Chapter 3 — Physics for computing

Lesson map

Lesson 03 published. Map, example, EX007.

flowchart TD
  N0["Learning outcomes"]
  N1["3.1 Environment and error"]
  N2["3.2 Signal and noise"]
  N3["3.3 Physical security and people"]
  N4["Synthesis"]
  N0 --> N1 --> N2 --> N3 --> N4
  N4 --> EX["Worked example"]
  EX --> Q["Exercise EX007"]

Logical drawing

flowchart LR
  N0["Learning outcomes"]
  N1["3.1 Environment and error"]
  N2["3.2 Signal and noise"]
  N3["3.3 Physical security and people"]
  N4["Synthesis"]
  N0 --> N1 --> N2 --> N3 --> N4 --> Q["EX007"]

[Learning outcomes]
    |
    v
[3.1 Environment and error]
    |
    v
[3.2 Signal and noise]
    |
    v
[3.3 Physical security and people]
    |
    v
[Synthesis]
    |
    v
[Worked example] --> [Exercise EX007]

Learning outcomes

You will (1) relate physical environments to human error; (2) apply signal and noise ideas to alarms; (3) consider physical security in human terms.

3.1 Environment and error

Aiko visits Greyford's control room. Lighting, noise from equipment, shift length and screen glare all affect how accurately operators read displays. Physics explains some of these effects: glare reduces contrast, and background noise masks audible alarms. Human errors in such conditions are often predictable consequences of the environment rather than personal failings. She notes that improving lighting and alarm tones may reduce mistakes more cheaply than retraining staff who are already competent. She lists the conditions she observes in a short table and shares it with the control room supervisor for comment.

3.2 Signal and noise

In physics, signal-to-noise ratio measures how clearly a signal stands out. Aiko applies it to alarms and to security alerts. If operators see hundreds of low-value alarms per shift, the important ones are lost in noise, and staff learn to acknowledge without reading. The same happens with security warnings on screens: frequent warnings teach people to click through. Herley (2009) argued that warnings with mostly false positives can be rationally ignored. Raising the signal means removing low-value alerts rather than asking people to concentrate harder.

3.3 Physical security and people

Aiko examines physical access. Doors to the control room use badges, but staff often hold them open for colleagues carrying equipment. She notes that physics of doors matters: a heavy door that closes slowly invites tailgating. A design change, such as a faster closer and a second entrance for deliveries, removes the pressure to hold the door. Physical controls work best when they match the tasks people perform, which is the same lesson as for digital controls. Greyford's facilities manager agrees to trial the faster closer at one site.

Worked example

Aiko links control room conditions to predictable errors, uses signal-to-noise to explain alarm fatigue and redesigns door behaviour to reduce tailgating.

Synthetic case. The control room is synthetic; Herley (2009) is real.

Errors and limits

Environmental fixes need ergonomic expertise. Alarm reduction must not remove critical alarms.

Exercises

• EX007 — Predictable errors. Why are many control room errors predictable?
  Show marking criteria

  Rubric: 3 = they follow from lighting, noise, glare and shift length;; 2 = environment;; 1 = carelessness;; 0 = none.
• EX008 — Alarm noise. What happens when operators see hundreds of low-value alarms?
  Show marking criteria

  Rubric: 3 = they acknowledge without reading;; 2 = fatigue;; 1 = vague;; 0 = none.
• EX009 — Door redesign. How can a door design reduce tailgating?
  Show marking criteria

  Rubric: 3 = faster closer and a separate delivery entrance;; 2 = design;; 1 = signs;; 0 = none.

Synthesis

Physical environment and signal quality shape human error, so engineers should fix conditions before blaming people.

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