claim

active

claim:monotonic-non-linearities-are-insufficient-to-reverse-evolutionary-outcomes

Monotonic non-linearities are insufficient to reverse evolutionary outcomes

Source paper

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Design for an Individual: Connectionist Approaches to the Evolutionary Transitions in Individuality

(2022) · Watson, Richard A. · Levin, Michael · Buckley, Christopher L.

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Communities (2)

community

Causal emergence in biological systems
members_of
Examines how macro-scale causal power exceeds micro-scale in living and learning systems.
Evolutionary individuality and hierarchical agency
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Multi-level selection theory examining how non-aggregative fitness interactions enable higher-order units as genuine evolutionary agents, emphasizing problem-solving over predetermined mechanisms.

Related by similarity (8)

cosine ≥ 0.65 · no typed edge

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The results generalise readily to non-equilibrium systems where scaling relationships remain similar (e.g., dynamic or localised scaling).claim0.774
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Evolution often produces general-purpose problem-solving machines whose capacities cannot be inferred from the default invariant course of development.claim0.770
A claim about the outcome of the MCA-enhanced process.
Evolution makes problem-solving agents, not solutions; commitment to mutation and uncontrollable environments as ratchet for intelligenceclaim0.769
Biology's robustness, open-endedness, evolvability, and unique complexity likely depend on the fact that evolution works with an agential material.claim0.768
Central claim linking life's properties to the inherent competencies of its material substrate.
Evolution learns to generalize beyond default morphologies, producing problem-solving machines.claim0.763
Argues that evolutionary learning goes beyond specific adaptations.
For a new level of individuality to have causal role as evolutionary unit, heritable fitness differences must be non-aggregative (not decomposable into lower-level differences).claim0.763
Core claim: monotonic non-linearities are insufficient; evolutionary outcomes must change depending on context.
Does the multi-directional nature of truth imply an underlying nonlinear representation, or is it compatible with linear separability?question0.759
Theoretical open question about the geometry of truth in LLMs raised in Discussion
Artificially evolved neural networks and robots often lack modularity unless it is directly selected for, and exhibit inefficiencies from evolutionarily duplicated sub-structures.finding0.754
Evidence that evolved machines share biological property of non-optimal modularity, blurring the distinction