Building Realistic Models with Adaptive Rules
- Complex simulations require more than static rules.
- They often involve dynamic changes in rules, formulas, or algorithms based on evolving conditions.
Example
- In a traffic simulation, speed limits might change based on weather conditions.
- If it starts raining, the simulation reduces speed limits and increases stopping distances.
Step-by-Step Example: Ecosystem Simulation
1. Defining the Initial Rules (Prey-Predator)
- Predators gain energy by eating prey.
- Prey reproduce if they have enough energy.
- Predators lose energy over time.
Example
- Rule:
- If a predator's energy drops to zero, it dies.
- If a prey's energy exceeds a threshold, it reproduces.
2. Introducing Dynamic Changes
- Seasonal Changes: In winter, prey reproduction rates decrease.
- Resource Scarcity: If prey population drops below a threshold, predator starvation rates increase.
Example
- Formula Update:
- Prey reproduction rate = 0.8 × normal rate during winter months.
- Algorithm Change: If prey population < 20, predator death rate increases by 50%.
3. Implementing the Simulation
- Use conditional statements to adjust rules dynamically:
- If (season == winter) { reduce reproductionRate; }
- If (preyPopulation < threshold) { increase predatorDeathRate; }
Hint
Break down the simulation into modular functions. This makes it easier to update rules without rewriting the entire code.
4. Testing and Refining
- Run the simulation under different scenarios:
- Normal conditions
- Winter season
- Low prey population
- Adjust parameters to ensure realistic outcomes.
Note
- Avoid hard-coding values.
- Use variables for parameters like reproduction rates or death rates to make the simulation flexible.
Why This Matters
- Complex simulations are essential for modeling real-world systems where conditions change over time.
- They help researchers and decision-makers predict outcomes and test scenarios without real-world risks.
