Probabilistic projections of distributions of kin over the life course

Dados Bibliográficos

ID8238366
AutoresJoe Butterick, Joe W B Butterick (University of Southampton), John Hilton (0000-0001-9473-757X, University of Southampton), Jason Hilton (0000-0003-0286-8236), Jakub Bijak (0000-0002-2563-5040, University of Southampton), Peter W F Smith (University of Southampton), Erengul Dodd (0000-0003-0834-4492, University of Southampton)
Ano2026
Volume54
Páginas263-308
Data de publicação2026-02-11
Peer ReviewedSim
Open AccessSim
TipoARTICLE
PeriódicoDemographic Research (JOURNAL)
Identificadores do periódicoISSN: 1435-9871 • E-ISSN: 2363-7064
EditoraMax Planck Institute for Demographic Research (PUBLISHER • DE)
DOI10.4054/demres.2026.54.9
OpenAlexW7128548962
IdiomaEN
Referências citadas33

BACKGROUNDMathematical kinship demography is an expanding area of research.Recent papers have explored the expected number of kin a typical individual should experience.Despite the uncertainty of the future number and distributions of kin, just one paper investigates it. OBJECTIVEWe aim to develop a new method for obtaining the probability that a typical population member experiences one or more of some kin at any age through the life course. METHODSCombinatorics, matrix algebra, and convolution theory are combined to find discrete probability distributions of kin number.We propose closed form expressions, illustrating the recursive nature of kin replenishment, using composition of matrix operations.Our model requires as inputs age-specific mortality and fertility. CONCLUSIONSWe derive probabilities of kin number for fixed age of kin and over all possible ages of kin.From these the expectation, variance, and other moments of kin number can be found.We demonstrate how kinship structures are conditional on familial events

Course (navigation · Life course approach · Population · Probabilistic logic · Statistical model · Evolutionary Psychology and Human Behavior · Insurance, Mortality, Demography, Risk Management · Markov Chains and Monte Carlo Methods