Gregarious Behavior, Human Colonization and Social Differentiation Agent-Based Model (1.0.0)
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.
Release Notes
Simulation experiments are included in the behavior space interface. Seed to replicate the experiments can be found in the input data file.
Associated Publications
This release is out-of-date. The latest version is
1.1.0
Gregarious Behavior, Human Colonization and Social Differentiation Agent-Based Model 1.0.0
Submitted bysdfajardobPublished Sep 08, 2020
Last modified May 05, 2022
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.
Release Notes
Simulation experiments are included in the behavior space interface. Seed to replicate the experiments can be found in the input data file.
Cite this Model
Sebastian Fajardo, Andrés Bernal, Gert Jan Hofstede, Mark R Kramer, Martijn de Vries (2020, September 08). “Gregarious Behavior, Human Colonization and Social Differentiation Agent-Based Model” (Version 1.0.0). CoMSES Computational Model Library. Retrieved from: https://www.comses.net/codebases/d5cfd5b6-6cfe-4dbc-ab9c-6fadb3ed688a/releases/1.0.0/
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