如何分析未来的安全威胁(4)。情景与战争

这篇文章主要讨论战争的情景。它首先解释了为什么情景需要相互排斥。然后,它提供了构建处理战争的情景的逻辑模板。最后,它提供了一个最新的关于叙利亚的情景的书目。迈向分析未来安全威胁和政治风险的操作方法 (1) 分析方法...

Everstate的特点

The initial variables chosen to start building our scenario are the five most important variables of our model, according to Eigenvector centrality, as explained in the article “Revisiting influence analysis”. We shall now choose values for each criterion. Consistency is then checked, but only for the variables that are linked. As we aim at finding …

动态风险和不确定因素的建模 (1) : 绘制风险和不确定因素图

(本文是2011年11月以 "创建预见和预警模型 "为题发表的原始文章的全面更新版。绘制动态网络图(一)》)。绘制风险和不确定性是正确预测和管理风险和不确定性的正确过程的第二步。 这个阶段从建立一个模型开始,一旦完成,它将描述和解释手头的问题或议题,同时允许预测或预知。换句话说,随着第一步的结束,你已经选择了一个风险,一个不确定因素,或一系列的风险和不确定因素,或一个值得关注的问题,并有其适当的时间框架和范围,例如,什么是风险和不确定因素,以...

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未来威胁分析中的行为者和因素(3)--乌克兰的危机

本文重点讨论了处于预见和预警过程核心的第三个分析性挑战,即行为者和 "因素",或者说是变量,经常混在一起。以乌克兰正在发生的危机为例,本系列的第一篇文章解释了如何绘制战略前瞻和预警问题,特别是如何从因素到变量,第二篇文章强调了尽可能客观地定义和命名与问题相关的行为体的重要性,并提出了相关的方法。正如我们在上一篇文章中所回顾的那样,在绘制乌克兰危机未来演变图的第一步中,因素或更确切地说,变量和行为者都将是...。

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未来安全威胁分析中的行为者和因素(1)--乌克兰的危机

This series of articles deals with the core and basis of the foresight and warning analytical process, explaining it while stressing three most common challenges analysts and participants to workshops face: identifying factors correctly (this article); specifying actors objectively (2-); overcoming an inadequate mix of “actors and factors” (3-). Practical ways forward will be suggested. The example that will be used as case study throughout those three posts is the 2013-2014 crisis in Ukraine, with, as corresponding strategic foresight and warning (SF&W) question, “What are the possible futures for the Ukrainian crisis over the next two years?” Compared with our previous methodological series, these posts may seem to address more basic problems. However, as workshop after workshop, participants, be they …

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如何分析未来的安全威胁(3)。作为一个有机生命系统的情景

This article is the third of a series looking for a methodology that would fulfill the challenging criteria demanded by our time. We shall now focus on scenarios, which are a way to simulate how the actors we defined and described during the previous step interact, not only among themselves but also with their environment, up until the end of the chosen timeframe. Using the precedent post’s game of chess analogy, with scenarios we imagine the various ways the game may “end”. Towards an Operational Methodology to Analyse Future Security Threats and Political Risk (1)Methodology to Analyse Future Security Threats (2): a Game of ChessHow to Analyse Future Security Threats (3): Scenarios as an Organic Living SystemHow to Analyse Future Security Threats (4): Scenarios …

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分析未来安全威胁的方法(2):一盘棋

This article is the second of a series looking for a methodology that would fulfill the challenging criteria demanded by our time. Previously, we saw that a single “story” initially told at a general level, the political dynamics that are at the core of a polity, could be used to build the very specific model needed to answer a strategic foresight and warning (national security) question or a political risk interrogation. Very practically, how shall we do that? How are generic dynamics going to help us with our task? How can we proceed? This is what we shall see now. Related Towards an Operational Methodology to Analyze Future Security Threats and Political Risk (1) Methodology to Analyze Future Security Threats (2): a …

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谜题:2030年全球趋势中的奇怪的相似性

Global Trends 2030 compares our current transition period with 1815, 1919, 1945 and 1989. Yet we have not known recently any global systemic war. Thus why choose such a comparison? What could explain such a puzzling choice and what could we learn from it, for our understanding of the world and its potential future(s)?

动态网络中的变量、价值和一致性

在这篇文章中,我们解释并讨论了使我们能够为Everstate--或为任何国家或所选择的问题--设定标准的方法背景,如帖子中所例举的"Everstate的特点."同时,我们还解决了一致性的问题。

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重新审视影响力分析

Once variables (also called factors and drivers according to authors) have been identified – and in our case mapped, most foresight methodologies aim at reducing their number, i.e. keeping only a few of those variables.

Indeed, considering cognitive limitations, as well as finite resources, one tries obtaining a number of variables that can be easily and relatively quickly combined by the human brain.

The problem we here face methodologically is how to reduce this number of variables at best, making sure we do not reintroduce biases or/and simplify our model so much it becomes useless or suboptimal.

Furthermore, considering also the potential adverse reactions of practitioners to complex models, being able to present a properly simplified or reduced model (however remaining faithful to the initial one) is most often necessary.

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