hypothesis

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prediction:conditions-necessary-to-evolve-new-individuality-level-are-described-by-conditions-for-learning-non-decomposable-functions-deep-model-induction

Conditions necessary to evolve new individuality level are described by conditions for learning non-decomposable functions (deep model induction)

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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Questions (1)

question

How do multiple short-sighted, self-interested entities organise their relationships with one another to create a new level of individuality?
answered_by
Central puzzle of ETIs: how cooperation at higher level evolves when lower-level selection favors individual reproduction.

Related by similarity (8)

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

The conditions necessary to evolve a new level of individuality are described by the conditions necessary to learn non-decomposable functions (deep model induction)claim0.959
Core interpretive claim of the paper connecting ETIs to connectionist learning
Evolutionary transitions in individuality constitute a form of deep model induction.claim0.841
Links ETIs to the learning of hierarchical representations.
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).claim0.831
Core claim: monotonic non-linearities are insufficient; evolutionary outcomes must change depending on context.
Functional relationships required for new individuality level encode non-linearly separable functions and are enacted via information integration and collective actionhypothesis0.818
ETIs are described by conditions necessary for deep learning / non-decomposable function learninghypothesis0.807
Developmental interactions required for evolutionary individuality must be able to coordinate solutions to non-decomposable functions (division of labour games)claim0.800
Developmental process is identified as computing non-linearly separable collective phenotypes
ETIs are the evolutionary equivalent of deep learning: (i) required functional relationships encode non-decomposable functions, (ii) these are enacted by basal cognition mechanisms, and (iii) conditions for deep model induction predict ETI occurrence.hypothesis0.798
Overarching three-part hypothesis stated in introduction
The kind of relationships necessary to produce evolutionary individuality – the generation and heritability of fitness differences at the collective level – are the same as those required to produce organismic individuality – the information integration and collective action characteristic of a self.claim0.789
Core conjecture linking evolutionary and organismic individuality.

Cross-corpus bridges (5)

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How are the conditions for deep model induction in connectionist learning systems predictive of ETI evolution conditions?questions/how-are-the-conditions-for-deep-model-induction.md0.831
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Deep Model Induction Equivalence (Hypothesis H2)frameworks/deep-model-induction-equivalence-hypothesis-h2.md0.819
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Under what conditions can deep interaction structures computing non-linearly separable functions evolve through bottom-up selection?questions/under-what-conditions-can-deep-interaction-structures-computing.md0.818
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Deep Model Inductionframeworks/deep-model-induction.md0.794
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How do non-decomposable fitness functions get solved during evolutionary transitions?questions/how-do-nondecomposable-fitness-functions-get-solved-during.md0.792