Cross-sector scenario design
Scenarios are built with a trigger, severity, timing and conditions, then traced through the dependencies of energy, water, communications, finance and public services, with every assumption written down.
GCRI · 系统性风险管理
情景设计与压力测试
Scenario design and stress testing are the method this platform stewards for the network. They turn severe but plausible events into structured tests of plans, buffers and dependencies. In 系统性风险 management they are used to probe cross-sector cascades, to find breaking points and to compare preparedness across institutions on common assumptions.
02申请
What scenario design and stress testing does here.
Scenario design and stress testing construct plausible, severe future conditions to examine how 系统 and institutions would perform. They complement probabilistic analysis where data are scarce or futures are deeply uncertain. In risk management and 系统性风险 and methods and modelling, stress tests reveal vulnerabilities that arise when several risks materialise together. They inform capital, contingency and coordination plans across sectors. The canonical description is on STEM Commons.
Reverse stress testing
Starting from a defined failure, such as a service stopping, analysts search for combinations of events that would cause it and document which ones are plausible.
Second-round effects
Tests add the reactions of institutions during a crisis, such as asset sales, supply hoarding or service cuts, to see how responses amplify or dampen the initial shock.
03模拟器
存量、流量、延迟和一个临界阈值。
两个存量，即压力和 适应能力，由一个增强的增长回路、一个延迟的平衡响应以及压力超过能力时产生的损害相连；越过阈值后，系统可能发生突变。这是使用明示示例数值的教学模型，不是预测。
控制措施
- 压力增长
- 每年 6.0%
- 能力在降低压力方面的效力
- 每年 4.0%
- 对感知压力的投资响应
- 20%
- 感知延迟
- 4.0 年
- 压力的临界阈值
- 60 项指数
- 越过阈值后的放大
- 2.0 倍
- 模拟年数
- 50 年
读数
- 峰值压力
- 113 指数
- 期末能力
- 186 指数
- 超过阈值的年数
- 15.8 年
数字、方法与来源
| 年份 | 压力 | 能力 | 感知 |
|---|---|---|---|
| 0 | 20.0 | 50.0 | 20.0 |
| 4 | 23.3 | 44.9 | 21.2 |
| 8 | 27.2 | 42.2 | 24.0 |
| 12 | 31.7 | 42.3 | 27.7 |
| 16 | 36.8 | 45.5 | 32.2 |
| 20 | 42.3 | 51.5 | 37.2 |
| 24 | 47.9 | 60.3 | 42.6 |
| 28 | 53.2 | 71.3 | 48.0 |
| 32 | 57.8 | 84.3 | 53.1 |
| 36 | 66.4 | 98.4 | 58.4 |
| 40 | 82.7 | 115.9 | 69.7 |
| 44 | 97.8 | 139.7 | 83.8 |
| 48 | 109.1 | 169.4 | 97.3 |
方法
- 系统动力学 Forrester (1961), Industrial Dynamics, MIT Press; Sterman (2000), Business Dynamics
- 临界转变与临界点 Scheffer and others (2009), Early-warning signals for critical transitions, Nature 461
- 因果回路图 Sterman (2000), Business Dynamics, McGraw-Hill
- 不确定性扇形图（参数蒙特卡罗） Spiegelhalter, Pearson and Short (2011), Visualising uncertainty about the future, Science 333
- 单因素敏感性（龙卷风图） Saltelli and others (2008), Global Sensitivity Analysis: The Primer, Wiley
数据
No observed data enters this model: it runs on the teaching values set by the controls, to be replaced with a programme’s own evidence.
贯穿六大支柱，由 Nexus Consortium 牵头
- 实验 实验室
- 构建于 Foundry 中的系统性风险管理
- 检验地点 Universe 中的系统性风险管理
- 编入 Standards 中的系统性风险
- 发表于 Reports 中的系统性风险管理
- 授课于 Academy 中的风险科学与方法学院
04数据
该方法使用的输入。
Datasets and records used by scenario design and stress testing on this platform, with the role each plays and the label its values carry. Sources are listed in 来源登记册.
| 数据 | 角色 | 标签 |
|---|---|---|
| NGFS 气候情景 | Reference physical and transition pathways | 模型生成 |
| Reasonable worst-case planning scenarios | Civil contingency assumptions | 已发布统计 |
| Historical event reconstructions | Calibration of severity | 观测 |
| Dependency inventories | Propagation structure | 已声明 |
05方法与证据
How scenario design and stress testing is modelled, measured and tested.
模型、其假设、数据和不确定性，以科学家或工程师会核查的形式呈现。结构遵循已发表的方法。所取数值仅为示意，不是研究发现。
模型、方程与假设
The model structure used for scenario design and stress testing, the assumptions it makes and how each is tested.
要素 i 的风险
Ri = f(Hi, Ei, Vi, Ci)危害、风险暴露、脆弱性和应对能力预期年度损失
EAL = ∑k pk · Lk对年概率为 pk耦合系统
xt+1 = A xt + B ut + εtA carries the couplings, u the shock, ε the error| 假设 | 为何制作 | 如何检验 |
|---|---|---|
| 在一次事件中，危害与暴露相互独立 | 使损失积分保持可计算 | 与基于联合记录拟合的 copula 进行比较 |
| 耦合在工作点附近是线性的 | 让冲击通过 A 传播 | 在线性范围之外施加扰动并比较 |
| 脆弱性曲线可以在不同地点之间迁移使用 | 很少有本地曲线可用 | 在有观测损失的地方据此进行验证 |
- 输入
- 带溯源信息的版本化数据集
- 成果
- 分布，绝不是单一数字
- 代码
- 连同模型卡和测试一起发布
- 复现
- 相同的数据、代码、环境和随机种子
FAIR 指导原则 · GO FAIR 和 FORCE11W3C PROV · 万维网联盟关于一致处理不确定性的指南 · IPCCCEOS 分析就绪数据 · 地球观测卫星委员会
06验证
结果如何核查。
Scenarios are reviewed for plausibility, internal consistency and severity relative to history. Exercise outcomes are compared with actual events when they occur.
| 核查 | 如何开展 |
|---|---|
| Plausibility review | Scenarios are reviewed by domain experts for internal consistency and plausibility before use |
| Severity benchmarking | Scenario severity is compared with historical events and published reference scenarios |
| Model coverage | The share of mapped critical dependencies represented in the test is reported with the results |
Scenario choice can reflect prevailing assumptions and miss novel combinations. Results depend on how participants and models represent responses.
08管理责任
由谁维护该方法。
系统性风险 Management stewards scenario design and stress testing for the Consortium and reviews how it is applied across platforms.
09问题
问题与解答。
What are the limits of scenario design and stress testing in this field?
Scenario choice can reflect prevailing assumptions and miss novel combinations. Results depend on how participants and models represent responses.
结果如何核查？
Scenarios are reviewed for plausibility, internal consistency and severity relative to history. Exercise outcomes are compared with actual events when they occur.
本页面上的数值是真实的吗？
The data names, roles and checks are real descriptions of practice. The canvas shows a design of the method’s use, not a run; no result is reported here.
该方法还应用于何处？
维持社会运转的 规范方法页面 列出应用该方法的每个平台。
报告贡献
从本页贡献证据。
报告 is the Consortium’s quarterly publication. What you know about this subject can become evidence, a case or a correction in the next edition, credited to you and published under CC BY 4.0 where it is your own work.
GCRI系统性风险管理情景与压力测试方法与技术组合与准备度
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