Volume 2 – Handbook #II: Glossary & “Terms of Art” Definitions – Page 5
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Please read the “Pinned Post” at the top of this Substack’s Home Page, and titled Why Use Public Peer-Review to Write a Book? - “See for Yourself”.
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This post presents Page 5 for the “Terms-of-Art” definitions as shown below:
Volume 2 – Handbook #II: Glossary & “Terms of Art” Definitions – Page 5
Decision
- Step that concludes a decision-making “Process” such as John Boyd’s “OODA-Loop”, and established with regards to consequences, and under time pressure.
Decision-Maker (DM)
- Term with three different views in these Workbooks, Handbooks & Reading Notes:
o Individual facing the consequences of alternative choices within the internal scope of psychological constraints, limited time & limited resources
o A component of Herbert Simon’s “Scissors” - the interplay between the the “Decision-Maker” & the “Task Environment”
o Ole Peters’ articulation of the difference between the “Axioms, Assumptions & Hypotheses” of the individual Decision-Maker’s (DM) emulation of reality vs. those of the Disinterested Observer (DOs), e.g. researchers writing papers, provides an alternative explanation for behaviors described by the Heuristics & Bias Program.
Deductive Inference
- Decision-making “Methodology” that derives solutions from the application of a set of logical rules to a set of “Axioms, Assumptions & Hypotheses”.
Deep Learning
- A form of “AI/ML” (“Statistical AI”) focused on the use of neural networks, as contrasted to other forms of AI such as Expert Systems (“Psychological AI”) that focus on If-Then rules based on a human, conceptual understanding of reality. Large Language Models (LLMs) work by training Neural Networks to predict the next letter, word, code, sentence, paragraph, pattern, or narrative when prompted by a specific input. AI/ML/DL/LLMs do not create conceptual mind-maps of reality, but fit theory-free, multidimensional functions to the data. “Hallucinations” comes from machine-driven theory-free correlations that do not fit human conceptual mind-maps
Descriptive Research
- Empirical research describing patterns from observations as contrasted with “Prescriptive Research”, and “Predictive Research”.
Differences
- Statistical research design based on categorizing variables based on their “Differences” [d-family of statistical tools], and using tools such as “Classification”.
Directed Acyclic Graph (DAG)
- In his 2016 book “Causal Inference in Statistics – A Primer”, Judea Pearl uses “Simpson’s Paradox” to show that “certain decisions cannot be made on the basis of data alone, but instead depend on the story behind the data”. He then develops a graph theory, using DAGs [a collection of directed vertices/nodes connected by edges that do not include loops] to help see the “Real Story” behind the data, the “Process” that generates the data, or the “Process” that hides behind the data.
Disinterested Observer (DO)
- Ole Peters contrasts the model assumptions of the DO with those of the “Decision-Maker” (DM) as an alternative explanation for the behaviors described by Cumulative Prospect Theory (CPT), obviating the need for psychological narratives about the re-weighting of “Probabilities”.
“CTRI by Francois Gadenne” writes a book in three volumes, published at the rate of one two-pages section per day on Substack for public peer-review. The book connects the dots of life-enhancing practices for the next generation, free of controlling algorithms, based on the lifetime experience of a retirement age entrepreneur, & continuously updated with insights from reading Wealth, Health, & Statistics (i.e. AI/ML/LLM) research papers on behalf of large companies as the co-founder of CTRI.