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How to Categorise Scenarios | Baseline, Alternative, Stress, Probability, Horizon and Consequence

A scenario is a structured description of a possible situation used to explore what could happen, what might matter and how a system could respond.

Business plans, emergency exercises, climate studies, military planning, education pathways, financial stress tests and technology forecasts all use scenarios. Some represent the expected case. Others represent plausible alternatives, extremes or deliberate stress conditions.

Quick answer: how should scenarios be categorised?

  • Purpose: planning, testing, forecasting, training, comparison?
  • Baseline: expected, reference or business-as-usual?
  • Probability: likely, plausible, low-probability, unknown?
  • Horizon: immediate, short-term, long-term?
  • Assumptions: what conditions are being held or changed?
  • Trigger: what event or threshold starts the scenario?
  • Stress: normal, adverse, severe, extreme?
  • Consequence: what outcomes are explored?
  • Reversibility: can the system recover?
  • Decision relevance: what choice or preparation does the scenario inform?

This article applies How to Categorise Anything to scenarios as structured alternative worlds.

1. Separate scenario from prediction

A prediction states what is expected to happen. A scenario describes what could happen under specified assumptions.

2. Separate scenario from plan

A scenario describes conditions; a plan specifies actions under those conditions.

3. Baseline scenarios provide reference

They describe the expected or current path against which alternatives are compared.

4. Alternative scenarios explore divergence

They change selected assumptions to show how different futures can emerge.

5. Best-case scenarios explore upside

They should still remain plausible rather than becoming wish lists.

6. Worst-case scenarios explore downside

They are useful when rare failures have severe consequences.

7. Stress scenarios deliberately push limits

They test resilience under conditions more severe than routine expectations.

8. Extreme scenarios test tail exposure

Low-probability high-impact conditions can reveal hidden dependencies.

9. Probability and plausibility differ

A scenario can be plausible without having a reliable probability estimate.

10. Deep uncertainty resists precise probabilities

Where evidence is weak, scenario families can be more honest than one numerical forecast.

11. Horizon changes what matters

Short-term scenarios emphasise current capacity; long-term scenarios emphasise adaptation and structural change.

12. Triggers create entry conditions

A scenario may begin when a price threshold, policy change, weather event or technical failure occurs.

13. Trigger and cause are not identical

The trigger may start the scenario while deeper causes explain why the system is vulnerable.

14. Assumptions define scenario boundaries

Population, technology, policy, resource and behaviour assumptions should be explicit.

15. Scenario families vary one assumption at a time

This can reveal which assumptions drive outcomes most strongly.

16. Multi-factor scenarios explore interaction

Real disruptions often combine several changes at once.

17. Internal scenarios originate within the system

Leadership change, process failure and capacity expansion are examples.

18. External scenarios originate outside

Regulation, market shocks, disasters and technology shifts can alter the environment.

19. Controllable and uncontrollable scenarios differ

Some conditions can be influenced; others can only be prepared for.

20. Scenario severity should be multidimensional

Cost, safety, continuity, trust and capability may deteriorate differently.

21. Duration matters

A one-hour outage and a six-month disruption can share trigger type but require different responses.

22. Recovery path is part of the scenario

Scenarios should explore whether the system returns, adapts or settles into a new state.

23. Irreversible scenarios deserve attention

Some decisions or shocks permanently alter later options.

24. Cascading scenarios model propagation

One failure can trigger downstream disruptions through dependencies.

25. Scenario branching creates decision trees

Different choices at key points can lead to different later states.

26. Scenario comparison needs common metrics

Cost, risk, time, service and resilience should be measured consistently across alternatives.

27. Scenario quality depends on internal consistency

Assumptions should not contradict one another unless the contradiction is the point being tested.

28. Scenario realism is not enough

A realistic scenario that does not change any decision may have low planning value.

29. Decision relevance should be explicit

Each scenario should inform preparation, investment, thresholds, reserves or routing.

30. Scenario exercises can test people

Training scenarios reveal communication, coordination and authority gaps.

31. Simulated scenarios can test systems

Computational models can explore many combinations too costly to test physically.

32. AI can generate scenario candidates

Models can broaden the set of possibilities, but generated scenarios should be checked for coherence and relevance.

33. AI should not assign unsupported probabilities

Plausibility can be explored without pretending the likelihood is known.

34. A practical scenario record

  • scenario ID;
  • purpose;
  • baseline relationship;
  • horizon;
  • trigger;
  • assumptions;
  • severity;
  • probability or plausibility;
  • key events;
  • outcomes;
  • recovery path;
  • decision relevance;
  • owner;
  • version.

35. Scenario categories should improve preparedness

A useful scenario scheme reveals which futures deserve reserves, monitoring, action thresholds or strategic options.

36. The deeper idea

Scenarios are not predictions of the future. They are structured ways of preventing one imagined future from becoming the only future we can think about.

To categorise a scenario well is to know why it exists, what assumptions define it, how severe it is, what decisions it informs and what recovery looks like if it becomes real.

Final answer

Categorise scenarios by purpose, baseline, probability or plausibility, horizon, assumptions, triggers, severity, consequences, reversibility and decision relevance. Keep scenarios separate from predictions and plans, and use families of scenarios where uncertainty is too deep for one reliable forecast.

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