Chapter 21 — Human factors and cyber policy: system models
Lesson map
Lesson 21 published. Map, example, EX061.
flowchart TD
N0["Learning outcomes"]
N1["21.1 A process model"]
N2["21.2 Delays and losses"]
N3["Lab — Reporting funnel from noticing to reply"]
N4["21.3 Prioritising"]
N5["Synthesis"]
N0 --> N1 --> N2 --> N3 --> N4 --> N5
N5 --> EX["Worked example"]
EX --> Q["Exercise EX061"]
Logical drawing
flowchart LR
N0["Learning outcomes"]
N1["21.1 A process model"]
N2["21.2 Delays and losses"]
N3["Lab — Reporting funnel from noticing to reply"]
N4["21.3 Prioritising"]
N5["Synthesis"]
N0 --> N1 --> N2 --> N3 --> N4 --> N5 --> Q["EX061"]
[Learning outcomes]
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v
[21.1 A process model]
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[21.2 Delays and losses]
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v
[Lab — Reporting funnel from noticing to reply]
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v
[21.3 Prioritising]
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v
[Synthesis]
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v
[Worked example] --> [Exercise EX061]
Learning outcomes
You will (1) model the reporting process as a system with inputs, steps and feedback; (2) identify delays and loss points; (3) use the model to prioritise improvements.
21.1 A process model
Nadia models Ashcombe's reporting process as a system. The input is a suspicious email arriving in a staff inbox. Steps follow: the person notices something odd, decides whether to report, finds how to report, sends the report, IT reviews it and IT replies. Feedback from IT shapes whether the person reports next time. Drawing the process as boxes and arrows helps her see that reporting is not a single act but a chain, and any weak link in that chain can stop a warning from reaching IT.
21.2 Delays and losses
At each step, some reports are lost or delayed. Some staff do not notice cues; others notice but decide not to bother; some cannot find the reporting address; and IT, staffed by two people, may take a day to reply. Nadia estimates from her survey and IT records how many potential reports survive each step. The lab multiplies the step rates to estimate how many of every hundred suspicious emails become reports with a reply. The result shows that the decision step loses the most.
Lab — Reporting funnel from noticing to reply
# Reporting funnel for 100 suspicious emails (synthetic step rates).
steps = [("notice", 0.70), ("decide to report", 0.45), ("find how to report", 0.85), ("receive reply", 0.90)]
n = 100.0
for s, r in steps:
n2 = n * r; print(f"{s}: {n:.0f} -> {n2:.0f} (lose {n - n2:.0f})"); n = n2
print(f"replied reports per 100: {n:.0f}")
Run with python3 lab_funnel.py. Verified output:
notice: 100 -> 70 (lose 30)
decide to report: 70 -> 32 (lose 38)
find how to report: 32 -> 27 (lose 5)
receive reply: 27 -> 24 (lose 3)
replied reports per 100: 24
21.3 Prioritising
The model helps Nadia prioritise. Improving the decision step, by making staff confident that reporting is welcome even when unsure, would raise reports more than improving noticing through training. Adding a report button would help the finding step. Faster IT replies would strengthen feedback for future reporting. She ranks improvements by the number of extra reports each would produce per unit of effort. A system model shifts attention from vague calls for awareness to specific steps where people need support. Managers agree the ranking.
Worked example
Nadia models reporting as a chain, estimates losses at each step and prioritises the decision step.
Synthetic case. Rates are synthetic.
Errors and limits
Step rates are rough estimates. Steps interact.
Exercises
- EX061 — Chain view. Why model reporting as a chain? Rubric: 3 = any weak step can stop a warning;; 2 = weak link;; 1 = single act;; 0 = none.
- EX062 — Funnel survivors. How many of 100 suspicious emails become replied reports in the lab? Rubric: 3 = about 24;; 2 = 24;; 1 = vague;; 0 = none.
- EX063 — Biggest loss. Which step loses the most? Rubric: 3 = the decision to report;; 2 = decision;; 1 = noticing;; 0 = none.
Synthesis
A system model shows where reports are lost and which step to improve first.
Continue studying — chapter 21 of 40
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