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Building Blocks

These tutorials walk you through building a simple model of a population constrained by natural resources with Stella Architect. Each tutorial is broken out into small segments that tackle one step at a time. Through these tutorials you will come to learn the basic building blocks of Stella, data inputs and outputs, how to set the models simulation specs, as well as organizing and testing your model.

Placing a Stock

Learn how to place and label reservoir stocks, a common starting place for many models. A reservoir stock is a building block that accumulates and stores something. It collects whatever flows into it, minus whatever flows out of it.

Adding Documentation and Color

Learn how to add documentation and incorporate color in your model. The documentation tab allows you to describe a variable and include assumptions. Incorporating color helps organize your model or provides visual cues.

Drawing Inflows and Outflows

Learn how to place, connect and label inflows and outflows to stocks. Flows change the value of a stock by adding or subtracting from the stock.

Entering Values for Stocks and Flows

Start incorporating data to your model by adding values to the variables. For a model to simulate, all variables need to be defined with a constant or an equation. This tutorial will add constant values to all the variables currently in the model.

Choosing Run Specs

Learn how to set and adjust the models’ Run Specs including the simulation length, DT (time steps between calculations), simulation speed, the unit of time used and integration method.

Creating Graphs

Learn how to place a graph and add variables to view their results. Graphs can be used to display the results of multiple variables over a single simulation or comparative results from multiple simulations. This tutorial will add 3 variables to a graph and display the results over one simulation.

Scaling Variables

Learn how to create clean graphs through scaling. Multiple variables on one graph can become difficult to read if the variables have different scales. Setting up correct scales for individual variables in the graph allows for clean, easily read results.

Creating Tables

Learn how to place a table and add variables to view their results. Results can also be displayed in numerical output on tables. As with graphs, tables can display multiple variable results and multiple simulation results. This tutorial will add three variables to a graph and display the results over one simulation.

Formatting Tables

Learn how to format the data in tables. By default, tables use the format and precision settings of the variables displayed. For more uniform results across the table, each variable can be formatted to show the same precision and scale.

Placing Converters

Learn how to place and label converters for your model. A converter is a building block that converts inputs into outputs. Converters can be constant values, equations or can serve as a repository for graphical functions. When converters are added, the model begins to have feedback.

Adding Connectors

Learn how to connect the variables in your model. For models to simulate, some variables must be connected to others through a connector. Connectors indicate an immediate effect that one variable has on another.

Defining Converters

Learn how to define variables through constants and equations using required inputs. Many of the variables are still undefined and some that were defined with constant values now have required inputs due to the connector. If a variable is connected to another variable through a connector, the connected variable becomes a required input for defining the initial variable.

Defining Graphical Functions

Learn how to set up and incorporate graphical functions into your model. A graphical function displays the relationship between an input and an output. In the case of our model, the death rate is dependent on the resources/person. As the resources/person increases, death rates decrease. We can incorporate this relationship into our model using a graphical function.

Defining Ghosts

Learn how to create a copy of a variable using the “Ghost” tool. A Ghost is an alias of a model variable. Ghosts can be used as shortcuts to a variable, so you can use it elsewhere in your model.

Assigning Polarity

Learn how to assign polarity to connectors to help display cause-and-effect relationships. Assigning polarity offers a visual cue that indicates the cause-and-effect relationship between variables. Positive (+) polarity means that as the cause increases, the effect increases. Negative (–) polarity means that as the cause increases, the effect decreases.

Setting Up Sensitivity Analysis

Learn how to set up a sensitivity analysis for variables in your model. Sensitivity analysis allows you to test the validity of your model by generating data under different assumptions. It allows you to change one or more constant over multiple simulations and review the results on a comparative graph.

Running Sensitivity Analysis

Learn how to run Sensitivity analysis and view results. To view the results of the sensitivity analysis, a comparative graph is required. Comparative graphs allow data from multiple simulations to be displayed.

Creating Sector Frames

Learn how to create sector frames around certain variables in your model. Sector frames allow you to organize your model into different parts or to isolate parts for partial simulation. They are often useful tools when developing models in portions.

Running Sectors

Learn how to run individual sectors without running the full model. A benefit to sectors is the ability to run only the model structure within that frame. Only the model variables within that sector needs to be defined for the sector to run. External variables do not need to be defined.

Managing Data for Model Runs

Learn how to operate the data manager and save data from previous runs. The data manager allows you to manage the data from remembered simulation runs and to save that data for future use. Saved data sets can be labeled and reloaded to compare it to new data.

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