Predefined Models

Predefined agent based models exist in the Models submodule in the form of functions that return model, agent_step!, model_step! when called.

They are accessed like:

using Agents
model, agent_step!, model_step! = Models.flocking(; kwargs...)

The Examples section of the docs outline how to use and interact with each model.

Warn

Please notice that the predefined models are a convenience and not considered part of the public API. This means that they can have breaking changes between versions of Agents.jl without warning.

So far, the predefined models that exist in the Models sub-module are:

Agents.Models.battle — Method

battle(; fighters = 50)

Same as in Battle Royale.

source

Agents.Models.daisyworld — Method

daisyworld(;
    griddims = (30, 30),
    max_age = 25,
    init_white = 0.2,
    init_black = 0.2,
    albedo_white = 0.75,
    albedo_black = 0.25,
    surface_albedo = 0.4,
    solar_change = 0.005,
    solar_luminosity = 1.0,
    scenario = :default,
    seed = 165
)

Same as in Daisyworld.

To access the Daisy and Land types, simply call

using Agents.Models: Daisy, Land

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Agents.Models.flocking — Method

flocking(;
    n_birds = 100,
    speed = 1.0,
    cohere_factor = 0.25,
    separation = 4.0,
    separate_factor = 0.25,
    match_factor = 0.01,
    visual_distance = 5.0,
    extent = (100, 100),
    spacing = visual_distance / 1.5
)

Same as in Flock model.

source

Agents.Models.forest_fire — Method

forest_fire(;
    density = 0.8,
    griddims = (100, 100)
)

Same as in Forest fire model.

source

Agents.Models.fractal_growth — Method

fractal_growth(;
    initial_particles::Int = 100,
    space_extents::NTuple{2,Float64} = (150.0, 150.0),
    speed = 0.5,
    vibration = 0.55,
    attraction = 0.45,
    spin = 0.55,
    clockwise_fraction = 0.0,
    min_radius = 1.0,
    max_radius = 2.0,
)

Same as in Fractal Growth.

source

Agents.Models.game_of_life — Method

game_of_life(;
    rules::Tuple = (2, 3, 3, 3),
    dims = (100, 100),
    metric = :chebyshev
)

Same as in Conway's game of life.

source

Agents.Models.growing_bacteria — Method

growing_bacteria()

Same as in Bacterial Growth.

source

Agents.Models.hk — Method

hk(; 
    numagents = 100, 
    ϵ = 0.2
)

Same as in HK (Hegselmann and Krause) opinion dynamics model.

source

Agents.Models.opinion — Method

opinion(;dims=(10, 10), nopinions=3, levels_per_opinion=4)

Same as in Opinion spread.

source

Agents.Models.predator_prey — Method

predator_prey(;
    n_sheep = 100,
    n_wolves = 50,
    dims = (20, 20),
    regrowth_time = 30,
    Δenergy_sheep = 4,
    Δenergy_wolf = 20,
    sheep_reproduce = 0.04,
    wolf_reproduce = 0.05,
)

Same as in Predator-prey dynamics.

source

Agents.Models.schelling — Method

schelling(;
    numagents = 320,
    griddims = (20, 20),
    min_to_be_happy = 3,
)

Same as in Schelling's segregation model.

source

Agents.Models.social_distancing — Method

social_distancing(;
    infection_period = 30 * steps_per_day,
    detection_time = 14 * steps_per_day,
    reinfection_probability = 0.05,
    isolated = 0.5, # in percentage
    interaction_radius = 0.012,
    dt = 1.0,
    speed = 0.002,
    death_rate = 0.044, # from website of WHO
    N = 1000,
    initial_infected = 5,
    seed = 42,
    βmin = 0.4,
    βmax = 0.8,
)

Same as in Continuous space social distancing for COVID-19.

source

Agents.Models.sugarscape — Method

sugarscape(;
    dims = (50, 50),
    sugar_peaks = ((10, 40), (40, 10)),
    growth_rate = 1,
    N = 250,
    w0_dist = (5, 25),
    metabolic_rate_dist = (1, 4),
    vision_dist = (1, 6),
    max_age_dist = (60, 100),
    max_sugar = 4,
)

Same as in Sugarscape.

source

Agents.Models.wealth_distribution — Method

wealth_distribution(; 
    dims = (25, 25),
    wealth = 1,
    M = 1000
)

Same as in Wealth distribution model.

source

Agents.Models.wright_fisher — Method

wright_fisher(; 
    numagents = 100,
    selection = true
)

Same as in Wright-Fisher model of evolution.

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