
Let's see how Machinations can help us create models, run simulations and make predictions to make informed decisions.
We are going to use as an example a campaign for Willy Wonka's Chocolate Factory. Will you be able to visit Wonka's factory with a limited amount of money and time?
Sometimes it might be too hard to run an experiment in a real system. Maybe the system is too complex, tests may be destructive, it may take a lot of time or it might be too expensive.
In those cases, it is recommended to build a model, which is an abstract representation of the real system, and then run simulations on the model with specific parameters to get results, analyze them and make informed decisions.
In Machinations, the execution time of the simulation is counted in steps.
Steps are the increment of time in the simulation. This means that when a simulation runs, everything happens step by step. In each step the system will have a specific state which is shown through different components. For example, in the Willy Wonka diagram, in one step the number of chocolate bars without golden tickets can be 4, then 10, then 11 and so on.
Welcome to the Machinations Essentials course!
You are going to look at the basics of Machinations and create your first diagram working on a Loot and Craft project. You will understand how to use Machinations to analyze and create models to solve problems and make informed decisions. By the end of this course, you will have a good comprehension of the most important components, resources, steps, simulations and predictions.
In the first lesson you will learn the basics of Machinations to understand how simulation models work and how predictions can help you make informed decisions.
In the second lesson you will create your first Machinations project, in which you are going to design, simulate and analyze a model for a basic loot and craft system. This covers the basic components and skills required to build a simple Machinations diagram.
At the end of the course you can take a test to assess your knowledge about Machinations, simulations and the components.
Overall, in this course we will cover the following topics:
Simulation models and abstraction
Steps
Resources
Performing a preliminary analysis: defining data, results, steps, runtime and resources
Running simulations with interactive plays
Executing predictions to extrapolate results
Pools
Sources
Resource connections
Randomness: dice throws and random functions
Converters
Triggers: automatic and passive
Diagram styles and best practices
Charts