Suggest edit — 19-01-27 Modes of Thinking worth Internalizing

Title

Name

Note

---
title: "19-01-27 Modes of Thinking worth Internalizing"
visibility: public
---

# 19-01-27 Modes of Thinking worth Internalizing

Category: [[idea-lists-upon-request|Idea Lists (Upon Request)]]

[Read the original document](https://docs.google.com/document/d/1WoGqX4WpI9c0X6SOpcGiq7yS3BI7vXwYb62hE73g7xY/edit?usp=drivesdk&sa=D&ust=1596495076807000&usg=AOvVaw1_e_nt1_dUETm0C0j2pMUC)

---

1. Latticework of Mental Models
   1. Ex., pushing How to Think into intuition
2. Systematizing Creativity
   1. Ex., Reframing, Questioning Assumptions, Abstraction & Generalization, Decomposition, Composition / Recombination, Generators, Leading Questions, etc.
3. Filter at the Intersection of Competing Worldviews
   1. Ex., Worldview Building
   2. Ex., Internalizing the conceptual style of intellectual giants
4. Intelligence Decomposition & Optimization Along All Axes
   1. Memory (Working, Episodic, Long Term)
   2. Attention (though this feels like a limitation)
   3. Having a model
   4. Abstract Knowledge Representation
   5. Ability to Generalize
   6. Learning / Adaptivity
   7. Creativity
   8. Information processing / Computation speed
   9. Goal accomplishment (ugh)
   10. Generality (over environments, tasks, representations)
5. Technical Consilience (Seeing the unity of knowledge behind every natural science) by internalizing:
   1. Information Theory (Cover / Thomas)
   2. Statistical Mechanics (Talman)
   3. Algorithmic Game Theory (Nisan)
   4. Nonlinear Dynamics and Chaos (Strogatz)
   5. Seven Sketches in Compositionality (Spivak)
   6. Mechanism Design (Borgers)
   7. Algorithms (CLRS)
   8. Neuroscience (Principles of Neural Science)
   9. Electromagnetics (Haliday / Resnick)
   10. Quantum Mechanics (Griffiths)
   11. Nuclear Physics (Krane)
   12. Chemistry (Brown)
   13. Intro Proof (How to Prove It)
   14. Analysis (Abbott)
   15. Set Theory (Halmus)
   16. Abstract Algebra (Dummit, Foote)
   17. Topology (Munkres)
   18. Category Theory (Pierce, then Awodey)
   19. Probability Theory: The Logic of Science (Jaynes)
   20. Machine Learning: A Probabilistic Perspective
   21. Computational Learning Theory (Kearns)
   22. Learning Invariant Representations (Poggio)
   23. Causality (Pearl)
   24. Computability and Logic (Boolos)
6. Thinking and Deciding (Baron)
   1. Types of thinking
   2. On the Study of thinking (meta)
   3. Rationality
   4. Logic
   5. Normative Theory of Probability
   6. Descriptive theory of probability judgment
   7. Hypothesis Testing
   8. Judgment of correlation and contingency
   9. Actively open-minded thinking
   10. Normative theory of choice under uncertainty
   11. Descriptive theory of choice under uncertainty
   12. Choice under certainty
   13. Utility Measurement
   14. Decision Analysis and Values
   15. Quantitative Judgment
   16. Moral judgment and choice
   17. Fairness and Justice
   18. Social Dilemmas: Cooperation vs. Defection
   19. Decisions about the future
   20. Risk
7. Path to Awakening / Enlightenment, ex. the intersection of:
   1. Shinzen Young’s The Science of Enlightenment
   2. Calduasa’s The Mind Illuminated
   3. Chapman’s Meaningness
   4. Wallis’s Trantra Illuminated
   5. Ingram’s Mastering the Core Teachings of the Buddha
8. Discover and internalize lost philosophical traditions
   1. Ex., Al Gazali, Mohisim, Presocratics
9. Rationalism (May be too similar to Thinking and Deciding…)
   1. As framed in SSC / LW / Sequences / HPMOR / Inadequate Equilibria
10. Turn machine learning into a fully fledged philosophy around how to think (May be a subset of technical concilience…)
   1. Bias-Variance Tradeoff
      1. Overfitting
      2. Controlling complexity
         1. Model simplicity (restriction methods)
         2. Selection methods (over features)
         3. Regularization
   2. Curse of dimensionality
   3. Ensemble Modeling
   4. Occam’s Razor (Formalized)
   5. Training vs. Generalization Error
   6. Interpolation vs. Extrapolation
   7. Smoothness
   8. VC Dimension
   9. Variance Maximization
      1. Optimizing for Volatility vs Expected Value
   10. Bayes Rule
   11. Bayes Error
   12. Exploration-Exploitation
   13. Manifolds as Data Representation

---

*Source: [Original Google Doc](https://docs.google.com/document/d/1WoGqX4WpI9c0X6SOpcGiq7yS3BI7vXwYb62hE73g7xY/edit?usp=drivesdk&sa=D&ust=1596495076807000&usg=AOvVaw1_e_nt1_dUETm0C0j2pMUC)*