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Gradual development and chance beget individuality
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Abstract
Behaviors – and thus behavioral individuality – rarely emerge fully formed but are instead built gradually through development, shaped by processes involving learning, skill formation, and experience. Prevailing theory in behavioral ecology, however, has largely focused on static equilibrium outcomes where behaviors are analyzed only as fully formed traits, often neglecting development. Here, we challenge this tradition by placing gradual development at the center of the emergence of individuality. We show, using a hierarchy of models, that when traits develop incrementally and are subject to even minimal stochasticity, individuality is not a special case but an inevitable outcome. Early chance deviations are preserved and amplified by the path-dependent nature of development, generating high and sustained repeatability across individuals. We demonstrate that this logic holds across neutral processes, directional development toward phenotypic targets, and adaptive, state-dependent decision-making. By integrating stochasticity, developmental dynamics, and optimality theory, our results recast individuality as a generic property of gradual development, not requiring specific adaptive or constraint-based explanations. More broadly, our findings illustrate that understanding biological variation may often require a shift away from static equilibria thinking toward explicitly time-dependent, developmental perspectives.
DOI
https://doi.org/10.32942/X2696V
Subjects
Behavior and Ethology, Developmental Biology, Ecology and Evolutionary Biology, Evolution
Keywords
behavioral development, developmental noise, intragenotypic variability, skill, stochasticity, personality, developmental noise, intragenotypic variability, skill formation, stochasticity, personality
Dates
Published: 2026-06-02 11:25
Last Updated: 2026-06-02 11:25
License
CC BY Attribution 4.0 International
Additional Metadata
Conflict of interest statement:
None
Data and Code Availability Statement:
All associated code is publicly accessible at: https://github.com/smehlman/Gradual-development
Language:
English
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