Population Dynamics Examples

Population dynamics models examine how births, deaths, and competition drive changes in group size. They reveal the forces that cause populations to boom when resources are plentiful and taper off or crash when constraints tighten.

These examples highlight classic mechanisms such as logistic growth and predator-prey cycles. By experimenting with them you can see how simple feedback loops create rich behavior that mirrors trends in ecology, finance, and social systems.

Select an example to explore it in the playground.

Beginner - Introductory Models

Fishery Simulation

A fishery simulation of stocks, flows, and feedback loops managing fish populations.

Level:Beginner

populationresource-managementsustainabilitymanagementecosystemstocks-flowsreinforcing-loopbalancing-looprenewable-resourcequota-policy

  • Stocks:population
  • Flows:births, quota
  • Feedback Loops:reproduction (reinforcing), quota (balancing)
  • Probes:population, quota, gap_to_capacity, extracted_total

Logistic Growth

A logistic growth simulation of population increase with saturation.

Level:Beginner

populationcapacitynonlinear-dynamicsreinforcing-loop

  • Stocks:population
  • Flows:growth
  • Feedback Loops:reinforcing adoption, saturation constraint
  • Probes:population, growth
Intermediate - Classic Dynamics

Predator-Prey (Lotka–Volterra, SciPy)

A predator-prey simulation using the Lotka-Volterra equations.

Level:Intermediate

populationecosystemnonlinear-dynamics

  • Stocks:prey, predator
  • Flows:prey_births, predations, predator_deaths
  • Feedback Loops:predation cycle
  • Probes:prey, predator

Predator-Prey (Lotka–Volterra, SimPy)

Discrete-time SimPy approximation of the predator–prey model.

Level:Intermediate

populationecosystemnonlinear-dynamics

  • Stocks:prey, predator
  • Flows:prey_births, predations, predator_deaths
  • Feedback Loops:predation cycle
  • Probes:prey, predator

SIR Model with Vaccination

An SIR vaccination simulation modeling epidemics with optional vaccination.

Level:Intermediate

populationreinforcing-loopbalancing-loopcontrol

  • Stocks:susceptible, infected, recovered
  • Flows:infections, recoveries, vaccinations
  • Feedback Loops:disease spread (reinforcing), herd immunity (balancing)
  • Probes:susceptible, infected, recovered
Advanced - Complex Simulations

Logistic Map

A logistic map simulation that iterates x_{n+1}=r*x_n*(1-x_n) to illustrate chaos.

Level:Advanced

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  • Stocks:x
  • Feedback Loops:growth with self-limiting term
  • Probes:x