2.3. Study stakeholders!
2.6. People do not understand how much (amount) data is representative of the matter (especially if it is consistent).
People like similarity between cause and effect both in nature and in size. People feel that correlation equals causality.
2.12. Give explicit probabilities to events, and prefer probabilities to grades. (How likely are you to buy a game/film, not “how good it is”.)
2.15. Trick to assess quality of information. If the opposite had occurred, would I have been surprised?
2.16. Analyst’s checklist
- Define the problem
- Generate hypotheses
- Collect information
- Evaluate hypotheses
- Select the most likely hypothesis
- Monitor quality in hindsight
- Sensitivity testing. What would change the assumption output?
- How do you find that this is no longer true?
- Are you attributing your own characteristics to other people?
2.21. Read your old documents!
2.22. Use scientific method.
- Situational logic
- similar to imperative programming. Cause and effect are unique and concrete. Predict forward by extrapolation, or backward by reverse execution.
- Theoretical logic
- use generalisation to obtain for the same goals. (Similar to functional programming?)
- Logic by analogy
- machine learning? pattern transfer?
- Logic by data
- kinda same?
- Data on variable value in model instances.
- Additional variables.
- Trustworthiness of variable data metadata.
- Variable coupling medatada.
3. Summary for myself
Intelligence people’s work consists of writing reports. This is one of the reasons why literature is considered such an important subject in school. How many books do these people create each year? How much is declassified? How much is destroyed? How can this be compared to the FSB, GRU, and SVR? Who gives them assignments?
It is impressive how much the way a human society works resembles how a living organism works, consisting of different organs.
It is hard to make yourself think about problem solving directly.