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.

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.

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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.

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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.

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Agents.Models.growing_bacteria — Method

growing_bacteria()

Same as in Bacterial Growth.

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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.

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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 Model of predator-prey dynamics.

To access the Sheep, Wolf and Grass types, simply call

using Agents.Models: Sheep, Wolf, Grass

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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.

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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.

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Agents.Models.wealth_distribution — Method

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

Same as in Wealth distribution model.

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Agents.Models.wright_fisher — Method

wright_fisher(; 
    numagents = 100,
    selection = true
)

Same as in Wright-Fisher model of evolution.

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