The goal of socmod is to simplify development of models of social behavior for beginners and experts alike, currently focusing on agent-based models of adaptation diffusion. The framework is general, though, and the software could be extended to other social behavior models.
Quickstart examples
Below we present two examples of how socmod can be used for succinctly defining models of social behavior. In the first example we initialize and run, then visualize adaptation prevalence over time, with a single model initalized with a single set of model parameters. In the second example we set up a computational experiment to analyze how simulated prevalence dynamics and intervention success rate depend on the adaptive fitness when agents learn via success-biased social learning.
make_abm(n_agents = 100) %>%
# Initialize 20% of agents w/ Adaptive w/ fitness 0.125 times > than Legacy
initialize_agents(initial_prevalence = 0.1, adaptive_fitness = 1.125) %>%
run_trial %>%
plot_prevalence %>%
print
Installation
You can install the development version of socmod from GitHub with devtools or pak:
devtools
# Install this if you don't have devtools.
install.packages("devtools")
devtools::install_github("css4s/socmod")pak
# Install this if you don't have pak.
install.packages("pak")
pak::pak("css4s/socmod")More information and the philosophy of socmod
Different models of social behavior are specified by the details of how many individuals are in a population, what behaviors or opinions they do or have, what benefits they accrue(d) through their behaviors, how they learn or influence one another, and any environmental or other relevant factors. This framework seeks to encapsulate different approaches to modeling diverse social behaviors, such as those thoroughly reviewed in Paul Smaldino’s (2023) textbook Modeling Social Behavior.
Technically, socmod uses object-oriented programming, provided by R6, and functional-style agent and model behavior specification inspired by Agents.jl, which I myself have enjoyed using. But, I still had to do my plotting in R, and more beginning students across disciplines will tend to know R than Julia. R also seems to have a great community with the r-lib project that seems to be bringing a continuity to scientific programming that I have not seen in any other programming language.