title: Entropy Based Modelling for Estimating Demographic Trends.
abstract: In this paper, an entropy-based method is proposed to forecast the demographical changes of countries. We formulate the estimation of future demographical profiles as a constrained optimization problem, anchored on the empirically validated assumption that the entropy of age distribution is increasing in time. The procedure of the proposed method involves three stages, namely: 1) Prediction of the age distribution of a country's population based on an "age-structured population model"; 2) Estimation the age distribution of each individual household size with an entropy-based formulation based on an "individual household size model"; and 3) Estimation the number of each household size based on a "total household size model". The last stage is achieved by projecting the age distribution of the country's population (obtained in stage 1) onto the age distributions of individual household sizes (obtained in stage 2). The effectiveness of the proposed method is demonstrated by feeding real world data, and it is general and versatile enough to be extended to other time dependent demographic variables.
authors.affiliation: Department of Precision Instrument, Center for Brain-Inspired Computing Research, Tsinghua University, Beijing, P.R.China; Institute of High Performance Computing, A*STAR, Singapore, Singapore.
authors.forename: Guoqi
authors.initials: G
authors.lastname: Li
authors.affiliation: Institute of High Performance Computing, A*STAR, Singapore, Singapore; School of Businesses, Renmin University of China, Beijing, P.R.China.
authors.forename: Daxuan
authors.initials: D
authors.lastname: Zhao
authors.affiliation: School of computing engineering, Nanyang Technological University, Singapore, Singapore.
authors.forename: Yi
authors.initials: Y
authors.lastname: Xu
authors.affiliation: Institute of High Performance Computing, A*STAR, Singapore, Singapore.
authors.forename: Shyh-Hao
authors.initials: SH
authors.lastname: Kuo
authors.affiliation: Institute of High Performance Computing, A*STAR, Singapore, Singapore.
authors.forename: Hai-Yan
authors.initials: HY
authors.lastname: Xu
authors.affiliation: Institute of High Performance Computing, A*STAR, Singapore, Singapore.
authors.forename: Nan
authors.initials: N
authors.lastname: Hu
authors.affiliation: School of Aerospace, Xi'an Jiaotong University, Xi'an, P. R. China.
authors.forename: Guangshe
authors.initials: G
authors.lastname: Zhao
authors.affiliation: Institute of High Performance Computing, A*STAR, Singapore, Singapore.
authors.forename: Christopher
authors.initials: C
authors.lastname: Monterola
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D019277:Entropy; D005191:Family Characteristics; D006801:Humans; D008962:Models, Theoretical; D011157:Population Dynamics / Q000639:trends*; D014481:United States