They go over many biases and attentional phenomena, tools to prevent or mitigate, visualization techniques to address problems (timelines, etc.) with several examples.
It also goes into explaining the context that practitioners evolve in a context where information can be scarce, contradictory either by happenstance or by design by actors who want to confuse.
Here's a RAND report titled "Assessing the Value of Structured Analytic Techniques in the U.S. Intelligence Community".
- [0]: https://www.rand.org/content/dam/rand/pubs/research_reports/...
The table of contents is here https://www.cia.gov/library/center-for-the-study-of-intellig...
A brief look at the table of contents suggest that the whole book is worth looking at.
https://www.cia.gov/library/center-for-the-study-of-intellig...
I’d like to share a tangential thought in case others have noticed the same. One thing I’ve found odd with the CIA and “Intelligence” community is they seem to use the terms “Information” and “Intelligence” interchangeably. Labeling their Information as Intelligence, I would think introduces cognitive bias. Perhaps Authority Bias or another.
Going with the DIKW paradigm:
- Data
- Information (The symbols and words we assign to the Data)
- Knowledge (Which I’ll define as a storage of Information with Metainformation attached to it. Kind of like a computer filesystem. The actual file is Information and has Metainfo like Source, Timestamp, filetype, and other contextual properties which is useful for processing and thinking about during decision-making.
- Wisdom (Which I’ll defined as the process of consciously running Knowledge through your Mind and Emotions and Third-party Cognitive Fallacy Checklists) and then drawing a Conclusion.
When they say “Intelligence-gathering”, really they mean “Information-gathering.”
Long story, short: if I ran the CIA, I’d change the name to “Central Information Processing Agency”
- Noise (illegible)
- Data (legible symbols)
- Information (relevant, salient data)
- Knowledge (integrated, coherent, true information) - "a justified, true belief"
- Understanding (an anticipatory, predictive, analytical model)
- Wisdom (an autoregressive, self-awareness like term in the predictive model)
I would put data first, with signal and noise components determined second. Although this probably some subset of the information in the structure you present?
That seems to fit regular people and regular news these days. Too much "information"; not enough actual information; and too many biases.
Our wiki on GitHub also has related reading, including guides from other countries: https://github.com/twschiller/open-synthesis/wiki/Reading-Li...