile risk management using software agents,” Edzreena Edza Odzaly, Des Greer, and Darryl Stewart, Journal Ambient Intelligent Humanized Computing, pp. 1‒19, 2 May 2017.
• “The Trouble with Risk Matrices,” Kent D. Wall, Naval Postgraduate School (DRMI), April 2011.
• “Schedule Matters: Understanding the Relationship between Schedule Delays and Costs on Overruns,” Walt Majerowicz and Stephen Shinn, IEEE, 2016.
• “Perception on Risk Management Strategies in Software Development,” Nitin Deepak and Shishir Kumar, Publishing India
• “Risk Management, Project Success, and Technological Uncertainty,” Tzvi Raz, Aaron Shenhar, and Dov Dvir, R&D Management, 32, 2, 2002.
• “Risk Impact Analysis across the Phases of Software Development,” Raghavi K. Bhujang and Suma V., Lecture Notes on Software Engineering, Vol. 2, No. 3, August 2014.
• “Timid Choices and Bold Forecasts: A Cognitive Perspective of Risk-taking,” Daniel Kahneman and Dan Lovallo, Management Science, Vol. 39, No. 1, Jan 1993, pp. 17‒31.
• “The Macro Risk Model: An Early Warning Tool for Software‒Intensive Systems Projects,” Barry Boehm, Dan Ingold, and Raymond Madachy, INCOSE International Symposium, Volume 18, Issue 1, June 2008, pp. 958–972
• “Project Risk Management Model Based on PRINCE2 and Scrum Frameworks,” Martin Tomanek and Jan Juricek, The International Journal of Software Engineering & Applications (IJSEA), January 2015, Volume 6, Number 1.
• “Understanding Risk Attitude,” David Hillson and Ruth Murray‒Webster, Defence Management Network, 14 September 2006.
• “Decision Making Under Epistemic Uncertainty for a Complex Mechanical System,” Angel Urbina and Laura P. Swiler, 52^nd AIAA/ASME/AHSA/ASC Structures, Structural Dynamics and Materials Conference, 4‒7 April 2011, Denver, Colorado.
• “A Non‒Simulation-Based Method for Inducing Pearson’s Correlation Between Input Random Variables And its application on the CG(X) risk assessment,” Eric Druker, 2008 Acquisition Research Symposium, 15 May 2008.
• “Numeracy for Cost Analysts Doing the Right Math, Getting the Math Right, Neal D. Hulkower, Ph.D., MCR Federal, LLC.
• “Statistical Methods for Learning Curves and Cost Analysis,” Matthew 5. Goldberg and Anduin Touw, CIM D0006870.A3/1 Rev, March 2003.
• “A Step-Wise Approach to Elicit Triangular Distributions, Marc Greenberg,” SCEA Luncheon Series, Washington Area Chapter of SCEA April 17, 2012, Arlington, Virginia.
• “The Respective Roles of Risk and Decision Analyses in Decision Support,” M. Elisabeth Paté‒Cornell and Robin L. Dillon, Decision Analysis, Vol. 3, No. 4, December 2006, pp. 220‒232.
• “Some considerations on the treatment of uncertainties in risk assessment for practical decision making,” Terje Aven and Enrico Zio, Reliability Engineering and System Safety, 96, 2001, pp. 64‒74.
• “Department of Defense Risk, Issue, and Opportunity Management Guide for Defense Acquisition Programs,” January 2017
• “FRISK‒ Formal Risk Assessment of System Cost,” Phillip Young, American Institute of Astronautics and Astronautics, 1992.
• “Characterization of unknown unknowns using separation principles in a case study on Deepwater Horizon oil spill,” Seong Dae Kim, Journal of Risk Research, 28 November 2014.
• “Zen in the Art of Cost Estimation,” Ricardo Valerdi.
• “From Research to Reality: Making COSYSMO a trusted estimation tool in your organization,” Ricardo Valerdi, 17^th Annual INCOSE International Symposium, 2007.
• “How Engineers Really Think About Risk: A Study of JPL Engineers,” Ricardo Valerdi, Jairus Hihn, and Deb Chattopadhyay, ISPA/SCEA Southern California Chapter Workshop, 16 March 2011.
• “Unknown Unknowns in Project Probabilistic Cost and Schedule Risk Models,” Yuri Raydugin, Risk Services & Solutions.
• “A Systematic Review of Software Development Cost Estimation Studies,” Seong Dae Kim, PMI Global Conference, 2012, Vancouver, British Columbia, Canada.
• “Embrace Risk! An agile approach to risk management,” Alan Moran, IARM, 2014
• “An Information‒Entropy-Based Risk Management Method for Software Development,” Rong Jiang, Journal of Information Science and Engineering, 30, pp. 1271‒1301, 2014.
• “An Entropy-Based Approach for Risk Factor Analysis in a Software Development Project,” Pradnya Purandare, International Journal of Applied Engineering Research, Volume 11, Number 4, 2016.
• “A Correlation Model to Analyze Dependent Variables,” H. Dezfuli, M. Modarres, Department of Chemical and Nuclear Engineering, University of Maryland, College Park, MD 20742.
• “The Development of Progress Plans Using a Performance-Based Expert Judgment Model to Assess Technical Performance and Risk,” Justin W. Eggstaff, Thomas A. Mazzuchi, and Shahram Sarkani, Systems Engineering, Volume 17, Issue 4, Winter 2014, Pages: 375–391
• “Using the Agile Methodology to Mitigate the Risks of Highly Adaptive Projects,” Dana Roberson and Mary Anne Herndon, 10^th Annual CMMI Technology Conference and User Group, November 5 – 8, 2012, Denver, CO.
• “Hybrid–Agile Software Development AntiPatterns, Risks, and Recommendations,” Paul E. McMahon, Cross Talk: The Journal of Defense Software Engineering, July/August 2015, pp. 22–26.
• “Using the Agile Methodology to Mitigate the Risks of Highly Adaptive Projects,” Dana Roberson and Mary Anne Herndon, 10^th Annual CMMI Technology Conference and User Group, November 5 – 8, 2012, Denver, Colorado.
• “Assessment of risks introduced to safety-critical software by agile practices — A Software Engineer’s Perspective,” Janusz Górski Katarzyna Łukasiewicz, AGH University of Science and Technology, University in Kraków, Poland, Computer Science, Vol 13, No 4.
• “Ready & Fit: Understanding Agile Adoption Risk in DoD and Other Highly Regulated Settings,” Suzanne Miller and Mary Ann Lapham, 25^th Annual Software Technology Conference, Salt Lake City, 8‒10 April 2013.
• “Architecting Large Scale Agile Software Development: A Risk-Driven Approach,” Ipek Ozkaya, Michael Gagliardi, Robert L. Nord, CrossTalk: The Journal of Defense Software Engineering, May/June 2013.
• “Risk Management Method using Data from EVM in Software Development Projects,” Akihiro Hayashi and Nobuhiro Kataoka, International Conference on Computational Intelligence for Modelling, Control and Automation, Vienna, Austria, Dec. 10 to Dec. 12, 2008.
• “Analyse Changing Risk of Organizational Factors in Agile Project Management,” Shi Tong, Chen Jianbin, and Fang DeYing, The 1^st International Conference on Information Science and Engineering (ICISE 2009).
• “Modeling Negative User Stories is Risky Business,” Pankaj Kamthan and Nazlie Shahmir, 2016 IEEE 17^th International Symposium on High Assurance Systems Engineering.
• “Project Risk Management Model Based on PRINCE2 and Scrum Frameworks,” Martin Tomanek, Jan Juricek, The International Journal of Software Engineering & Applications (IJSEA), January 2015, Volume 6, Number 1, ISSN: 0975‒9018
• “A Methodology for Exposing Software Development Risk in Emergent System Properties,” Technical Report 11‒101, April 21, 2001, Victor Basili, Lucas Layman, and Marvin Zelkowitz, Fraunhofer Center for Experimental Software Engineering, College Park, Maryland.
• “Outlining a Model Integrating Risk Management and Agile Software Development,” Jaana Nyfjord and Mira Kajko‒Mattsson, 34^th Euromicro Conference Software Engineering and Advanced Applications.
• “Towards a Contingency Theory of Enterprise Risk Management,” Anette Mikes Robert Kaplan, Working Paper 13–063 January 13, 2014, AAA 2014 Management Accounting Section (MAS) Meeting Paper
• “Agile Development and Software Architecture: Understanding Scale and Risk,” Robert L. Nord, IEEE Software Technology Conference, 2012, Salt Lake City, 23‒26 April 2012.
• “Using Risk to Balance Agile and Plan-Driven Methods,” Barry Boehm and Richard Turner, IEEE Computer, June 2003.
• “Does Risk Management Contribute to IT Project Success? A Meta-Analysis of Empirical Evidence,” Karel de Bakker, Albert Boonstra, Hans Wortmann, International Journal of Project Management, 2010.
• “A Model for Risk Management in Agile Software Development,” Ville Ylimannela, Communications of Cloud Software.
• “Product Security Risk Management in Agile Product Management,” Antti Vähä‒Sipilä, OWASP AppSec Research, 2010
• “A Probabilistic Software Risk Assessment and Estimation Model for Software Projects,” Chandan Kumar and Dilip Kumar Yadav, Eleventh International Multi-Conference on Information Processing‒2015 (IMCIP‒2015).
• “Decision Making under Epistemic Uncertainty: An Application to Seismic Design,” Anna Agarwal, MIT, February 2008.
• “Risk: The Final Agile Frontier,” Troy Magennis, Agile 2015.
• “Risk Management and Reliable Forecasting using Un‒Reliable Date,” Troy Magennis, Lean Kanban, Central Europe, 2014.
• “Management of risks, uncertainties and opportunities on projects: time for a fundamental shift,” Ali Jaafari, International Journal of Project Management 19 (2001) 89‒101.
• “On Uncertainty, Ambiguity, and Complexity in Project Management,” Michael T. Pich, Christoph H. Loch, and Arnoud De Meyer, Management Science © 2002 INFORMS, Vol. 48, No. 8, August 2002 pp. 1008–1023
• “Risk Options and Cost of Delay,” Troy Magennis, LKNA 2014.
• “Transforming project risk management into project uncertainty management,” Stephen Ward and Chris Chapman, International Journal of Project Management, 21 (2003) 97–105.
• “A case study of risk management in agile systems development,” Sharon Coyle and Kieran Conboy, 17^th European Conference on Information Systems (Newell S, Whitley EA, Pouloudi N, Wareham J, Mathiassen L eds.), 2567‒2578, Verona, Italy, 2009
• “Risk management in agile methods: a study of DSDM in practice,” Sharon Coyle, 10^th International Conference on eXtreme Programming and Agile Processes in Software Engineering, 2009.
• “Two Dimensions of Subjective Uncertainty: Clues from Natural Language,” Craig R. Fox and Gülden Ülkümen, Journal of Experimental Psychology, Oct 2016.
• “An Essay Towards Solving a Problem in the Doctrine of Chance,” By the late Rev. Mr. Bayes, communicated by Mr. Price, in a letter to John Canton, M. A., and F. R. S.
• “Playbook: Enterprise Risk Management for the U.S. Federal Government, in support of OMB Circular A‒123.”
• “Joint Agency Cost Schedule Risk and Uncertainty Handbook,” Naval Center for Cost Analysis, 12 March 2014.
• “Commonalities in Risk Management and Agile Process Models,” Jaana Nyfjord and Mira Kajko‒Mattsson, International Conference on Software Engineering Advances (ICSEA 2007).
• “Software Risk Management in Practice: Shed Light on Your Software Product,” Jens Knodel, Matthias Naab, Eric Bouwers, Joost Visser, IEEE 22^nd International Conference on Software Analysis, Evolution, and Reengineering (SANER), 2015
• “Software risk management,” Sergey M. Avdoshin and Elena Y. Pesotskaya, 7^th Central and Eastern European Software Engineering Conference, 2011.
• “A New Perspective on GDSD Risk Management Agile Risk Management,” Venkateshwara Mudumba and One‒Ki (Daniel) Lee, International Conference on Global Software Engineering, 2010
• “Using Risk Management to Balance Agile Methods: A Study of the Scrum Process,” Benjamin Gold and Clive Vassell, 2^nd International Conference on Knowledge-Based Engineering and Innovation, November 5‒6, 2015
• “Using Velocity, Acceleration, and Jerk to Manage Agile Schedule Risk,” Karen M. Bumbary, IEEE‒ICISE 2016 International Conference on Information Systems Engineering, pp. 73‒80, 2016.
• “The Risks of Agile Software Development Learning from Adopters,” Amany Elbanna and Suprateek Sarker, IEEE Software, September / October 2016.
• “Lightweight Risk Management in Agile Projects,” Edzreena Edza Odzaly, Des Greer, Darryl Stewart, 26^th Software Engineering Knowledge Engineering Conference (SEKE), November 2015.
• “A Method of Software Requirements Analysis Considering the Requirements Volatility from the Risk Management Point of View,” Yunarso Anang, Masakazu Takahashi, and Yoshimichi Watanabe, 22^nd International Symposium on QFD, Boise, Idaho.
• “Analyse Changing Risk of Organizational Factors in Agile Project Management,” Shi Tong, Chen Jiabin, and Fang DeYing, The 1^st International Conference on Information Science and Engineering (ICISE2009).
• “Outlining a Model Integrating Risk Management and Agile Software Development,” Jaana Nyfjord and Mira Kajko‒Mattsson, 34^th Euromicro Conference Software Engineering and Advanced Applications. 2009.
• “How Do Real Options Concepts Fit in Agile Requirements Engineering?,” Zornitza Racheva and Maya Daneva, Eighth ACIS International Conference on Software Engineering Research, Management and Applications, 2010.
• “Managing Risk Within A Decision Analysis Framework,” Homayoon Dezfuli, Robert Youngblood, Joshua Reinert, NASA Risk Management Page.
• “NASA Risk Management Handbook,” NASA/SP‒2011‒3422, Version 1.0, November 2011.
• “Risk Management for Software Projects in An Agile Environment – Adopting Key Lessons from The Automotive Industry,” Oana Iamandi, Marius Dan, and Sorin Popescu, Conference: MakeLearn and TIIM Joint International Conference 2015: Managing Intellectual Capital and Innovation for Sustainable and Inclusive Society, Bari, Italy, 2015.
• “Role of Agile Methodology in Software Development,” Sonia Thakur and Amandeep Kaur, International Journal of Computer Science and Mobile Computing, Volume 2, Issue 10, October 2013, pp. 86‒90.
• “Agile Risk Management Workshop,” Alan Moran, Agile Business Conference, 08.10.2014, London England.
• “Embrace Risk! An Agile approach to risk management,” Institute for Agile Risk Management, 2014.
• “Risks in distributed agile development: A review,” Suprika Vasudeva Shrivastava and Urvashi Rathod, Procedia ‒ Social and Behavioral Sciences 133 (2014) 417 – 424, ICTMS‒2013.
• “Risk and Uncertainty in Project Management Decision‒Making,” Karolina Koleczko, Public Infrastructure Bulletin, Vol. 1, Issue. 8 [2012], Art.-13.
• “Uncertainty and Project Management: Beyond the Critical Path Mentality,” A. De Meyer, C. Loch, And M. Pich, INSEAD Working Paper, 2001.
• “Proposal of Risk Management Metrics for Multiple Project Software Development,” Miguel Wanderleya, Júlio Menezes Jr., Cristine Gusmãoa, Filipe Limaa, Conference on ENTERprise Information Systems / International Conference on Project Management / Conference on Health and Social Care Information Systems and Technologies, CENTERIS / ProjMAN / HCist, 2015 October 7‒9, 2015.
• “Outlining a Model Integration Risk Management and Agile Software Development,” Jaana Nyfjord and Mira Kajko‒Mattsson, 34^th Euromicro Conference Software Engineering and Advanced Applications, 2008.
• “The Impact of Risk Checklists on Project Manager’s Risk Perception and Decision‒Making Process,” Lei Li, Proceedings of the Southern Association for Information Systems Conference, Savannah, GA, USA March 8th–9th, 2013.
• “Identifying the Risks Associated with Agile Software Development: An Empirical Investigation.” Amany Elbanna, MCIS 2014 Proceedings. Paper-19.
• “Uncertainty, Risk, and Information Value in Software Requirements and Architecture,” Emmanuel Letier, David Stefan, and Earl T. Barr, ICSE ’14, May 31 – June 7, 2014, Hyderabad, India
• “Risk-Based Scrum Method: A Conceptual Framework,” Nitin Uikey and Ugrasen Suman, 2015 2^nd International Conference on “Computing for Sustainable Global Development, 11^th ‒ 13^th March 2015.
• “Implementation of Risk Management with SCRUM to Achieve CMMI Requirements,” Eman Talal Alharbi, M. Rizwan Jameel Qureshi, International Journal Computer Network and Information Security, 2014, 11, 20‒25.
• “Risk, ambiguity, and the Savage axioms,” Daniel Ellsberg, The Quarterly Journal of Economics, Vol. 75, No. 4, pp. 643‒669, Nov 1961.
• “Risk, Ambiguity, and the Savage Axioms,” Daniel Ellsberg, August 1961, RAND Corporation, Report P‒2173.
• “Dealing with Uncertainty Arising Out of Probabilistic Risk Assessment,” Kenneth Solomon, William Kastenberg, and Pamela Nelson, RAND Corporation, R‒3045‒ORNL, September 1983.
• “Epistemic Uncertainty Analysis: An Approach Using Expert Judgment and Evidential Credibility,” Patrick Hester, International Journal of Quality, Statistics, and Reliability, Volume 2012, Article ID 617481, 8 pages.

Risk Management Thesis (#RMThesis)

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• "Characterizing Uncertainty to Manage Risk in Spacecraft Development with Application to Structures and Masses," Emily Baker Clements, MIT, June 2013
• “Applying Bayesian networks to model uncertainty in project scheduling,” Vahid Khodakarami, Queen Mary, University of London, 2009
• “A Study Of Uncertainty And Risk Management Practice Relative To Perceived Project Complexity,” Craig Michael Harvett, Bond University, May 2013.
• “Uncertainty analysis and sensitivity analysis for multidisciplinary systems design systems design,” Jia Guo, Missouri State, Department of Mechanical and Aerospace Engineering, 2008.
• “Integrated Model for Project Risk & Uncertainty Management Ilyas B. Alhassan Tahir Mehmod, Royal Institute of Technology Stockholm, Sweden,
• “Cognitive Biases in Risk Management,” William Thomas Siefert, Master’s Thesis, Missouri University of Science and Technology, Fall, 2007.
• "Uncertainty in Qualitative Risk Analysis and Rating Systems: Modeling Decision Making Determinants," Ogaga Jonathan Tebehaevu, July 2015.
• “The Development Of An Empirical-Based Framework For Project Risk Management,” Saipol Bari Abd Krim, Manchester Business School, 2014.
• “The Impact of Risk Identification Biases and Trends on Space System Project Performance,” John Daniel Reeves Jr., George Washington University, May 19, 2013.
• “Defining Risk Assessment Confidence Levels For Use In Project Management Communications,” Gary L. Johnson, University of Central Florida, 2008.
• “Uncertainty in Qualitative Risk Analysis and Rating Systems: Modeling Decision Making Determinants,” Ogaga Jonathan Tebehaevu, East Carolina University, July 2015.
• “UAS Risk Analysis using Batesian Belief Networks: An Application to the Virginia Tech ESPAARO,” Christopher G. Kevorkian, Master of Science in Aerospace Engineering, August 5, 2016.
• “The Identification and Reduction of Risk in Project Management,” Rene Cortin, The University of Birmingham, Faculty of Commerce, The Birmingham Business School.
• “Project Management Uncertainty,” Ssempebwa Kibuuka Ronald, Atlantic International University.
• “A risk register based on the (A, C, U) risk perspective,” Anders Jensen, University of Stavanger, June 14, 2012.
• “Integrating Software into PRA (Probabilistic Risk Assessment),” Bin Li, University of Maryland, 2004.
• “Integrated Model for Project Risk & Uncertainty Management,” Llyas B. Alhassan and Tahir Mehmood, The Royal Institute of Technology, KTH, Stockholm, Sweden.
• “Project Risk Propagation Modeling of Engineering, Procurement, and Construction,” Alireza Atin, Wayne State University, 2016.
• “Modeling and Analyzing Propagation Behavior in Complex Risk Network: A Decision Support System for Project Risk Management,” Chao Fang, Laboratoire Génie Industriel, École Centrale des Arts et Manufactures, 2011.
• “Integrated Scenario-Based Methodology for Project Risk Management,” Clayton A. Smith, University of Maryland, 2011.
• “The Influence of Software Risk Management on Software Project Success,” Hiba Arafeh and Adham El-Ahmad, Lund University, School of Economics and Management, May 2017.
• “Project Manager Confidence and Risk Awareness,” Carol S. Davis, Liberty University, School of Business, August 2018.
• “Uncertainty Quantification and Optimization of Structural Response Using Evidence Theory,” Ha‒Rok Bae, Wright State University, 2004.
• “A Study on Software Development Project Risk, Risk Management, Project Outcomes, and Their Inter-Relationship,” Sam Thomas, Cochin University of Science and Technology, July 2008.
• “Early Warning Signs in Software Projects,” Mihir Kothari, Auckland University of Technology, 2010.
• “Method for Risk Analysis in regard of different types of projects,” Alex Maj Bangsgaard, Aarhus School of Business, University of Aarhus 2010.
• “On the use of expert judgment in the qualification of risk assessment,” Tony Rosqvist, VTT Publications 507, Helsinki University of Technology, 11 December 2003.
• “Propagating and Mitigating Uncertainty in the Design on Complex Multidisciplinary Systems,” Daniel Thunnissen, California Institute of Technology, 2005.
• “Project management in a high‒uncertainty situation Uncertainty, risk and project management in international development projects,” Knut Samset, Norwegian University of Science and Technology Faculty of Civil and Environmental Engineering Department of Building and Construction Engineering, May 1998.
• “A Methodology for Project Risk Analysis Using Bayesian Belief Networks Within A Monte Carlo Simulation Environment,” Javier F. Ordóñez Arízaga, Graduate School of the University of Maryland, College Park, Maryland.
• “Distributed Software Development Agile Risk Management Framework: A Systematic Literature Review,” Navid Vajdi and Raja Manzar Abbass, Chalmers University of Technology University of Gothenburg, Department of Computer Science and Engineering, SE‒412 96 Göteborg, Sweden
• “Empirical research on relative and absolute effort estimation in software development projects,” Ivar Fredriksen, University of Oslo, Department of Informatics, 2 November 2009.
• “Towards integrating agile development and risk management,” Jaana Nyfjord, Stockholm University, DSV Report Series, No. 08‒008, 2008.

定义

项目治理是制定项目决策的管理框架。项目治理是任何项目的一个关键要素，因为与组织照常经营活动相关的责任和职责在其组织治理安排中规定；很少有同等的框架来管理其资本投资（项目）的发展。例如，组织结构图很好地显示了组织中谁负责组织开展的任何特定运营活动。但是，除非组织专门制定了项目治理策略，否则项目开发活动不可能存在这样的图表。

因此，项目治理的作用是提供一个逻辑性、健壮性和可重复性的决策框架来治理组织的资本投资。通过这种方式，一个组织将拥有一种结构化的方法来进行其业务照常活动和业务变更或项目活动。

项目治理的三大支柱

决策框架由三大支柱支持：

结构

这是指治理委员会结构。首先是批准项目资源（资本、人力和其他）的资本支出委员会。其次是项目组合委员会，确保选择正确的项目。除了有一个项目委员会[1]或项目指导委员会外，更广泛的治理环境可能包括各种利益相关者群体，也许还有用户群体。此外，可能有一个计划，管理一组相关的项目，这是其中之一，也可能是某种形式的投资组合决策小组。必须在政策和程序文件中规定所有这些委员会的决策权及其相互关系。通过这种方式，项目的治理可以集成到更广泛的治理领域中。

其他理事机构包括：

• 职能管理层负责项目团队成员及其工作和可交付成果的战术治理
• 项目资源和活动运营治理项目团队
• 审查和审计作为独立的过程治理、可交付成果和信息质量的职能
• 独立财务合规审计的财务审计

人

委员会结构的有效性取决于各个治理委员会的成员。委员会成员由项目的性质决定——在决定项目和投资组合委员会成员时，其他因素也起作用——这反过来决定了委员会中应代表哪些组织角色。

信息

这涉及通知决策者的信息，包括关于项目的定期报告、项目经理上报的问题和风险以及描述项目的某些关键文件，其中最重要的是商业案例。

核心项目治理原则

项目治理框架应以若干核心原则为基础，以确保其有效性。

原则1：确保项目成功的单一责任点

最基本的项目责任是项目成功的责任。一个项目如果不清楚谁对其成功负责，就没有明确的领导。没有明确的项目成功责任，就没有一个人来推动解决困扰所有项目生命中某个时刻的难题。在关键的项目启动阶段，这也会减慢项目的进度，因为没有人做出必要的重要决策，从而使项目站稳脚跟。单一责任点的概念是有效项目治理的首要原则。

然而，仅仅提名一个人负责是不够的——必须让合适的人负责。这有两个方面。负责人必须在组织内拥有足够的权力，以确保他们有权做出项目成功所需的决策。然而，除此之外，来自组织内正确区域的正确人员必须承担责任。如果选择了错误的人，该项目的地位并不比没有人对其成功负责要好。将对项目的成功承担责任的个人是原则2的主题。

原则2：项目所有权独立于资产所有权、服务所有权或其他利益相关者群体

通常，组织促进将项目所有者角色分配给服务所有者或资产所有者，目的是更确定项目将满足这些所有者的基本需求，这也是项目成功的关键衡量标准。然而，这种方法的结果可能涉及浪费的范围包含和未能实现替代利益相关者和客户要求：

• 质疑的好处在于分配了项目业主责任的利益相关者，从而扭曲了项目结果；
• 项目业主的要求较少受到审查，减少了创新，降低了成果效率；
• 围绕项目所有权、资产所有权和服务所有权的不同技能将健全的项目决策和程序置于风险之中；
• 运营需求始终占主导地位，使项目处于被忽视的风险之中；
• 项目或有事项有被分配给利益相关者分配的项目所有权的额外范围的风险。

确保项目满足客户和利益相关者需求的唯一行之有效的机制是将项目所有权分配给专业方，否则专业方将不会成为项目的利益相关者。这是项目治理的第二条原则。

项目业主在明确的条款下参与，这些条款概述了组织的关键成果领域以及组织对关键项目利益相关者的看法。通常，组织会建立项目治理委员会，该委员会确定项目的存在，并在项目生命周期内尽早任命项目所有者，建立项目委员会，形成客户和利益相关者参与的基础，建立符合组织价值观的项目关键成果领域，并监督项目绩效。这些参数通常在项目管理计划中详细说明，该计划在项目生命周期内保持不变（与项目管理计划不同，该计划更详细，仅在项目开发期间生效）。

项目有许多利益相关者，有效的项目治理框架必须满足他们的需求。下一个原则涉及这种情况的发生方式。

原则3：确保利益相关者管理和项目决策活动的分离

一个委员会的决策效率可以被认为与其规模成反比。大型委员会不仅不能及时做出决策，而且由于特殊的群体动态，它所做的决策往往考虑不周。

随着项目决策论坛规模的扩大，它们往往会演变成利益相关者管理小组。当人数增加时，每位与会者对关键项目问题的详细了解就会减少。出席会议的许多人不是为了做决定，而是为了了解项目的进展情况。不仅每个人都没有足够的时间来表达自己的观点，而且那些最有效的投入者必须与那些只参与项目外围的人争夺时间和影响力。此外，并非所有出席会议的人都对这些问题有相同程度的理解，因此浪费时间让大家了解正在讨论的特定问题。因此，就所有意图和目的而言，大型项目委员会更像是一个利益相关者管理论坛，而不是一个项目决策论坛。当项目取决于委员会及时作出决定时，这是一个重大问题。

毫无疑问，项目决策和利益相关者管理这两项活动对项目的成功至关重要。问题是，它们是两个独立的活动，需要这样对待。这是有效项目治理的第三个原则。如果能够实现这种分离，它将避免因众多利益相关者而阻塞决策论坛，因为它将其成员限制在那些对其成功至关重要的特定利益相关者。

总是有人担心，如果不满的利益相关者没有考虑到他们的需求得到满足，这个解决方案将导致另一个问题。无论采用何种利益相关者管理机制，都必须充分满足所有项目利益相关者的需求。它将需要收集他们的意见和看法，并满足他们的关切。这可以通过由项目委员会主席主持任何关键利益相关者小组来部分实现。这确保了利益相关者有项目所有者（或SRO）在项目委员会中支持他们的问题和关注。

原则4：确保项目治理和组织治理结构分离

项目治理结构的建立正是因为人们认识到组织结构不提供交付项目所需的框架。项目需要决策的灵活性和速度，与组织结构图相关的层级机制无法实现这一点。项目治理结构通过将关键决策者从组织结构中拉出来，并将他们放在论坛中，从而避免了与层级相关的连续决策过程，从而克服了这一问题。

因此，为项目建立的项目治理框架应与组织结构分开。公认组织在报告和利益相关者参与方面有有效的要求。然而，项目建立的专门报告机制可以解决前者，项目治理框架本身必须解决后者。应避免的情况是，指导委员会或项目委员会的决定需要得到该项目决策论坛以外组织中一名或多名人员的批准；要么将这些个人作为项目决策机构的成员，要么充分授权当前的指导委员会/项目委员会。指导委员会/项目委员会负责批准、审查进度、交付项目成果及其预期收益，因此，他们必须有能力做出决策，这可能会在原始计划之外投入资源和资金。这是有效项目治理的最终原则。

采用这一原则将最大限度地减少多层决策以及与之相关的时间延迟和效率低下。它将确保项目决策机构有权及时作出决策。[2]

治理的补充和补充原则

董事会全面负责项目管理的治理。明确定义了项目管理治理的角色、责任和绩效标准。在整个项目生命周期中，采用适当的方法和控制措施支持的严格的治理安排。整体业务战略和项目组合之间表现出连贯和支持的关系。

所有项目都有一个包含授权点的批准计划，在该计划中审查和批准业务案例。记录并传达在授权点做出的决定。授权机构的成员具有足够的代表性、能力、权限和资源，使他们能够做出适当的决策。项目商业案例由相关和现实的信息支持，这些信息为做出授权决策提供了可靠的依据。董事会或其委托代理人决定何时需要对项目和项目管理系统进行独立审查，并相应实施此类审查。

对于报告项目状态以及将风险和问题升级到组织要求的水平，有明确定义的标准。该组织培养了一种改进和坦率地内部披露项目信息的文化。项目干系人的参与水平与其对组织的重要性相称，并以促进信任的方式进行。[3]

多所有项目的原则

多所有被定义为董事会与其他方共享最终控制权的项目。原则是：[4]

• 应该有正式商定的治理安排
• 项目应该有一个单一的决策点
• 在与业主、利益相关者和第三方接触时，应明确分配代表项目的权力
• 项目商业案例应包括商定的和当前的项目目标定义、每个业主的角色、他们的激励、投入、权力和责任
• 各业主应确保共同业主的法律权限和义务以及内部治理安排符合其可接受的项目治理标准
• 应有项目授权点和限制条件，以便业主对项目进行必要程度的控制
• 考虑到影响结果的能力和创造激励以促进合作行为，应就奖励和风险的认可、分配或分享达成一致
• 项目领导层应利用多重所有权产生的协同效应，并应积极管理冲突或效率低下的潜在来源
• 应当有一份正式协议，规定在考虑重大所有权变更时要调用的流程以及对资产和所有者的后果
• 项目期间和收益实现期间的报告应提供诚实、及时、现实和相关的进度、成就、预测和风险数据，以满足业主的良好治理要求
• 当符合一个或多个项目业主的合法利益时，应建立一种机制，以进行独立审查或审查。
• 业主之间应商定一个不危及项目目标实现的争议解决程序。

角色

项目治理中的一个关键角色是项目发起人。项目发起人有三个主要职责领域，分别是董事会、项目经理和项目利益相关者。

董事会

对于董事会而言，赞助人在文化和价值观方面发挥领导作用，拥有商业案例，使项目与组织的战略和投资组合方向保持一致，管理项目风险，与其他赞助人合作，注重实现利益，推荐优化成本/利益的机会，确保赞助的连续性，提供保证并提供反馈和经验教训。

项目经理

对于项目经理，发起人提供及时的决策，澄清决策框架，澄清业务优先级和战略，沟通业务问题，提供资源，建立信任，管理关系，促进道德工作。

项目利益相关者

对于其他项目干系人，项目发起人与干系人接触，管理干系人沟通，指导客户关系，指导用户管理，指导供应商管理，并在干系人之间进行仲裁。

元素

项目治理将：

• 概述项目涉及的所有内部和外部团体之间的关系
• 向所有利益相关者描述有关项目的适当信息流
• 确保对每个项目中遇到的问题进行适当审查
• 确保在项目的每个适当阶段获得项目所需的批准和指导。

良好项目治理的重要具体要素包括：

• 一个引人注目的业务案例，说明项目的对象，并指定范围内和范围外的方面
• 评估已完成项目是否符合其原始目标的机制
• 确定对项目感兴趣的所有利益相关者
• 与各利益相关者沟通的既定方法
• 由所有利益相关者商定的一组业务级需求
• 商定的项目交付物规范
• 项目经理的任命
• 项目角色和责任的明确分配
• 当前发布的项目计划，涵盖从项目启动到开发到运营过渡的所有项目阶段。
• 一个准确的向上状态和进度报告系统，包括时间记录。
• 项目的中央文档存储库
• 集中保存的项目术语表
• 管理和解决项目期间出现的问题的过程
• 项目期间识别风险的记录和沟通流程
• 关键治理文件和项目交付物的质量审查标准。

原文：https://en.wikipedia.org/wiki/Project_governance