Schelling segregation model

using EasyABM

Step 1: Create Agents and Model

Lets create 200 agents with properties pos, mood and color. The keeps_record_of argument is list of properties that the agent will record during time evolution. The min_alike property of the model which specifies the minimum number of alike neighbors for an agent to be happy, is set to be 4.

@enum agentsfeeling happy sad
agents = grid_2d_agents(200, pos = Vect(1,1), color = cl"green", mood = happy, keeps_record_of=Set([:pos, :mood]))
model = create_2d_model(agents, agents_type = Static, space_type=NPeriodic, size = (20,20), min_alike = 4)

Step 2: Initialise the model

In the second step we initialise the agents by defining initialiser! function and sending it as an argument to init_model!. In the initialiser! function we randomly set agents color to be either cl"red" or cl"green" and set their positions randomly such that there is not more than one agent on each patch. Then we set the mood property of each agent to be happy or sad depending upon the number of alike agents in neighborhood.

function initialiser!(model)
    min_alike = model.properties.min_alike
    for agent in model.agents
        agent.color = [cl"red", cl"green"][rand(1:2)]
        x,y = random_empty_patch(model)   
        agent.pos = Vect(x,y)
    end    
    for agent in model.agents
        nbrs = neighbors_moore(agent, model, 1)
        num_alike = 0
        for nbr in nbrs
            if nbr.color == agent.color
                num_alike += 1
            end
        end
        if num_alike < min_alike
            agent.mood = sad
        end
    end
end

init_model!(model, initialiser = initialiser!)

Step 3: Defining the step_rule! and running the model

In this step we implement the step logic of the Schelling's model in the step_rule! function and run the model for 200 steps.

function step_rule!(model)
    min_alike = model.properties.min_alike
    for agent in model.agents
        count_alike = 0
        for nbr in neighbors_moore(agent, model,1)
            if agent.color == nbr.color
                count_alike += 1
            end
        end
        if count_alike ≥ min_alike
            agent.mood = happy
        else
            agent.mood = sad
            x,y = random_empty_patch(model) 
            agent.pos = Vect(x,y)
        end
    end
    return
end

run_model!(model, steps=200, step_rule = step_rule! )

Step 4: Visualisation

In order to draw the model at a specific frame, say 4th, one can use draw_frame(model, frame = 4, show_patches=true). If one wants to see the animation of the model run, it can be done as

animate_sim(model,agent_plots=Dict("happy"=> agent-> agent.mood == happy, "sad"=> agent-> agent.mood == sad), show_patches=true)

After defining the step_rule! function we can also choose to create an interactive application (which currently works in Jupyter with WebIO installation) as shown below. It is recommended to define a fresh model and not initialise it with init_model! or run with run_model! before creating interactive app.

agents = grid_2d_agents(200, pos = Vect(1,1), color = cl"green", mood = happy, keeps_record_of=Set([:pos, :mood]))

model = create_2d_model(agents, agents_type = Static, space_type=NPeriodic, size = (20,20), min_alike = 4)

create_interactive_app(model, 
    initialiser= initialiser!,
    step_rule=step_rule!,
    model_controls=[(:min_alike, "slider", 1:8)], 
    agent_plots=Dict(
        "happy"=> agent-> agent.mood == happy, 
        "sad"=> agent-> agent.mood == sad),
    frames=200, show_patches=true) 

Step 5: Fetch Data

In this step we fetch data of number of happy and sad agents at each time step as a dataframe using following line of code.

df = get_nums_agents(model, 
agent-> agent.mood == happy, 
agent-> agent.mood == sad,
labels=["happy","sad"], 
plot_result=true)

Individual agent data recorded during model run can be obtained as

df = get_agent_data(model.agents[1], model).record

References

1.) https://en.wikipedia.org/wiki/Schelling%27smodelof_segregation