System Dynamics

System Dynamics (SysDyn) is a modelling paradigm that represents a system as a set of stocks (quantities that accumulate over time) and flows (rates that change those quantities). The behaviour of the system emerges from the feedback loops between stocks and flows.

How SysDyn works in DisSModel

DisSModel implements system dynamics models as Model subclasses that run inside the same salabim environment as spatial models. This means a SysDyn model and a Cellular Automaton can share the same simulation clock and exchange state at every step.

Stocks are stored as instance attributes updated in execute().

Flows are computed each step from the current stock values and any parameters.

Feedback loops are expressed naturally as Python arithmetic — no special graph editor needed.

from dissmodel.core import Environment
from dissmodel.models.sysdyn import SIR
from dissmodel.visualization import Chart

env = Environment()
SIR(susceptible=9998, infected=2, recovered=0,
    duration=2, contacts=6, probability=0.25)
Chart(show_legend=True)
env.run(30)

Implementing your own SysDyn model

Subclass Model and update stocks in execute():

from dissmodel.core import Model, Environment
from dissmodel.visualization import Chart, track_plot


@track_plot("population", "red")
class Logistic(Model):
    def setup(self, population, capacity, rate):
        self.population = population
        self.capacity = capacity
        self.r = rate

    def execute(self):
        growth = self.r * self.population * (1 - self.population / self.capacity)
        self.population += growth

Then run it:

env = Environment(end_time=50)
Logistic(population=10, capacity=1000, rate=0.3)
Chart(show_legend=True, show_grid=True, title="Logistic Model")

env.run()

Available models

Model	Description
SIR	Susceptible–Infected–Recovered epidemiological model
Predator Prey	Lotka–Volterra ecological dynamics
Population Growth	Exponential growth with variable rate
Lorenz	Deterministic chaos — Lorenz attractor
Coffee	Newton's Law of Cooling