Learning in adaptive systems

On staring into scopophilic abysses

October 19, 2019 — January 22, 2020

economics
hidden variables
hierarchical models
machine learning
mind
sociology
statistics
wonk

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Figure 1

Placeholder.

Heuristics about what science can tell us about systems that we learn and that learn from us.

What the success of machine learning tells us about the structure of the world. Non-arbitrage versus hypothesis tests. Adversarial versus random noise. Economic systems. The bit before attaining equilibrium. Homogeneity versus local regularities. Our tendency to look for universals.

1 Incoming

Scale effects in economics are a subject of the 2019 Nobel Memorial economics prize. Internal versus external validity in economics. Daniel Lakens reviews () on generalizability in psychology.

2 Stylised comparison

Static world Adaptive world
People are… the objects the subjects
Individual behaviour emerges from… fixed personalities that we can discover networks and group dynamics
Society is… a system with static rules a game with rules made up by players as they go along
We should… learn the truth and act upon it guess a better next move than the other actors
Learning means… accepting a hypothesis based on P-value betting on the best portfolio of actions

3 As pertains to AI

Embedded agency and other framings of the Machine Intelligence Research Institute taxonomize some related ideas from the perspective of building intelligences that interact with the world. Maybe I will think about those in ecology of mind.

4 References

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