TaxSystem
Study this example to learn about
- Parallelizing a model where agents are simulated independently
Overview of TaxSystem
This model is a toy tax system simulation with the main purpose to explain how parallelization of the agent step can be achieved when agents don't interact with each other, but only evolve following a set of rules. A tax system is a good candidate to show that because taxes can have heterogeneous effects on the wealth of the agents depending on some of their characteristics, and at the same time there is no need for any interaction between agents to simulate such a system.
using Agents, Random
the Payer agent contains the necessary characteristics to simulate its taxation in this toy model. In a real application, the properties can be much more than these
@agent struct Payer(NoSpaceAgent)
alive::Bool
age::Int
wage::Float64
wealth::Float64
end
Here we create the model, which contains a certain number of agents with a random age and a random wage each. The accumulated wealth at the start of the simulation is zero.
function taxsystem(; nagents = 100000)
model = StandardABM(Payer; model_step!, container = Vector, rng = Xoshiro(42))
for _ in 1:nagents
add_agent!(model, true, rand(abmrng(model), 20:30), rand(abmrng(model), 500:5000), 0)
end
return model
end
taxsystem (generic function with 1 method)
To parallelize the stepping function we use a model step because it allows the loop updating the agents. Since the tax_agent!
function evolves each agent without updating any other agent inside the function, it is thread-safe and so the simulation can be easily parallelized with Threads.@threads
function model_step!(model)
Threads.@threads for agent in allagents(model)
tax_agent!(agent)
end
end
model_step! (generic function with 1 method)
In the simulation agents only get older (eventually dying) and accumulate their residual wage after paying taxes
function tax_agent!(agent)
!agent.alive && return
agent.age += 1
agent.wealth += agent.wage - (agent.wage < 1000 ? 0.0 : agent.wage * 0.2)
agent.alive = rand(abmrng(model)) < 0.02 ? false : true
end
tax_agent! (generic function with 1 method)
model = taxsystem()
StandardABM with 100000 agents of type Payer
agents container: Vector
space: nothing (no spatial structure)
scheduler: fastest
Run the example with a different number of threads to see the speed-up, on the tested machine the model with 6 threads was 3.4 times faster than with 1 thread
@time step!(model, 100);
2.105507 seconds (4.39 M allocations: 69.448 MiB, 3.60% compilation time)