Modeling and Simulation

From Computer Science Wiki
Modeling & Simulation[1]

Modeling and simulation (M&S) is the use of models – physical, mathematical, or otherwise logical representation of a system, entity, phenomenon, or process – as a basis for simulations – methods for implementing a model (either statically or) over time – to develop data as a basis for managerial or technical decision making. M&S supports analysis, experimentation, and training. As such, M&S can facilitate understanding a system's behavior without actually testing the system in the real world.[2]

The big ideas[edit]

The Basic Model[edit]



Communication modeling and simulation (HL only)[edit]

helpful resources[edit]


  • Define the term computer modelling.
  • Identify a system that can be modelled.
  • Identify the variables required to model a given system.
  • Describe the limitations of computer (mathematical) models.
  • Outline sensible grouping for collections of data items, including sample data.
  • Design test-cases to evaluate a model.
  • Discuss the effectiveness of a test-case in a specified situation.
  • Discuss the correctness of a model by comparing generated results with data that were observed in the original problem.
  • Define the term simulation.
  • Explain the difference between a model and a simulation.
  • Describe rules that process data appropriately and that produce results.
  • Discuss rules and data representations and organization.
  • Construct simple models that use different forms of data representation and organization.
  • Design test-cases to evaluate a simulation program.
  • Outline the software and hardware required for a simulation.
  • Describe changes in rules, formulae and algorithms that would improve the correspondence between results and observed data.
  • Construct examples of simulations that involve changes in rules, formulae and algorithms.
  • Describe changes in data collection that could improve the model or simulation.
  • Discuss the reliability of a simulation by comparing generated results with data that were observed in the original problem.
  • Outline the advantages and disadvantages of simulation in a given situation rather than simply observing a real-life situation.
  • Discuss advantages and disadvantages of using a simulation for making predictions.
  • Define the term visualization.
  • Identify a two-dimensional use of visualization.
  • Outline the memory needs of 2D visualization
  • Identify a three-dimensional use of visualization.
  • Outline the relationship between the images in memory and the 3D visualization.
  • Discuss the time and memory considerations of 3D animation in a given scenario.

HL standards[edit]

  • Outline the use of genetic algorithms.
  • Outline the structure of neural networks.
  • Compare applications that use neural network modelling.
  • Compare different ways in which neural networks can be used to recognize patterns.
  • Identify the key structures of natural language.
  • Discuss the differences between human and machine learning when related to language.
  • Outline the evolution of modern machine translators.
  • Describe the role of chatbots to simulate conversation.
  • Discuss the latest advances in natural language processing.