Probably actually reading/writing

1 Currently writing

Not all of it is published yet.

1. Ecology of agency

   1. What even is “agency”?
   2. Agency under bounded compute and information
   3. What are human values?
   4. Embedded agency Not what I thought. done.
   5. Homunculi all the way down I think this one led me nowhere
   6. mechanised causal graphs for understanding decision theories
   7. Boundaries and blankets
   8. Intrinsic motivation
   9. Utility as a local linearization of fitness
   10. What use is utility?
   11. Opponent shaping needs a rewrite to be less about Advantage Alignment.
   12. Big history
   13. Intelligence in big history Paused for now — this doesn’t seem fruitful
   14. Human collective agency
   15. Coalition games
   16. Generic multi-agent systems
   17. Multi-scale agency
   18. Commitment
   19. Empowerment
   20. Cooperation through uncertainty

2. Run-ups at bounded cognition. Three notebooks I keep starting and not finishing on the same underlying problem — what cognition can be when both information and compute are bounded by the agent’s substrate.

   1. Agency under bounded compute and information — the foundational why-must-the-agent-compress angle.
   2. Homunculi all the way down — compute split across self, other, and reflective sub-models. I think this one is going nowhere
   3. Generalized economics of compute and cognition — compute as a substitutable factor of production.
   4. Making good intertemporal decisions — naïve/sophisticated/resolute choosers as a bounded-cognition story.

3. Epistemic community design

   1. Knowledge collapse and the epistemic commons
   2. Learning from the madness of crowds
   3. Scientific community
   4. Messenger shooting
   5. Steps to an ecology of mind
   6. Epistemic bottlenecks is probably in this series too.
   7. Truth/effectiveness heat pumps
   8. Formal models of science-as-enterprise
   9. Strategic ignorance
   10. Public sphere business models

4. Collective truth-finding and its mechanism design — extracting reliable belief and decision from heterogeneous, strategic, or boundedly-rational populations, and the institutional / algorithmic machinery for doing so.

   1. Learning from the madness of crowds — formalisms for extracting truth from biased, strategic crowds
   2. Groupthink and the wisdom of crowds — diversity dividends, information cascades
   3. Bayesian epistemics — proper scoring rules, peer prediction, Bayesian truth serum
   4. Calibration — scoring rules from the forecast-evaluation side
   5. Voting systems / social choice — preference aggregation, impossibility results
   6. AI alignment to collective values — RLHF / ERM as implicit voting, differentiable social choice
   7. Utopian governance — sortition, futarchy, liquid democracy, mechanism composition
   8. Civic tech and AI-mediated governance — bridging-based aggregation, deliberative platforms

5. AI Safety and society

   1. AI Safety movement design
   2. Science communication for ML
   3. Bay-area-flavoured Rationalists and the public sphere
   4. Gradual disempowerment

6. Cooperative AI. Building machines that work with us and each other.

   1. Value/reward learning
   2. Debate and interactive verification
   3. Learning with theory of mind
   4. Opponent shaping
   5. Coalition games
   6. Civic tech & AI mediation
   7. Delegated agent governance
   8. Bargaining with successors
   9. Intergenerational game theory
   10. Making good intertemporal decisions
   11. Markov decision problems
   12. Empowerment
   13. Cooperation through uncertainty
   14. Open-source game theory

7. Ethics. I’m sceptical of utilitarianism as an ultimate moral framework (although at the margins it is fine). Nonetheless, I need to make decisions under uncertainty. What to do?

   1. Steps to an ecology of mind

   2. Diversity as an end

   3. Optionality as an end

   4. Moral orbits.

   5. Utility as a local linearization of fitness

   6. What use is utility?

   7. Computational morality

   8. Where does utilitarianism even come from?

      1. Where do preferences come from
      2. The moral wetware

   9. Starfish problems

   10. Ethical consumption

   11. Prefigurative politics

   12. Making good intertemporal decisions

   13. Intergenerational game theory

8. Coalitional fairness

   1. Shapley values
   2. Am I getting fucked?
   3. When does the GDP measure importance
   4. Coalition games
   5. Economics of growth
   6. Bargaining with successors
   7. Intergenerational game theory
   8. Making good intertemporal decisions

9. AI Safety Esp gradual disempowerment

   1. Generalized economics of compute and cognition

      1. Quis computat?

   2. Economics of cognitive and labour automation

   3. Domestication of humans

   4. Causal agency

      1. Causal hierarchy
      2. Causal inference in learning to act

   5. Graphical model / ML decoder ring

   6. Causal abstraction

   7. Learning with theory of mind

   8. History-based RL generalises RL from POMDPs to even less tractable settings.

   9. Cosmic decision theories

   10. DIY Generative AI

   11. Bargaining with successors

   12. Markov decision problems

   13. Nearly sufficient statistics and information bottlenecks

   14. Empowerment

   15. Open-source game theory

10. Economics of innovation, progress, automation The nexus where growth theory, the economics of cognition, and the political economy of AI meet.

    1. Economics of growth

    2. Generalized economics of compute and cognition

       1. Quis computat?

    3. Economics of cognitive and labour automation

    4. Knowledge collapse and the epistemic commons

    5. Economic dematerialization

    6. When does the GDP measure importance

11. Sciences of the Artificial

    1. ML benchmarks
    2. Snowmobile or bicycle?

12. Social license for AI Safety

    1. Science communication for ML
    2. AI Safety movement design

13. Yak shaving

    1. Generative AI workflows and hacks 2026
    2. Editing images with AI
    3. Generative art with language+diffusion models
    4. Front-end clients for AI image models
    5. Running LLMs locally on a Mac
    6. AI search
    7. Vibecoding Apple apps
    8. Fine-tuning danbot

14. Evolving a just society

    1. A social divide I’ve seen a lot recently: people who value cheap signalling highly versus those who view it negatively.

    2. Goodhart coordination

    3. Structural problems are hard — let’s do training programs

    4. Emancipating my tribe: the cruelty of collectivism (and why I love it anyway)

    5. Collective care

    6. Social calendaring

    7. Psychological resilience

    8. Nationalism

    9. Sovereign community compute

       1. Technical implementation
       2. DIY Generative AI

    10. Local social platforms

        1. Technical implementation

    11. Grass-roots friendly societies

        1. Hacking financial regulation for community mutual aid

    12. Delegated agent governance

    13. So you’ve joined a union

15. Authoritarianism and resilience against it

    1. Authoritarian drift in Australia
    2. Returns to Leviathan
    3. Reducing state spying
    4. Social infosec
    5. Prepping
    6. Psychological resilience
    7. Movement design

16. Complexity theory revival (Santa Fe Institute detritus)

    1. Categorical systems theory
    2. Computational mechanics
    3. Edge of chaos
    4. Open-ended intelligence
    5. Dynamical systems

17. Human superorganisms

    1. Moral orbits.
    2. Revisit Probability collectives
    3. Movement design
    4. Returns on hierarchy
    5. Effective collectivism
    6. Alignment
    7. Institutions for angels
    8. Institutional alignment
    9. Beliefs and rituals of tribes
    10. Where to deploy taboo
    11. The Great Society will never feel great; it’ll merely be better than the alternatives
    12. Player versus game
    13. Something about the fungibility of hipness and cash
    14. Monastic traditions
    15. Returns to scale in technological society

18. Foundation models and their world models

    1. Causal/Bayesian inference in foundation models
    2. Neural net reasoning and symbolic mathematics

19. Bayes-meets-neural-nets

    1. Italian school Predictive Bayes

    2. Singular Learning Theory

    3. Continual learning.

    4. Imprecise Bayesianism

    5. Bayes about logical statements

    6. Probability, Rényi-style

    7. Quantum probability

    8. MaxEnt inference

    9. Multivariate information decomposition

    10. Evolution strategies

        1. for neural networks

    11. Nearly sufficient statistics and information bottlenecks

20. Other ProbML stuff

    1. Approximate conditioning
    2. What even are GFlownets?
    3. Reality gap
    4. Nested sampling
    5. Elliptical belief propagation

21. Classification and society series

    1. Constructivist rationalism
    2. Affirming the consequent and evaporative tribalism
    3. Classifications are not very informative
    4. Adversarial categorization
    5. AUC and collateral damage
    6. Bias and base rates
    7. Decision theory
    8. Decision theory and prejudice
    9. Is academic literary studies actually distinct from the security discipline of studying side-channel attacks?

22. Shouting at each other on the internet series (Teleological liberalism)

    1. Modern politics seems excellent at reducing the vast spectrum of policy options to two mediocre choices, then arguing about which is worse. What is this tendency called?
    2. The Activist and decoupling games, and game-changing
    3. Lived evidence deductions and/or ad hominem for discussing genetic arguments.
    4. Diffusion of responsibility — is this distinct from messenger shooting?
    5. Iterative game theory of communication styles
    6. Invasive arguments
    7. Coalition games
    8. All We Need Is Hate
    9. Speech standards
    10. Pluralism ✅

23. Human learner series

    1. Which self?

    2. Is language symbolic?

    3. Our moral wetware

    4. Is “is” “ought”?

    5. Morality under uncertainty and computational constraint

    6. Superstimuli

    7. Clickbait bandits

    8. Correlation construction

    9. Moral explainability

       1. Burkean conservatism is about identifying when moral training data is out-of-distribution.
       2. Something about universal grammar and its learnable local approximations versus universal ethics and their learnable local approximations. Morality by template; the computational difficulty of moral identification. Leading by example of necessity.

    10. Righting and wronging

    11. Akrasia in stochastic processes: what time-integrated happiness should we optimise?

    12. Snowmobile or bicycle?

    13. Comfort traps ✅ Good enough for now

    14. Myths ✅ a few notes are enough

24. When is computation “statistical”? I mean this in the sense that, as in statistical mechanics, we know some bulk statistics of a population of solutions even when we can’t do the calculations for everything (like: air pressure doesn’t require simulating every molecule). It seems that machine learning sometimes behaves like this in a certain sense. I’m not sure of the scope of this idea — maybe I’m reinventing computational mechanics — so let’s use some examples to flesh it out:

    1. Trading equities. We can’t know every trade, but we can price options well under no-arbitrage assumptions, even though traders’ calculations can be far more complex than ours. No-arbitrage assumptions aren’t strictly true, but the returns from extra complexity to find arbitrage opportunities seem to diminish with compute, so in the wash it’s pretty similar.
    2. Statistical mechanics of statistics
    3. Scaling laws: we can’t know the exact computations an LLM will do, but we can predict its performance remarkably well given a data-parameter-train-compute budget.
    4. Algorithmic statistics and pseudorandomness study the statistical behaviours of some classes of algorithms, where they become near-indistinguishable from randomness in technical senses.
    5. Computational mechanics
    6. Neural net reasoning and symbolic mathematics
    7. …

25. How to do house stuff (renovation etc)

26. Learning in context

    1. Interaction effects are what we want
    2. Interpolation is what we want
    3. Optimal conditioning is what we want
    4. Correlation construction is easier than causation learning

27. Economic dematerialization via

    1. Enclosing the intellectual commons
    2. Creative economy jobs

28. Haunting and exchangeability. Connection to interpolation, individuation, legibility and nonparametrics.

29. X is Yer than Z

30. Is residual prediction different from adversarial prediction?

2 Meta

• Aunty Val, the digest persona

3 Music skills

• Learn How To Make Noisia-Inspired Synths With Ableton’s Operator
• Dongkong track breakdown

4 Music

Nestup / cutelabnyc/nested-tuplets: Fancy javascript for manipulating nested tuplets.

5 SDEs, optimization and gradient flows

Nguyen and Malinsky (2020)

Statistical Inference via Convex Optimization.

Conjugate functions illustrated.

Francis Bach on the use of geometric sums and a different take by Julyan Arbel.

Tutorial to approximating differentiable control problems. An extension of this is universal differential equations.

• An Introduction to Stochastic Calculus

6 References