What kinds of entities are in the model? By what state variables, or attributes, are these entities characterized? What are the temporal and spatial resolutions and extents of the model?

        An entity is a distinct or separate object or actor that behaves as a unit and may interact with other entities or be affected by external environmental factors. Its current state is characterized by its state variables or attributes. A state variable or attribute is a variable that distinguishes an entity from other entities of the same type or category, or traces how the entity changes over time. Examples are weight, sex,  age, hormone level, social rank, spatial coordinates or which grid cell the entity is in, model parameters characterizing different types of agents (e.g., species), and behavioral strategies. The entities of an ABM are thus characterized by a set, or vector (Chambers, 1993; Huse et al., 2002), of attributes, which can contain both numerical variables and references to behavioral strategies.

    One way to define entities and state variables is the following: if you want (as modelers often do) to stop the model and save it in its current state, so it can be re-started later in exactly the same state, what kinds of information must you save? If state variables have units, they should be provided. State variables can change in the course of time (e.g. weight) or remain constant (e.g. sex, species-specific parameters, location of a non-mobile entity). State variables should be low level or elementary in the sense that they cannot be calculated from other state variables. For example, if farmers are represented by grid cells which have certain spatial coordinates, the distance of a farmer to a certain service centre would not be a state variable because it can be calculated from the farmer’s and service centre’s positions. Most ABMs include the following types of entities:

        Agents/individuals. A model can have different types of agents; for example, wolves and sheep, and even different sub-types within the same type, for example different functional types of plants or different life stages of animals. Examples of types of agents include the following:  organisms, humans, or institutions. Example state variables include: identity number (i.e., even if all other state variables would be the same, the agent  would still maintain a unique identity), age, sex, location (which may just be the grid cell it occupies instead of coordinates), size, weight, energy reserves, signals of fitness, type of land use, political opinion, cell type, species-specific parameters describing, for example, growth rate and maximum age, memory (e.g., list of friends or quality of sites visited the previous 20 time steps), behavioral strategy, etc.

        Spatial units (e.g., grid cells). Example state variables include the following: location, a list of agents in the cell, and descriptors of environmental conditions (elevation, vegetation cover, soil type, etc.) represented by the cell. In some ABMs, grid cells are used to represent agents:  the state and behavior of trees, businesses, etc., that can be modeled as characteristics of a cell. Some overlap of roles can occur. For example, a grid cell may be an entity with its own variables (e.g., soil moisture content, soil nutrient concentration, etc., for  a  terrestrial  cell),  but  may  also  function  as  a  location,  and  hence  an  attribute,  of  an organism.

        Environment. While spatial units often represent environmental conditions that vary over space, this entity refers to the overall environment, or forces that drive the behavior and dynamics of all agents or grid cells. Examples of environmental variables are temperature, rainfall, market price and demand, fishing pressure, and tax regulations.

        Collectives. Groups of agents can have their own behaviors, so that it can make sense to distinguish them as entities; for example, social groups of animals, households of human agents, or organs consisting of cells. A collective is usually characterized by the list of its agents, and by specific actions that are only performed by the collective, not by their constitutive entities.

        In describing spatial and temporal scales and extents (the amount of space and time represented in  a  simulation),  it  is  important  to  specify  what  the  model’s  units  represent  in  reality.  For example: “One time step represents one year and simulations were run for 100 years. One grid cell represents 1 ha and the model landscape comprised 1,000 x 1,000 ha; i.e., 10,000 square kilometers”.