← 研踪 PaperTrailarXiv:2610.24865 · v4
A self-organizing mechanism for collective foraging: recruitment cascades couple Levy search to a clustered maximum-entropy resource field
首次提交 2026-10-18 05:09
版本更新 2026-11-04 14:29
雷达观测 2026-11-05 11:10
摘要
Foraging recruitment is modeled as a contagion: an agent that finds a resource triggers a linear-threshold cascade on the group's communication network. Because activation is degree-normalized, crowding shuts the cascade off, so the swarm aggregates on a resource and then disperses to explore again. Acting on the cascade moves agents in space, and that motion rewires the network, so recruitment and exploration are one coupled process; removing this coupling alone abolishes the self-limitation. Recruitment spreads only within a connectivity window, bounded below by percolation onset and above by degree-blocking. The swarm self-organizes to the near-critical edge of this window -- self-organized quasi-criticality: recruitment sits near but below the critical point without settling onto a clean, size-invariant critical state, and the activity spectrum has a finite correlation time (Lorentzian-like, not 1/f), consistent with local rather than global criticality. Recruitment raises harvest up to 2.7x over solitary search on sparse, clustered resource fields and loses (down to 0.37x) on abundant or uniform ones; because resources generically aggregate, the favourable regime is the common one. The empirically motivated Levy search and the resource field it exploits are both least-committal: the search a maximum-caliber walk, and the field a maximum-entropy point process whose generic, aggregation-set sign is clustered (attractive). The search exponent that maximizes harvest tracks the field's fractal dimension (a heavier-tailed, more ballistic search for more strongly clustered fields). These are one coupled mechanism: heavy-tailed Levy exploration crosses the field's sparse voids while recruitment cascades exploit its clusters, so one maximum-entropy geometry binds exploration and recruitment into a single self-organizing cycle.
作者备注：21 pages, 19 figures (7 main + 12 SI), 1 tables, Code and data: https://github.com/galenwilkerson/collective-foraging-cascades
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v4 · 2026-11-04 14:29 · 最新观测版本
A self-organizing mechanism for collective foraging: recruitment cascades couple Levy search to a clustered maximum-entropy resource field
Foraging recruitment is modeled as a contagion: an agent that finds a resource triggers a linear-threshold cascade on the group's communication network. Because activation is degree-normalized, crowding shuts the cascade off, so the swarm aggregates on a resource and then disperses to explore again. Acting on the cascade moves agents in space, and that motion rewires the network, so recruitment and exploration are one coupled process; removing this coupling alone abolishes the self-limitation. Recruitment spreads only within a connectivity window, bounded below by percolation onset and above by degree-blocking. The swarm self-organizes to the near-critical edge of this window -- self-organized quasi-criticality: recruitment sits near but below the critical point without settling onto a clean, size-invariant critical state, and the activity spectrum has a finite correlation time (Lorentzian-like, not 1/f), consistent with local rather than global criticality. Recruitment raises harvest up to 2.7x over solitary search on sparse, clustered resource fields and loses (down to 0.37x) on abundant or uniform ones; because resources generically aggregate, the favourable regime is the common one. The empirically motivated Levy search and the resource field it exploits are both least-committal: the search a maximum-caliber walk, and the field a maximum-entropy point process whose generic, aggregation-set sign is clustered (attractive). The search exponent that maximizes harvest tracks the field's fractal dimension (a heavier-tailed, more ballistic search for more strongly clustered fields). These are one coupled mechanism: heavy-tailed Levy exploration crosses the field's sparse voids while recruitment cascades exploit its clusters, so one maximum-entropy geometry binds exploration and recruitment into a single self-organizing cycle.
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