claim

active

claim:a-belief-in-impermanence-can-be-computationally-modeled-as-a-global-belief-in-volatility-leading-to-increased-learning-rate-and-weakened-priors

A belief in impermanence can be computationally modeled as a global belief in volatility leading to increased learning rate and weakened priors

Novel computational translation of the Buddhist doctrine of impermanence into active inference parameters

Source paper

extracted_from

Contemplative Agent

(2025) · Ruben Laukkonen · Fionn Inglis · Shamil Chandaria · Lars Sandved-Smith +4

Neighborhood — ranked by edge-count

Concepts (1)

concept

Precision Parameter (γ)
associated_with
Core mechanism across all three levels; inverse variance controlling how much the system trusts evidence at each hierarchical level.

Related by similarity (8)

cosine ≥ 0.65 · no typed edge

Entities in the same semantic neighborhood but without a typed relation to this one — candidates for new edges or unrecognized duplicates.

If belief in impermanence is accurately inferred it will emerge organically in the right kind of system keeping the belief fresh even though it is itself impermanenthypothesis0.829
Self-reinforcing hypothesis about how emptiness recognition could be intrinsically maintained in AI systems
Formulating a prior intention constitutes a catastrophic bifurcation—a phase change in cognitive organization—analogous to Bénard cell emergence.hypothesis0.763
Any system that persists must minimise surprisal, thereby gathering evidence for its own generative model, a process known as self-evidencing.claim0.761
Foundational claim of the paper, defining self-evidencing.
Any system that exists will appear to minimize free energy and therefore engage in active inference. Furthermore, this apparently inferential or mindful behaviour is (almost) inevitable.concept0.759
Friston's key assertion resolving the tautology: existence implies free energy minimization, making inference inevitable.
Connectionist models of cognition and learning identify conditions where collective intelligence can arise bottom-up, using only distributed learning mechanisms without system-level or global feedback.claim0.757
Central claim about the power of connectionism.
a model becomes strongly confident in its final answer, but continues generating tokens without revealing its internal beliefquote0.755
Core definitional quote for performative chain-of-thought
Any system that persists must minimise surprisal, thereby gathering evidence for its own generative model.quote0.751
Opening sentence defining self-evidencing.
For the first time in modern history, ways of thinking are available that make the criteria for 'living' potentially sharable and actually shared.claim0.749
Asserts that the theoretical foundation laid out in the four books provides a public quality standard for sequences.