Illustrative: branching futures, each branch weighted by its probability

Risk Academy, led by the Nexus Consortium · Course catalogue · Risk Science and Methods

Decision, Behaviour and Communication

Uncertainty and decision making

Uncertainty and decision making, taught twice over: as the working methods of the field and as its frontier since 2024, with labs on the Academy's tools, open data and the Nexus Standards.

Core course Frontier course

The discipline

01Core course · L2 Intermediate

Uncertainty and decision making: foundations and practice

This area covers how to describe, quantify and communicate uncertainty and how to decide under it: types of uncertainty, likelihood and confidence language, decision analysis, cost-loss reasoning and the value of information, expert elicitation, and methods for deep uncertainty such as robust decision making and adaptive pathways.

Level
L2 Intermediate
Study time
about 24 hours (planning estimate)
Status
In authoring
Prerequisites
An L1 course in the area, or an entry review

Why now

Discounting decides how far-future and catastrophic losses are weighed, especially for pandemic preparedness and other counter-cyclical investments. HM Treasury (independent review by M. C. Freeman and B. Groom), Green Book Discount Rate Review: Summary of Findings and Recommendations, 30 June 2026

Learning outcomes

  1. Define aleatory and epistemic uncertainty and the other key terms of the discipline and use them correctly in a written case
  2. Explain types of uncertainty and confidence and likelihood language with a worked example and its source
  3. Apply the discipline's method to a documented case and explain each result with its source
  4. Analyse a named case against decision analysis, stating evidence, date and limits

Modules

  1. Types of uncertainty
    • Types of uncertainty
    • Confidence and likelihood language
    • Decision analysis
  2. Cost-loss reasoning and value of information
    • Cost-loss reasoning and value of information
    • Deep uncertainty
    • Robust decision making
  3. Adaptive pathways
    • Adaptive pathways
    • Expert elicitation
  4. Lab: the discipline's method
    • A decision under uncertainty has options, states and consequences; with a probability on the state, each option has an expected cost and the cheaper one is chosen; the gap between that expected cost and the cost with the state known in advance is the value of perfect information, and it says how much a better forecast or a study is worth.
    • Work the method through a documented case and explain each result
    • Read the method's sources and stated values

Labs with Academy tools

Nexus Standards

Teaches

  • NRS-C8 Evidence, uncertainty and provenance
  • NRS-C16 Risk-informed decisions
  • NRS-M1 Method fabric: Universal mathematics and statistics

Relies on

  • NS-1 The UNI and the artefact catalogue
  • NS-16 UNI application profile (how articles, documents, code, data, APIs and interfaces carry the UNI)
  • NRS-C1 Risk vocabulary and ontology
  • NRS-C2 Risk assessment method
  • NRS-C9 Risk models, methods and validation

Assessment

  • 40%Lab recordWork the discipline's method through a documented case and explain each result with its source and date
  • 40%Case memoApply the discipline's methods to a named place or organisation, stating evidence and limits
  • 20%Key terms checkShort answers on the key terms, marked against the definitions

Delivery

  • Self-paced online, with the labs in the browser
  • Cohort study over six to eight weeks, with a weekly lab review
  • Co-delivered with a partner institution, inside its own courses

Partner on this course

  • Co-teach a module and bring a current case from your work
  • Host a cohort for your learners, with your own schedule and review
  • Contribute a lab: a dataset your institution publishes openly, or a model
  • Review the course content and its references before it opens
  • Translate the course and adapt its cases to your region

How institutions and experts take part

In the Academy LMS

Course on the learning platform
Intermediate, 4 topics
Cohort group
A community group for each cohort of learners
Achievement
Recorded on completion of the assessed work

Open now in this discipline

References

  1. HM Treasury (independent review by M. C. Freeman and B. Groom), Green Book Discount Rate Review: Summary of Findings and Recommendations, 30 June 2026
  2. Howard, Decision analysis: practice and promise, Management Science, 1988
  3. Howard, Information value theory, IEEE Transactions on Systems Science and Cybernetics, 1966
  4. Morgan and Henrion, Uncertainty: A Guide to Dealing with Uncertainty in Quantitative Risk and Policy Analysis, 1990
02Live map

Hazards and exposure in Italy

Flood water, active fires and surface heat beside population and night-time lights, over Italy.

The interactive map needs JavaScript; the layer list and sources are below.

Layers 3

Hazards and exposure

Latest available

NASA GIBS / EOSDIS · ASTER GDEM (METI, NASA) · Blue Marble (NASA) · © OpenStreetMap contributors (ODbL) · Natural Earth

Loading the latest NASA imagery.

Drag to move. Ctrl or ⌘ and scroll, pinch or the buttons to zoom; hover for the values under the pointer; reset returns to the region.

Hazards and exposure, framed on Italy.Satellite and model products as NASA publishes them, each with its own date. They are indicators, not assessments of any place.

Data: NASA GIBS · ASTER GDEM · Blue Marble · © OpenStreetMap contributors · Natural Earth · NASA EONET · U.S. Geological SurveySources and licences

  • We acknowledge the use of imagery provided by services from NASA's Global Imagery Browse Services (GIBS), part of NASA's Earth Science Data and Information System (ESDIS). Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). NASA open data
  • We acknowledge the use of imagery provided by services from NASA's Global Imagery Browse Services (GIBS), part of NASA's Earth Science Data and Information System (ESDIS). Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). NASA open data
  • VIIRS near-real-time products (flood, land surface temperature, snow cover, corrected reflectance), NASA LANCE; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). NASA open data
  • NASA Fire Information for Resource Management System (FIRMS), VIIRS 375 m active fire product, NASA LANCE; vector tiles via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). NASA open data
  • Center for International Earth Science Information Network (CIESIN), Columbia University. 2018. Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11. NASA Socioeconomic Data and Applications Center (SEDAC). Licensed under CC BY 4.0. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). doi:10.7927/H49C6VHW. CC BY 4.0
  • VIIRS Black Marble night-time lights, NASA Goddard Space Flight Center; distributed by the NASA Level-1 and Atmosphere Archive and Distribution System (LAADS DAAC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). NASA open data
  • MODIS and VIIRS atmosphere products, NASA LANCE and LAADS DAAC; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). NASA open data
  • ASTER GDEM is a product of METI and NASA (ASTER Global Digital Elevation Model V003, NASA LP DAAC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). Cite as: NASA/METI/AIST/Japan Spacesystems and U.S./Japan ASTER Science Team (2019). ASTER Global Digital Elevation Model V003. doi:10.5067/ASTER/ASTGTM.003. NASA open data
  • Made with Natural Earth. Free vector and raster map data @ naturalearthdata.com. Public domain
  • © OpenStreetMap contributors (ODbL), via NASA GIBS (coastlines, boundaries, roads and place names). Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). ODbL
  • Blue Marble, NASA Earth Observatory; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). NASA open data
  • Context on the globe and flat map: coastlines, boundaries, roads and place names from NASA GIBS reference layers (OpenStreetMap data, ODbL, © OpenStreetMap contributors); Blue Marble imagery as an optional base; 3D terrain and the elevation read-out from the ASTER GDEM elevation colour index (5 m to 200 m classes, exaggerated for legibility). Boundaries are drawn as published and imply no position on any territory.
  • Globe: MapLibre GL JS 6.9.1, BSD-3-Clause, © MapLibre contributors.
  • Live events: NASA Earth Observatory Natural Event Tracker (EONET); events are indicative and keep their original source links.
  • Live: NASA EONET Events are indicative, not authority warnings.
  • Live: U.S. Geological Survey Events are indicative, not authority warnings.
  • NASA GIBS: We acknowledge the use of imagery provided by services from NASA's Global Imagery Browse Services (GIBS), part of NASA's Earth Science Data and Information System (ESDIS).
  • River discharge forecast, 30 days with range (GloFAS via Open-Meteo): Contains modified Copernicus Emergency Management Service information 2026; not a flood warning.
  • Weather data by Open-Meteo.com (https://open-meteo.com/), licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/).
  • Optional layers (10 sources in the layer list: live, forecast, observed and context) print their provider credit on the map and here when switched on; forecasts are model output and derived indices are estimates.

Layers and dates

  • Flood water, 3-day composite: VIIRS Global Flood Product, NASA LANCE · 14 Oct 2026
  • Active fires and thermal anomalies: VIIRS NOAA-20 375 m (FIRMS), NASA LANCE FIRMS · 13 Oct 2026
  • Population density, 2020: Population: CIESIN, Columbia University, Gridded Population of the World v4.11 (2018), NASA SEDAC, CC BY 4.0 · static
  • Land surface temperature, day, 8-day composite: VIIRS NOAA-20 Level 3, 8-day, NASA LANCE · 14 Sep 2026
  • Night-time lights, daily: VIIRS Black Marble (VNP46A2), NASA LAADS DAAC · 12 Oct 2026
  • Aerosol optical depth, daily: VIIRS NOAA-20 Deep Blue AOD, NASA LANCE · 13 Oct 2026

03How learning runs

How learning runs, as a process you can run

Select a scenario and press Play: course, pathway, practice, faculty, cells and record.

04What is changing

Frontier course · L3 advanced, programme courses and standards

Uncertainty and decision making: frontier methods

The methods, data, tools and studies that changed uncertainty and decision making between 2024 and 2026, taught by reproducing them on open data.

Level
L3 Advanced
Study time
about 28 hours (planning estimate)
Status
Planned
Prerequisites
Uncertainty and decision making: foundations and practice; Working knowledge of a scripting language (Python or R) for the labs

Why now

Discounting decides how far-future and catastrophic losses are weighed, especially for pandemic preparedness and other counter-cyclical investments. HM Treasury (independent review by M. C. Freeman and B. Groom), Green Book Discount Rate Review: Summary of Findings and Recommendations, 30 June 2026

Learning outcomes

  1. Explain what changed in uncertainty and decision making since 2024 and cite the studies, data or rules behind each change
  2. Reproduce independent review recommends lower UK discount rates with a risk premium on open data and state the uncertainty of the result
  3. Evaluate a 2024 to 2026 study: its question, data, method, limits and what it changes for practice
  4. Design an analysis that combines a frontier method with the faculty's instruments, and justify each choice

Modules

  1. Independent review recommends lower UK discount rates with a risk premium
    • An independent review for HM Treasury recommends a 3 per cent real rate for up to 30 years, combining a 2 per cent risk-free rate and a 1 per cent systematic risk premium, declining with horizon, and 1.5 per cent for social insurance projects.
    • Discounting decides how far-future and catastrophic losses are weighed, especially for pandemic preparedness and other counter-cyclical investments.
    • HM Treasury (independent review by M. C. Freeman and B. Groom), 30 June 2026
  2. Destination Earth uses storyline simulations to replay extreme events
    • Destination Earth's climate adaptation digital twin produces high-resolution storyline simulations that replay past extreme weather events under different climate conditions, focusing on specific events rather than long-term averages.
    • Storylines offer a way to explore deep uncertainty about local impacts when probabilities are hard to assign.
    • Destination Earth (European Commission, ECMWF, ESA, EUMETSAT), 23 June 2025
  3. WMO gives probabilities for exceeding 1.5 degrees by 2029
    • The WMO decadal update gives an 86 per cent chance at least one year in 2025 to 2029 exceeds 1.5 degrees, a 70 per cent chance the five-year mean does, and an 80 per cent chance a new record year occurs.
    • Explicit probabilities let decision makers apply likelihood language and cost-loss reasoning rather than single projections.
    • World Meteorological Organization, 28 May 2025
  4. Reproduction lab
    • Extend the discipline's method with one frontier method and compare its results
    • State the uncertainty and the limits of the result
    • Peer review against a shared checklist

Labs with Academy tools

Nexus Standards

Teaches

  • NRS-C8 Evidence, uncertainty and provenance
  • NRS-C16 Risk-informed decisions
  • NRS-M1 Method fabric: Universal mathematics and statistics

Relies on

  • NS-1 The UNI and the artefact catalogue
  • NS-16 UNI application profile (how articles, documents, code, data, APIs and interfaces carry the UNI)
  • NRS-C1 Risk vocabulary and ontology
  • NRS-C2 Risk assessment method
  • NRS-C9 Risk models, methods and validation

Assessment

  • 50%Method reproductionReproduce one frontier method on open data and state the uncertainty of the result
  • 30%Study reviewA critical review of a 2024 to 2026 study: question, data, method, limits, what it changes
  • 20%Peer reviewReview two other learners' reproductions against a shared checklist

Delivery

  • Self-paced online, with the labs in the browser
  • Cohort study over six to eight weeks, with a weekly lab review
  • Co-delivered with a partner institution, inside its own courses

Partner on this course

  • Co-teach a module and bring a current case from your work
  • Host a cohort for your learners, with your own schedule and review
  • Contribute a lab: a dataset your institution publishes openly, or a model
  • Review the course content and its references before it opens
  • Translate the course and adapt its cases to your region

How institutions and experts take part

In the Academy LMS

Course on the learning platform
Expert, 4 topics
Cohort group
A community group for each cohort of learners
Achievement
Recorded on completion of the assessed work

Open now in this discipline

References

  1. HM Treasury (independent review by M. C. Freeman and B. Groom), Green Book Discount Rate Review: Summary of Findings and Recommendations, 30 June 2026
  2. Destination Earth (European Commission, ECMWF, ESA, EUMETSAT), Replaying Extreme Weather Events to Understand Climate Change Impacts, 23 June 2025
  3. World Meteorological Organization, Global climate predictions show temperatures expected to remain at or near record levels in coming 5 years, 28 May 2025
  4. Howard, Decision analysis: practice and promise, Management Science, 1988
  5. Howard, Information value theory, IEEE Transactions on Systems Science and Cybernetics, 1966
  6. Morgan and Henrion, Uncertainty: A Guide to Dealing with Uncertainty in Quantitative Risk and Policy Analysis, 1990
05Take part

Take part in the work

Start from who you are.

06Questions

Questions, answers and key terms

Is the course free?
Yes. Every course and learning material of Risk Academy, led by the Nexus Consortium, is free to study, self-paced or with a cohort. Ordinary participation has no fee; paid work, internships and fellowships are separate arrangements.
What does completing a course give me?
A completion record: the course, its version, the date and what was assessed, private by default and shared by you. It stacks into discipline, department and programme achievements. A record states what was studied and assessed and does not certify competence; it is not a licence, a professional title or an employment status.
Where do the data and the places come from?
Every faculty teaches on a twin of a real place built from open data; each layer carries its provider, licence and date, and every result on the Academy states its source. Maps and figures are indicators for teaching, never an assessment of a place.
How do I take part beyond the course?
Form a competence cell of three to seven people, study a shared pathway, take a supervised assignment in the National Working Groups, or bring a cohort from your institution. For organisations.

Key terms

Term

Aleatory and epistemic uncertainty

Uncertainty from natural randomness and uncertainty from limited knowledge.

Term

Deep uncertainty

A situation in which parties cannot agree on models, probabilities or the value of outcomes.

Term

Value of information

The expected improvement in a decision from obtaining more information.

Term

Robust decision making

Choosing options that perform acceptably across many plausible futures.

ReportsContribute

Contribute evidence from this page.

Reports 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.

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