scholarly journals A Micro-Level Data-Calibrated Agent-Based Model: The Synergy between Microsimulation and Agent-Based Modeling

2018 ◽  
Vol 24 (2) ◽  
pp. 128-148
Author(s):  
Karandeep Singh ◽  
Chang-Won Ahn ◽  
Euihyun Paik ◽  
Jang Won Bae ◽  
Chun-Hee Lee
Keyword(s):  
Population Dynamics ◽  
Census Data ◽  
Social Issues ◽  
Real Data ◽  
Agent Based Modeling ◽  
Population Census ◽  
Agent Based ◽  
Artificial Society ◽  
Level Data ◽  
The Future

Artificial life (ALife) examines systems related to natural life, its processes, and its evolution, using simulations with computer models, robotics, and biochemistry. In this article, we focus on the computer modeling, or “soft,” aspects of ALife and prepare a framework for scientists and modelers to be able to support such experiments. The framework is designed and built to be a parallel as well as distributed agent-based modeling environment, and does not require end users to have expertise in parallel or distributed computing. Furthermore, we use this framework to implement a hybrid model using microsimulation and agent-based modeling techniques to generate an artificial society. We leverage this artificial society to simulate and analyze population dynamics using Korean population census data. The agents in this model derive their decisional behaviors from real data (microsimulation feature) and interact among themselves (agent-based modeling feature) to proceed in the simulation. The behaviors, interactions, and social scenarios of the agents are varied to perform an analysis of population dynamics. We also estimate the future cost of pension policies based on the future population structure of the artificial society. The proposed framework and model demonstrates how ALife techniques can be used by researchers in relation to social issues and policies.

Spatial analysis of child labour in India

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Lokender Prashad ◽  
Mili Dutta ◽  
Bishnu Mohan Dash
Keyword(s):  
Spatial Analysis ◽  
Census Data ◽  
Hot Spot ◽  
Labour Force ◽  
Child Labour ◽  
District Level ◽  
School Level ◽  
Population Census ◽  
Content Type ◽  
Level Data

Purpose This study on spatial analysis of child labour in India is a macro level analysis on child labour using the census data, 2011 of Government of India. The population census which is conducted once in 10 years only provides district level data on work-force distribution. The study has spatial analysis of child labour in the age group of 5–14 years in India. To assess the magnitude of the children in the labour force, district level data of Census 2011 has been used in the study. The study has made an attempt to identify the districts where there is high level of children in the labour force. This paper aims to estimate the magnitude and trends of children’s workforce participation using the census data as it is the only data base, which is available at the district level since 1961 onwards. The study has made an attempt to identify the clustering of child labour across districts in India and how child labour is clustered by different background characteristics. Design/methodology/approach The study has used ArcGIS software package, GeoDa software and local indicator of spatial association test. Findings The findings of study reveal that the proportion of rural, total fertility rate (TFR) and poverty headcount ratio is positively associated, whereas female literacy and the pupil-teacher ratio are negatively associated with child labour. It suggests that in the hot-spot areas and areas where there is a high prevalence of child labour, there is need to increase the teacher's number at the school level to improve the teacher-pupil ratio and also suggested to promote the female education, promote family planning practices to reduce TFR in those areas for reducing the incidences of child labour. Research limitations/implications The study also recommends that the incidences of child labour can be controlled by a comprehensive holistic action plan with the active participation of social workers. Practical implications The promulgation of effective legislation, active involvement of judiciary and police, political will, effective poverty alleviation and income generation programmes, sensitisation of parents, corporates and media can play effective role in mitigating the incidences of child labour in India. To achieve the sustainable development goals (SDGs) adopted by world leaders in 2015 to eradicate child labour in all its forms by 2025. Social implications The study aims to achieve the SDGs adopted by world leaders in 2015 to eradicate child labour in all its forms by 2025. Originality/value The study is purely original and there are no such studies in Indian context by using the latest software.

scholarly journals Development of an agent-based demographic model of Russia and its supercomputer implementation

2018 ◽  
pp. 368-378
Author(s):  
В.Л. Макаров ◽  
А.Р. Бахтизин ◽  
Е.Д. Сушко ◽  
Г.Б. Сушко
Keyword(s):  
Numerical Experiments ◽  
Agent Based Modeling ◽  
Human Migration ◽  
Demographic Model ◽  
Model Decomposition ◽  
Agent Based ◽  
Agent Model ◽  
Artificial Society ◽  
Administrative Division ◽  
And Migration

Рассмотрено применение агент-ориентированного подхода при моделировании естественного движения населения. Представлена демографическая модель России с учетом ее административного деления, в которой на основе моделирования поведения отдельных членов искусственного общества имитируются процессы смертности, рождаемости и миграции. Для моделирования поведения искусственного общества в целом требуется проведение модельных расчетов с числом агентов до $10^9$ и использование суперкомпьютерных технологий. Важной задачей в таких расчетах становится оптимальное распределение агентов по процессорам кластера. Показано применение декомпозиции модели с использованием алгоритма METIS с учетом основных особенностей агентной модели. Обсуждаются результаты апробации модели. The application of the agent-based modeling approach to the problem of natural human migration is considered. A demographic model of Russia is presented. This model takes into account the administrative division of Russia and simulates the processes of fertility, mortality and migration on the basis of modeling the behavior of individual members of the artificial society. In order to simulate the behavior of the artificial society as a whole, it is necessary to perform numerical experiments with the number of agents up to $10^9$ and to use supercomputer technologies. In such experiments, an important problem is the implementation of an optimal automatic distribution of agents across the cluster processors. The application of model decomposition using the METIS algorithm with consideration of the main features of the agent model is shown. The obtained numerical results are discussed.

scholarly journals Exploring the dynamics of neighborhood ethnic segregation with agent-based modelling: an empirical application to Bradford

2021 ◽  
Author(s):  
Carolina Zuccotti ◽  
Jan Lorenz ◽  
Rocco Paolillo ◽  
Alejandra Rodríguez Sánchez ◽  
Selamavit Serka
Keyword(s):  
Real Data ◽  
Agent Based Modeling ◽  
Residential Choice ◽  
Ethnic Segregation ◽  
Agent Based ◽  
Schelling Model ◽  
Residential Moves ◽  
Micro Level ◽  
The Uk

How individuals’ residential moves in space—derived from their varied preferences and constraints—translate into the overall segregation patterns that we observe, remains a key challenge in neighborhood ethnic segregation research. In this paper we use agent-based modeling to explore this concern, focusing on the interactive role of ethnic and socio-economic homophily preferences and housing constraints as determinants of residential choice. Specifically, we extend the notorious Schelling’s model to a random utility discrete choice approach to simulate the relocation decision of people (micro level) and how they translate into spatial segregation outcomes (macro level). We model different weights for preferences of ethnic and socioeconomic similarity in neighborhood composition over random relocations, in addition to housing constraints. We formalize how different combinations of these variables could replicate real segregation scenarios in Bradford, a substantially segregated local authority in the UK. We initialize our model with geo-referenced data from the 2011 Census and use Dissimilarity and the Average Local Simpson Indices as measures of segregation. As in the original Schelling model, the simulation shows that even mild preferences to reside close to co-ethnics can lead to high segregation levels. Nevertheless, ethnic over-segregation decreases, and results come close to real data, when preferences for socioeconomic similarity are slightly above preferences for ethnic similarity, and even more so when housing constraints are considered in relocation moves of agents. We discuss the theoretical and policy contributions of our work.

scholarly journals The Future of Agent-Based Modeling

2016 ◽  
Vol 43 (2) ◽  
pp. 271-287 ◽  
Author(s):  
Matteo G. Richiardi
Keyword(s):  
Agent Based Modeling ◽  
Agent Based ◽  
The Future

The Use of Agent-based Modeling in Projecting Risk Factors into the Future

2009 ◽  
pp. 1-6 ◽  
Author(s):  
Georgiy V. Bobashev ◽  
Robert J. Morris ◽  
William A. Zule ◽  
Andrei V. Borshchev ◽  
Lee Hoffer
Keyword(s):  
Risk Factors ◽  
Agent Based Modeling ◽  
Agent Based ◽  
The Future

scholarly journals Transition in Population Dynamics of the Intertidal Barnacle Balanus glandula after Invasion: Causes and Consequences of Change in Larval Supply

2020 ◽  
Vol 8 (11) ◽  
pp. 915
Author(s):  
Takashi Noda ◽  
Masashi Ohira
Keyword(s):  
Population Dynamics ◽  
Census Data ◽  
Spatial Scales ◽  
Local Population ◽  
Regional Scale ◽  
Population Census ◽  
Larval Supply ◽  
Balanus Glandula ◽  
Invasion Front ◽  
Larval Recruitment

To elucidate how the population dynamics of the acorn barnacle Balanus glandula transitioned after its invasion in 2000 along the Pacific coast of Japan, a population census was conducted from 2004 to 2014 at five shores along 49 km of coastline 144–193 km east outside of the invasion front. Survey areas at each shore consisted of five paired plots (cleared recruitment plots and control plots). Larval recruitment was first detected in 2004 but benthic individuals were not detected until 2 years later. The abundance and occurrence of B. glandula increased until around 2010; abundance then decreased but occurrence remained high (70%) until 2014, suggesting that the metapopulation of this barnacle approached a maximum around 2011. From 2011, the population dynamics of B. glandula changed considerably at two contrasting spatial scales: at a regional scale, the dependency of the number of larvae on stock size decreased, whereas at a local scale, the relative contribution of larval supply as a determinant of local population dynamics decreased. These findings suggest that the major driving force of population dynamics of the introduced barnacle changed in just a few years after invasion; therefore, population census data from just after an invasion, including larval recruitment monitoring just outside the invasion front, is essential to understanding invasion dynamics by sessile marine organisms.

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