2.5 Simulations

2.5.1 Simulations in Python

1. - The way statements are sequenced and combined in a program determines the computed result. Programs incorporate iteration and selection constructs to represent repetition and make decisions to handle varied input values.

1. - How can we store data in a program to solve problems?
  - What might happen if you completed the steps in your regular morning routine to get ready and go to school in a different order? How might the reordering affect the decisions you make each morning?
  - How do video games group different actions for a player based on what key is pressed on the keyboard or controller? How do apps group different actions together based on user interaction, such as pressing buttons?
  - What types of problems can be solved more easily with a computer, and what types can be solved more easily without a computer? Why?

Explain how computers can be used to represent real-world phenomena or outcomes.
Compare simulations with real-world contexts.

Simulations are abstractions of more complex objects or phenomena for a specific purpose.
A simulation is a representation that uses varying sets of values to reflect the changing state of a phenomenon.
Simulations often mimic real-world events with the purpose of drawing inferences, allowing investigation of a phenomenon without the constraints of the real world.
The process of developing an abstract simulation involves removing specific details or simplifying functionality.
Simulations can contain bias derived from the choices of real- world elements that were included or excluded.
Simulations are most useful when real-world events are impractical for experiments (e.g., too big, too small, too fast, too slow, too expensive, or too dangerous).
Simulations facilitate the formulation and refinement of hypotheses related to the objects or phenomena under consideration.
Random number generators can be used to simulate the variability that exists in the real world.