Skip to content
DraconiticResearch
Search Sources API
206,690 studies31 sourcesUpdated 36 min ago
PreprintCC BY 4.0arXiv

Genealogical Reinforcement in Branching Processes

Abstract

Within many ecological populations, survival and reproduction exhibit a dependence on social interactions with relatives. As one example, resource excess is often shared according to genealogical proximity. When viewed as a branching system, an individual's offspring distribution acquires an effective dependence on genealogical structure. In the specific case that a large, local family structure leads to a large set of offspring, this can be viewed as a form of genealogical reinforcement. To capture this mechanism, we introduce a branching model of finite-depth genealogical dependence. Given partial observations of this reproducing population and its genealogy, we would like to reject the hypothesis of independent branching and estimate the strength of dependence. We present a statistical framework for detection and estimation, combining analytical results with simulation-based validation. We then identify our branching model with a tree-valued multitype branching process on a finite type space, providing a rigorous characterization of the expected structural reinforcement. Finally, we discuss how these tree-valued interactions can be accommodated within a broader class of stochastic processes.

Related

  1. Encoding neuronal shape in the stochastic dynamics of branching processes
    Marc-Eric Perrin, Maximilien Courgeon, Elise da Silva +4
    HALPreprintCC BY 4.0Full textCell biologyDevelopmental biologyHAL
  2. Numerical approximations of population size distributions for multi-type branching processes
    Xiang Ge Luo, Jack Kuipers, Niko Beerenwinkel
    arXivPreprintCC BY 4.0Full textEvolutionary biologyarXiv
  3. Increased monetary incentives enhance goal-directed reinforcement learning but fail to suppress automatic learning processes
    Ido Ben-Artzi, Noham Wolpe, Nitzan Shahar
    PsyArXivPreprintCC BY 4.0Full textNeuroscienceOSF
  4. Combining branching processes and Allee effects into an integral projection model to assess invasion risk
    James P. Peirce, Cynthia S. Tam, Joseph J. Parkos +4
    PLOS EcosystemsPeer-reviewedCC BY 4.0Developmental biologyEcologyPLOS
  5. Evolutionary branching under slowly evolving constraints
    H. C. Ito
    bioRxivPreprintAll rights reservedFull textEvolutionary biologybioRxiv
BibTeX
@misc{kochocki2026genealogical,
  title = {{Genealogical Reinforcement in Branching Processes}},
  author = {Alina Kochocki},
  howpublished = {arXiv},
  year = {2026},
  month = nov,
  doi = {10.48550/arxiv.2611.08548},
  eprint = {2611.08548},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2611.08548},
  note = {CC BY 4.0},
}
RIS
TY  - UNPB
TI  - Genealogical Reinforcement in Branching Processes
AU  - Alina Kochocki
PY  - 2026
DA  - 2026/11/05
PB  - arXiv
DO  - 10.48550/arxiv.2611.08548
UR  - https://arxiv.org/abs/2611.08548
AB  - Within many ecological populations, survival and reproduction exhibit a dependence on social interactions with relatives. As one example, resource excess is often shared according to genealogical proximity. When viewed as a branching system, an individual's offspring distribution acquires an effective dependence on genealogical structure. In the specific case that a large, local family structure leads to a large set of offspring, this can be viewed as a form of genealogical reinforcement. To capture this mechanism, we introduce a branching model of finite-depth genealogical dependence. Given partial observations of this reproducing population and its genealogy, we would like to reject the hypothesis of independent branching and estimate the strength of dependence. We present a statistical framework for detection and estimation, combining analytical results with simulation-based validation. We then identify our branching model with a tree-valued multitype branching process on a finite type space, providing a rigorous characterization of the expected structural reinforcement. Finally, we discuss how these tree-valued interactions can be accommodated within a broader class of stochastic processes.
ER  -