Package index
The AgentBasedModel class to represent the agents and their social network
Create and run agent-based models of social behavior in socmod.
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AgentBasedModel socmodimplementation of an agent-based model
Running models
The run() function uses the specified partner_selection, interaction, and iterate_model functions to specify these dynamics and iterate the model (an instance of socmod::AgentBasedModel).
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run() - Run an agent-based model for either a number of iterations (
max_its) or until some stop condition is met (e.g., fixation).
Learning (strategy) functions
Functions to pass to run for model iteration in a learning model, and for either success-biased or frequency-biased adaptive learning.
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iterate_learning_model() - A generic method for iterating a learning model, setting the current behavior and fitness to be whatever was identified as the next behavior and fitness.
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success_bias_interact() - Success-biased interaction to be paired with success_bias_teacher_selection that sets the learner's next behavior and fitness to be the teacher's current behavior and fitness.
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success_bias_select_teacher() - Success-biased teacher selection
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frequency_bias_interact() - Interaction function for frequency-biased adaptive learning.
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frequency_bias_select_teacher() - Frequency biased teacher selection does nothing
Agents and their neighbors
Agents in the model are represented by a number of attributes, with select class methods to track lists of their neighbors.
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make_homophily_network() - Create an undirected asymmetric homophily network.
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make_small_world() - Make a small world network.
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make_preferential_attachment() - Make a preferential attachment network.
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G_NM() - Erdős-Rényi random graph G(N, M).
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make_regular_lattice() - Create a regular lattice graph.
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plot_homophilynet() - Plot homophily network where groups color nodes.
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ggnetplot() - Command that operates like ggplot
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add_unique_edge() - Add an undirected edge from v1 to v2 to graph g if it does not already exist.
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get_all_possible_edges() - Get all possible edges between node indices 1 to N for either directed or undirected networks.
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not_adjacent() - Check two vertices are not adjacent.