> For the complete documentation index, see [llms.txt](https://docs.therisk.global/organization/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.therisk.global/organization/model/method-and-limits.md). # Method and limits Method and limits of the Nexus model: the assumptions behind the teaching cascade, the four kinds of number on the model pages, live event data, and how a country replaces teaching values with its own **Where this fits:** all six sectors · Labs, Foundry and Nexus Reports · GCRI (methods), with the responsible national authority · Planning, readiness preparation and correction · method page. **In brief.** The model pages carry four kinds of number, and each is labelled differently. **Teaching values** (relation strengths, coupling, the cascade and amplification) illustrate how sectors depend on one another in a typical city-region; they are never measurements. **Illustrative applications** show what each pillar would do in a context and the measure it would answer for; they are not delivered projects. **Published benchmarks** are international evidence, each cited on the same line, never forecasts. **Specifications** (phases, gates, handoffs and identifiers) describe the designed programme year; nothing in them records that a cycle has run. A country replaces the teaching values with its own evidence through its National Model, and the responsible authority decides what follows. ## The four kinds of number | Kind | Where it appears | Status | How every page labels it | | ------------------------- | -------------------------------------------------------------------------------------- | ------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Teaching values | Relation strengths and mechanisms, coupling, the cascade, amplification, sector stress | Values for a typical city-region, chosen to teach | "Teaching values for a typical city-region, not measurements for any place." Never presented as findings for a place | | Illustrative applications | Pillar applications and the measures they answer for, on each scenario page | Designs of how the programme would respond | "Illustrative application." A measure is what the pillar would answer for, not a reported value | | Published benchmarks | Value at risk, return on resilience, avoided loss | International evidence from published studies | Cited on the same line, with the source and its scope; "not a forecast for any project or place" | | Specifications | Phases, gates G0 to G7, handoffs F01 to F14, identifiers | The designed programme year | "Designed cycle." No edition has been released and no credential issued; see the [claims and status register](https://therisk.global/trust/claims-and-status/) | ## Assumptions of the teaching cascade The cascade on the [Coupled risk](/organization/model/coupled-risk.md) page makes six deliberate simplifications. Each is the place where a real analysis must do more. | Assumption | What a real analysis adds | | ---------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | One set of strengths for a typical city-region | Strengths estimated for the place, by asset class and season, from operator data, outage records and engineering studies | | Linear propagation, capped at full stress | Thresholds and non-linear failure; interdependent networks can collapse abruptly past a critical point (Buldyrev et al., [2010](https://doi.org/10.1038/nature08932)) | | A fixed four rounds, without time | Restoration times, buffer stocks (fuel, water storage, food reserves) and the order in which services fail and return | | No geography | Hazard footprints, exposure and the location of each asset; geographic interdependency (Rinaldi, Peerenboom and Kelly, [2001](https://doi.org/10.1109/37.969131)) | | No behaviour or institutions | How operators, households and authorities respond, and the coordination failures between them (Pescaroli and Alexander, [2016](https://doi.org/10.1007/s11069-016-2186-3)) | | No uncertainty | Ranges, scenarios and the confidence of each estimate, stated with every result | Amplification has no units and no probability. It shows that the same shock does more harm in a tightly coupled set of sectors than in a loosely coupled one. It does not estimate losses, rank places or support any decision on its own. ## Published benchmarks: reading them precisely A benchmark is only as good as its scope. Two of the values shown in the model need careful wording. * **$390 billion a year.** The World Bank's Lifelines study finds that disruption to infrastructure costs households and firms in low- and middle-income countries at least $390 billion a year (Hallegatte, Rentschler and Rozenberg, [2019](https://openknowledge.worldbank.org/handle/10986/31805)). It is a cost of disruption from all causes, in those countries, not a global annual loss to natural hazards. * **30 per cent less damage.** The Global Commission on Adaptation reports that "just 24 hours warning of a coming storm or heat wave can cut the ensuing damage by 30 percent" (Global Commission on Adaptation, [2019](https://gca.org/reports/adapt-now-a-global-call-for-leadership-on-climate-resilience/)). The finding concerns warning lead time for storms and heatwaves. It supports the Labs measure of warning lead time; it is not a general estimate of avoided flood losses. The return on resilient infrastructure is supported as cited: Lifelines estimates a net benefit of $4.2 trillion over the life of new infrastructure in low- and middle-income countries, about $4 of benefit for each $1 invested (Hallegatte, Rentschler and Rozenberg, [2019](https://openknowledge.worldbank.org/handle/10986/31805)). ## Live event data On the public sites, the sector hexagon also shows how many natural events are currently open for each hazard, from NASA's Earth Observatory Natural Event Tracker (EONET), fetched when the page is viewed. These counts change by the hour. They are third-party data with their own attribution. This documentation never copies them as figures; it links the feed instead: [EONET](https://eonet.gsfc.nasa.gov/). ## From teaching values to a country's own evidence The teaching model shows what to observe together. A country that takes part replaces it with its own evidence through its **National Model**, part of the country programme described on [National Nexus](https://therisk.global/consortium/national/). The GCRI site describes the National Model in four layers: country context; systems and interdependencies across water, energy, food, health, biodiversity and infrastructure; evidence; and uncertainty. It is "never presented as a validated causal model or a forecast" ([National Model](https://therisk.global/consortium/national/model/)). In practice: 1. **Scope.** The national counterparts agree which sectors, hazards and places the model covers before the first quarter of the programme year. They own the agenda, the data and the decisions. 2. **Evidence.** The Foundry's observation and data systems hold the country's records under the country's terms; the [Labs pillar](/organization/programme/labs.md) tries new methods in the open, with their limits on record. 3. **Estimation.** Relation strengths are estimated from the country's own outage records, operator data and studies, with the uncertainty of each estimate stated. 4. **Testing.** [Nexus Universe](/organization/programme/universe.md) exercises test the model against scenarios with the operators who would act. 5. **Publication and correction.** [Nexus Reports](/organization/programme/reports.md) publishes the country profile within the scope the country agreed, and corrections become change proposals to the methods. At no step does the programme decide for the country. The relevant government, operator or licensed institution makes every decision and carries it out. ## Standards the method follows The model's vocabulary and process follow the international standards for risk management and resilience: ISO 31000 for the risk management process (ISO, [2018](https://www.iso.org/standard/65694.html)), ISO 22301 for continuity of operations (ISO, [2019](https://www.iso.org/standard/75106.html)), ISO 14090 for adaptation planning (ISO, [2019](https://www.iso.org/standard/68507.html)) and ISO 37123 for city resilience indicators (ISO, [2019](https://www.iso.org/standard/70428.html)). The [Nexus Standards](/organization/institutions/nexus-standards.md) that describe records, identifiers and figures are published on [nexustandards.com](https://nexustandards.com/standards/), whose text and status govern. ## Related pages [Coupled risk](/organization/model/coupled-risk.md) · [Applied response](/organization/model/applied-response.md) · [Scenarios](/organization/model/scenarios.md) · [The programme year](/organization/programme/the-programme-year.md) ## Sources * Buldyrev, S. V., Parshani, R., Paul, G., Stanley, H. E. and Havlin, S. (2010). Catastrophic cascade of failures in interdependent networks. *Nature* 464: 1025–1028. * Global Commission on Adaptation (2019). *Adapt Now: A Global Call for Leadership on Climate Resilience.* * Hallegatte, S., Rentschler, J. and Rozenberg, J. (2019). *Lifelines: The Resilient Infrastructure Opportunity.* Washington, DC: World Bank. * ISO (2018). *ISO 31000:2018 Risk management — Guidelines.* * ISO (2019). *ISO 14090:2019 Adaptation to climate change — Principles, requirements and guidelines.* * ISO (2019). *ISO 22301:2019 Security and resilience — Business continuity management systems — Requirements.* * ISO (2019). *ISO 37123:2019 Sustainable cities and communities — Indicators for resilient cities.* * NASA Earth Science Data Systems. *Earth Observatory Natural Event Tracker (EONET).* * Pescaroli, G. and Alexander, D. (2016). Critical infrastructure, panarchies and the vulnerability paths of cascading disasters. *Natural Hazards* 82: 175–192. * Rinaldi, S. M., Peerenboom, J. P. and Kelly, T. K. (2001). Identifying, understanding, and analyzing critical infrastructure interdependencies. *IEEE Control Systems Magazine* 21(6): 11–25. --- # Agent Instructions This documentation is published with GitBook. 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