Gregarious Behavior, Human Colonization and Social Diferentiation: An Agent-based Model

Studies of colonization processes in past human societies often use a standard population model in which population is represented as a single quantity. Real populations in these processes, however, are structured with internal classes or stages, and classes are sometimes created based on social differentiation. In this present work, information about the colonization of old Providence Island was used to create an agent-based model of the colonization process in a heterogeneous environment for a population with social differentiation. Agents were socially divided into two classes and modeled with dissimilar spatial clustering preferences. The model and simulations assessed the importance of gregarious behavior for colonization processes conducted in heterogeneous environments by socially-differentiated populations. Results suggest that in these conditions, the colonization process starts with an agent cluster in the largest and most suitable area. The spatial distribution of agents maintained a tendency toward randomness as simulation time increased, even when gregariousness values increased. The most conspicuous effects in agent clustering were produced by the initial conditions and behavioral adaptations that increased the agent capacity to access more resources and the likelihood of gregariousness. The approach presented here could be used to analyze past human colonization events or support long-term conceptual design of future human colonization processes with small social formations into unfamiliar and uninhabited environments.

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Gregarious Behavior, Human Colonization and Social Differentiation: An Agent-Based Model

Journal of Artificial Societies and Social Simulation ◽

10.18564/jasss.4433 ◽

2020 ◽

Vol 23 (4) ◽

Author(s):

Sebastian Fajardo ◽

Gert Jan Hofstede ◽

Martijn de Vries ◽

Mark Kramer ◽

Andrés Bernal

Keyword(s):

Social Differentiation ◽

Agent Based Model ◽

Agent Based ◽

Gregarious Behavior ◽

Human Colonization

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Estimating the impact of interventions against COVID-19: from lockdown to vaccination

10.1101/2021.03.21.21254049 ◽

2021 ◽

Author(s):

James Thompson ◽

Stephen Wattam

Keyword(s):

Spatial Clustering ◽

Herd Immunity ◽

Contact Tracing ◽

Agent Based Model ◽

Seir Model ◽

Agent Based ◽

Resident Population ◽

First Case ◽

Equivalent Equation ◽

The Impact

AbstractCoronavirus disease 2019 (COVID-19) is an infectious disease of humans caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Since the first case was identified in China in December 2019 the disease has spread worldwide, leading to an ongoing pandemic. In this article, we present a detailed agent-based model of COVID-19 in Luxembourg, and use it to estimate the impact, on cases and deaths, of interventions including testing, contact tracing, lockdown, curfew and vaccination.Our model is based on collation, with agents performing activities and moving between locations accordingly. The model is highly heterogeneous, featuring spatial clustering, over 2000 behavioural types and a 10 minute time resolution. The model is validated against COVID-19 clinical monitoring data collected in Luxembourg in 2020.Our model predicts far fewer cases and deaths than the equivalent equation-based SEIR model. In particular, with R0 = 2.45, the SEIR model infects 87% of the resident population while our agent-based model results, on average, in only around 23% of the resident population infected. Our simulations suggest that testing and contract tracing reduce cases substantially, but are much less effective at reducing deaths. Lockdowns appear very effective although costly, while the impact of an 11pm-6am curfew is relatively small. When vaccinating against a future outbreak, our results suggest that herd immunity can be achieved at relatively low levels, with substantial levels of protection achieved with only 30% of the population immune. When vaccinating in midst of an outbreak, the challenge is more difficult. In this context, we investigate the impact of vaccine efficacy, capacity, hesitancy and strategy.We conclude that, short of a permanent lockdown, vaccination is by far the most effective way to suppress and ultimately control the spread of COVID-19.

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Estimating the impact of interventions against COVID-19: From lockdown to vaccination

PLoS ONE ◽

10.1371/journal.pone.0261330 ◽

2021 ◽

Vol 16 (12) ◽

pp. e0261330

Author(s):

James Thompson ◽

Stephen Wattam

Keyword(s):

Spatial Clustering ◽

Herd Immunity ◽

Contact Tracing ◽

Agent Based Model ◽

Seir Model ◽

Agent Based ◽

Resident Population ◽

First Case ◽

Equivalent Equation ◽

The Impact

Coronavirus disease 2019 (COVID-19) is an infectious disease of humans caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Since the first case was identified in China in December 2019 the disease has spread worldwide, leading to an ongoing pandemic. In this article, we present an agent-based model of COVID-19 in Luxembourg, and use it to estimate the impact, on cases and deaths, of interventions including testing, contact tracing, lockdown, curfew and vaccination. Our model is based on collation, with agents performing activities and moving between locations accordingly. The model is highly heterogeneous, featuring spatial clustering, over 2000 behavioural types and a 10 minute time resolution. The model is validated against COVID-19 clinical monitoring data collected in Luxembourg in 2020. Our model predicts far fewer cases and deaths than the equivalent equation-based SEIR model. In particular, with R0 = 2.45, the SEIR model infects 87% of the resident population while our agent-based model infects only around 23% of the resident population. Our simulations suggest that testing and contract tracing reduce cases substantially, but are less effective at reducing deaths. Lockdowns are very effective although costly, while the impact of an 11pm-6am curfew is relatively small. When vaccinating against a future outbreak, our results suggest that herd immunity can be achieved at relatively low coverage, with substantial levels of protection achieved with only 30% of the population fully immune. When vaccinating in the midst of an outbreak, the challenge is more difficult. In this context, we investigate the impact of vaccine efficacy, capacity, hesitancy and strategy. We conclude that, short of a permanent lockdown, vaccination is by far the most effective way to suppress and ultimately control the spread of COVID-19.

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Simulation-based analyses of an evacuation from a metropolis during a bombardment

SIMULATION ◽

10.1177/0037549714551291 ◽

2014 ◽

Vol 90 (11) ◽

pp. 1244-1267 ◽

Cited By ~ 15

Author(s):

Jang Won Bae ◽

SeHoon Lee ◽

Jeong Hee Hong ◽

Il-Chul Moon

Keyword(s):

Initial Conditions ◽

Real Data ◽

Agent Based Modeling ◽

Agent Based Model ◽

Target Region ◽

Agent Based ◽

Virtual Experiments ◽

The Road ◽

Simulation Based ◽

The Military

The bombardment of a metropolis is considered a nightmare scenario. To reduce losses from such an assault, big cities have developed evacuation policies in case of bombardment. However, to build efficient evacuation policies, much footing data is required that considers both military and civilian views. Agent-based modeling and simulation could be utilized as a method to obtain the footing data. In this paper, we develop an evacuation agent-based model that describes a massive evacuation through the road network of a metropolis during a bombardment. In particular, our model took account of bombing strategies (i.e. the military view) as well as the characteristics of roads and evacuation agents (i.e. the civilian view) in order to analyze evacuations from both military and civilian perspectives. Moreover, we applied real data from a target region to calibrate parameters and initial conditions of the evacuation agent-based models, which increased the reliability of simulation results. Using the evacuation agent-based model, we designed and performed virtual experiments with varying military and civilian factors. Through the various analyses on the experiment results, we showed that our model could be a framework that provides footing data to develop efficient evacuation policies and preparations.

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