Using Mobility Profiles for Synthetic Population Generation

2021 ◽  
pp. 207-219
Author(s):  
Alperen Bektas ◽  
René Schumann
Keyword(s):  
Synthetic Population

Registration of the OhVRS-1 Maize Synthetic Population

2012 ◽  
Vol 7 (1) ◽  
pp. 100-103
Author(s):  
Margaret G. Redinbaugh ◽  
Mark W. Jones
Keyword(s):  
Synthetic Population

Genetic Analysis of Quantitative Traits in Synthetic Population 316P02 of Maize (Zea mays L.)

2001 ◽  
Vol 29 (1-2) ◽  
pp. 77-84
Author(s):  
Nenad Vasić ◽  
Djordje Jocković ◽  
Mile Ivanović ◽  
Luiz Peternelli ◽  
Milisav Stojaković ◽  
...  
Keyword(s):  
Zea Mays ◽  
Genetic Analysis ◽  
Quantitative Traits ◽  
Zea Mays L ◽  
Synthetic Population

scholarly journals Genetic variability in popcorn synthetic population

2018 ◽  
Vol 41 (1) ◽  
pp. 39497 ◽  
Author(s):  
Gustavo Hugo Ferreira de Oliveira ◽  
Camila Amaral ◽  
Lucas Tadeu Mazza Revolti ◽  
Rodolfo Buzinaro ◽  
Gustavo Vitti Moro
Keyword(s):  
Genetic Variability ◽  
Synthetic Population

Weighted social networks for a large scale artificial society

2016 ◽  
Vol 30 (02) ◽  
pp. 1550276 ◽  
Author(s):  
Zong Chen Fan ◽  
Wei Duan ◽  
Peng Zhang ◽  
Xiao Gang Qiu
Keyword(s):  
Social Networks ◽  
Social Structure ◽  
Social Relationships ◽  
Large Scale ◽  
Census Data ◽  
Clustering Coefficient ◽  
Generation Model ◽  
Social Phenomenon ◽  
Artificial Society ◽  
Synthetic Population

The method of artificial society has provided a powerful way to study and explain how individual behaviors at micro level give rise to the emergence of global social phenomenon. It also creates the need for an appropriate representation of social structure which usually has a significant influence on human behaviors. It has been widely acknowledged that social networks are the main paradigm to describe social structure and reflect social relationships within a population. To generate social networks for a population of interest, considering physical distance and social distance among people, we propose a generation model of social networks for a large-scale artificial society based on human choice behavior theory under the principle of random utility maximization. As a premise, we first build an artificial society through constructing a synthetic population with a series of attributes in line with the statistical (census) data for Beijing. Then the generation model is applied to assign social relationships to each individual in the synthetic population. Compared with previous empirical findings, the results show that our model can reproduce the general characteristics of social networks, such as high clustering coefficient, significant community structure and small-world property. Our model can also be extended to a larger social micro-simulation as an input initial. It will facilitate to research and predict some social phenomenon or issues, for example, epidemic transition and rumor spreading.

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