1. Basic Model

Content

This first step Illustrates how to write a model in GAMA. In particular, it describes how to structure a model and how to define species - that are the key components of GAMA models.

Formulation

• Definition of the prey species
• Definition of a nb_preys_init parameter
• Creation of nb_preys_init prey agents randomly located in the environment (size: 100x100)

Model Definition

Model structure

A GAMA model is composed of three types of sections:

• global: this section, that is unique, defines the "world" agent, a special agent of a GAMA model. It represents all that is global to the model: dynamics, variables, actions. In addition, it allows to initialize the simulation (init block).
• species and grid: these sections define the species of agents composing the model. Grid is defined in the following model step "vegetation dynamic";
• experiment: these sections define the execution context of the simulations. In particular, it defines the input (parameters) and output (displays, files...) of a model.

More details about the different sections of a GAMA model can be found here.

Species

A species represents a "prototype" of agents: it defines their common properties.

A species definition requires the definition of three different elements:

• the internal state of its agents (attributes)
• their behavior
• how they are displayed (aspects)

Internal state

An attribute is defined as follows: the type of the attribute and name. Numerous types of attributes are available: int (integer), float (floating-point number), string, bool (boolean, true or false), point (coordinates), list, pair, map, file, matrix, species of agents, rgb (color), graph, path...

• Optional facets: <- (initial value), update (value recomputed at each step of the simulation), function:{..} (value computed each time the variable is used), min, max

In addition to the attributes the modeler explicitly defines, species "inherits" other attributes called "built-in" variables:

• A name (name): the identifier of the species.
• A shape (shape): the default shape of the agents to be constructed after the species. It can be a point, a polygon, etc..
• A location (location): the centroid of its shape.

Behavior

In this first model, we define one species of agents: the prey species. For the moment, the agents of this species will not have any particular behavior, they will just exist and be displayed.

Display

An agent aspects have to be defined. An aspect is a way to display the agents of a species: aspect aspect_name {...}. In the block of an aspect, it is possible to draw:

• A geometry: for instance, the shape of the agent (but it may be a different one, for instance, a disk instead of a complex polygon)
• An image: to draw icons
• A text: to draw a text

In order to display our prey agents we define two attributes:

• size of type float, with for initial value:1.0
• color of type rgb, with for initial value: #blue. It is possible to get a color value by using the symbol # + color name: e.g. #blue, #red, #white, #yellow, #magenta, #pink...

Prey species code

For the moment, we only define an aspect for this species. We want to display for each prey agent a circle of radius size and color color. We then use the statement draw with a circle shape.

species prey {
    float size <- 1.0 ;
    rgb color <- #blue;
		
    aspect base {
	draw circle(size) color: color ;
    }
} 

global section

The global section represents a specific agent, called world. Defining this agent follows the same principle as any agent and is, thus, defined after a species. The world agent represents everything that is global to the model: dynamics, variables... It allows to initialize simulations (init block): the world is always created and initialized first when a simulation is launched (before any other agents). The geometry (shape) of the world agent is by default a square with 100m for side size, but can be redefined if necessary (see the Road traffic tutorial).

global attributes

In the current model, we will only have a certain number of preys thus we need to hold this number in a global or world's variable of type integer (int) which can be done as follows:

global {
    int nb_preys_init <- 200;
}

Model initialization

The init section of the global block allows initializing the model which is executing certain commands, here we will create nb_preys_init number of prey agents. We use the statement create to create agents of a specific species: create species_name + :

• number: number of agents to create (int, 1 by default)
• from: GIS file to use to create the agents (optional, string or file)
• returns: list of created agents (list)

Definition of the init block in order to create nb_preys_init prey agents:

init {
    create prey number: nb_preys_init ;
}

experiment

An experiment block defines how a model can be simulated (executed). Several experiments can be defined for a given model. They are defined using : experiment exp_name type: gui/batch { [input] [output]}

• gui: experiment with a graphical interface, which displays its input parameters and outputs.
• batch: Allows to set up a series of simulations (w/o graphical interface).

In our model, we define a gui experiment called prey_predator :

experiment prey_predator type: gui {
}

input

Experiments can define (input) parameters. A parameter definition allows to make the value of a global variable definable by the user through the graphic interface.

A parameter is defined as follows:

parameter title var: global_var category: cat;

• title: string to display
• var: reference to a global variable (defined in the global section)
• category: string used to «store» the operators on the UI - optional
• <-: init value - optional
• min: min value - optional
• max: min value - optional

Note that the init, min and max values can be defined in the global variable definition.

In the experiment, the definition of a parameter from the global variable nb_preys_init:

experiment prey_predator type: gui {
    parameter "Initial number of preys: " var: nb_preys_init min: 1 max: 1000 category: "Prey" ;
}

output

Output blocks are defined in an experiment and define how to visualize a simulation (with one or more display blocks that define separate windows). Each display can be refreshed independently by defining the facet refresh nb (int) (the display will be refreshed every nb steps of the simulation).

Each display can include different layers (like in a GIS):

• Agents species: species my_species aspect: my_aspect;
• Agents lists: agents layer_name value: agents_list aspect: my_aspect;
• Images: image image_file;
• Charts: see later.

Note that it is possible to define a opengl display (for 3D display) by using the facet type: opengl.

In our model, we define a display to draw the prey agents.

output {
    display main_display {
        species prey aspect: base ;
    }
}

Complete Model

msi.gama.models/models/Tutorials/Predator%20Prey/models/Model%2001.gaml

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See full example on GitHub