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 Formalizing the Role of Agent-Based Modeling in Causal Inference and Epidemiology

2014 ◽  
Vol 181 (2) ◽  
pp. 92-99 ◽  
Author(s):  
Brandon D. L. Marshall ◽  
Sandro Galea
Keyword(s):  
Causal Inference ◽  
Agent Based Modeling ◽  
Agent Based

scholarly journals Agent-based modeling and life cycle dynamics of COVID-19-related online collective actions

2021 ◽  
Author(s):  
Gang Zhang ◽  
Hao Li ◽  
Rong He ◽  
Peng Lu
Keyword(s):  
Big Data ◽  
Life Cycle ◽  
Evolutionary Dynamics ◽  
Life Cycles ◽  
Agent Based Modeling ◽  
Macro Level ◽  
Collective Actions ◽  
Agent Based ◽  
Individual Behaviors ◽  
Micro Level

AbstractThe outbreak of COVID-19 has greatly threatened global public health and produced social problems, which includes relative online collective actions. Based on the life cycle law, focusing on the life cycle process of COVID-19 online collective actions, we carried out both macro-level analysis (big data mining) and micro-level behaviors (Agent-Based Modeling) on pandemic-related online collective actions. We collected 138 related online events with macro-level big data characteristics, and used Agent-Based Modeling to capture micro-level individual behaviors of netizens. We set two kinds of movable agents, Hots (events) and Netizens (individuals), which behave smartly and autonomously. Based on multiple simulations and parametric traversal, we obtained the optimal parameter solution. Under the optimal solutions, we repeated simulations by ten times, and took the mean values as robust outcomes. Simulation outcomes well match the real big data of life cycle trends, and validity and robustness can be achieved. According to multiple criteria (spans, peaks, ratios, and distributions), the fitness between simulations and real big data has been substantially supported. Therefore, our Agent-Based Modeling well grasps the micro-level mechanisms of real-world individuals (netizens), based on which we can predict individual behaviors of netizens and big data trends of specific online events. Based on our model, it is feasible to model, calculate, and even predict evolutionary dynamics and life cycles trends of online collective actions. It facilitates public administrations and social governance.

scholarly journals UNDERSTANDING THE EFFECT OF CO-WORKER SUPPORT ON CONSTRUCTION SAFETY PERFORMANCE FROM THE PERSPECTIVE OF RISK THEORY: AN AGENT-BASED MODELING APPROACH

2019 ◽  
Vol 25 (2) ◽  
pp. 132-144 ◽  
Author(s):  
Tingting Ji ◽  
Hsi-Hsien Wei ◽  
Jiayu Chen
Keyword(s):  
Construction Safety ◽  
Agent Based Modeling ◽  
Safety Performance ◽  
Worker Safety ◽  
Risk Theory ◽  
System Level ◽  
Agent Based ◽  
Hazardous Area ◽  
Worker Support

Co-worker safety support has been given prominence in manufacturing and transportation field for its positive effect on individual workers’ safety; however, there is little evidence to show if such supporting role of co-workers is significant in improving project-level safety performance in construction workplace. This study adopts agent-based modeling (ABM) to understand the effectiveness of two distinct co-worker-safety-support actions on the safety performance of a construction project. Based on the risk theory, the ABM model simulates a construction site where worker agents reinforce steel bars with the likelihood of suffering crane-related incidents. The results indicate that both co-worker-support actions can significantly reduce the occurrence of nonfatal incidents but shows little influence in fatal incidents, and in reducing high-severity incidents, the action of warning peers to leave the hazardous area has the same effectiveness as reminding peers to wear Personal Protective Equipment. The present study provides a fresh insight into the safety-related role of co-workers: not only reveals how the local-level effects of co-workers’ safety assistance emerge the system-level consequences, but demonstrates the effectiveness of specific peer-support actions on three levels of construction safety performance, and thereby extends our existing body of knowledge on co-worker safety support in the construction field.

scholarly journals Understanding Lactobacillus paracasei and Streptococcus oralis Biofilm Interactions through Agent-Based Modeling

mSphere ◽  
2021 ◽  
Author(s):  
Linda Archambault ◽  
Sherli Koshy-Chenthittayil ◽  
Angela Thompson ◽  
Anna Dongari-Bagtzoglou ◽  
Reinhard Laubenbacher ◽  
...  
Keyword(s):  
Biofilm Formation ◽  
Oral Candidiasis ◽  
Agent Based Modeling ◽  
Cell Models ◽  
Complex Environments ◽  
Inhibitory Effects ◽  
Content Type ◽  
Agent Based ◽  
Streptococcus Oralis

We previously discovered a role of the oral commensal Streptococcus oralis as an accessory pathogen. S. oralis increases the virulence of Candida albicans infections in murine oral candidiasis and epithelial cell models through mechanisms which promote the formation of tissue-damaging biofilms. Lactobacillus species have known inhibitory effects on biofilm formation of many microbes, including Streptococcus species. Agent-based modeling has great advantages as a means of exploring multifaceted relationships between organisms in complex environments such as biofilms.

scholarly journals Rethinking the role of Agent-Based Modeling in archaeology

2016 ◽  
Vol 41 ◽  
pp. 283-298 ◽  
Author(s):  
Wendy H. Cegielski ◽  
J. Daniel Rogers
Keyword(s):  
Agent Based Modeling ◽  
Agent Based

scholarly journals COVID-19 Mortality in English Neighborhoods: The Relative Role of Socioeconomic and Environmental Factors

J ◽  
2021 ◽  
Vol 4 (2) ◽  
pp. 131-146
Author(s):  
Peter Congdon
Keyword(s):  
Risk Factors ◽  
Air Quality ◽  
Mortality Risk ◽  
Spatial Clustering ◽  
Ethnic Composition ◽  
Relative Role ◽  
Ethnic Segregation ◽  
Spatially Correlated ◽  
The Uk

Factors underlying neighborhood variation in COVID-19 mortality are important to assess in order to prioritize resourcing and policy intervention. As well as characteristics of area populations, such as health status and ethnic mix, it is important to assess the role of more specifically environmental variables (e.g., air quality, green space access). The analysis of this study focuses on neighborhood mortality variations during the first wave of the COVID-19 epidemic in England against a range of postulated area risk factors, both socio-demographic and environmental. We assess mortality gradients across levels of each risk factor and use regression methods to control for multicollinearity and spatially correlated unobserved risks. An analysis of spatial clustering is based on relative mortality risks estimated from the regression. We find mortality gradients in most risk factors showing appreciable differences in COVID mortality risk between English neighborhoods. A regression analysis shows that after allowing for health deprivation, ethnic mix, and ethnic segregation, environment (especially air quality) is an important influence on COVID mortality. Hence, environmental influences on COVID mortality risk in the UK first wave are substantial, after allowing for socio-demographic factors. Spatial clustering of high mortality shows a pronounced metropolitan-rural contrast, reflecting especially ethnic composition and air quality.

scholarly journals THE ROLE OF TASTE AFFINITY IN AGENT-BASED MODELS FOR SOCIAL RECOMMENDATION

2013 ◽  
Vol 16 (04n05) ◽  
pp. 1350009 ◽  
Author(s):  
GIULIO CIMINI ◽  
AN ZENG ◽  
MATÚŠ MEDO ◽  
DUANBING CHEN
Keyword(s):  
Main Tool ◽  
Real Data ◽  
Similarity Metrics ◽  
Adaptive Model ◽  
Social Recommendation ◽  
Agent Based ◽  
Online Social Media ◽  
Realistic Assumption ◽  
The Given

In the Internet era, online social media emerged as the main tool for sharing opinions and information among individuals. In this work, we study an adaptive model of a social network where directed links connect users with similar tastes, and over which information propagates through social recommendation. Agent-based simulations of two different artificial settings for modeling user tastes are compared with patterns seen in real data, suggesting that users differing in their scope of interests is a more realistic assumption than users differing only in their particular interests. We further introduce an extensive set of similarity metrics based on users' past assessments, and evaluate their use in the given social recommendation model with both artificial simulations and real data. Superior recommendation performance is observed for similarity metrics that give preference to users with small scope — who thus act as selective filters in social recommendation.

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.

Sign in / Sign up

Export Citation Format

Share Document