Sensing

What internal and environmental state variables are individuals assumed to sense and consider in their decisions?

This concept addresses what information agents “know” and use in their behaviors. The ability to represent how agents can have limited or only local information is a key characteristic of ABMs. Here, say which state variables of which entities an agent is assumed to “sense”, and how. Most often, sensing is modeled by simply assuming that the agent accurately knows the values of some variables, neglecting how the agent gets those values or any uncertainty in their values. But ABMs can model the actual sensing process—how an agent gathers information about its world—when that process is important to the model’s purpose. And ABMs can represent uncertainty in sensing: you could assume, for example, that your agents base some decision on the sensed value of a neighbor’s variable, when that sensed value includes random noise. (In fact, sensing itself can be modeled as an adaptive behavior: agents can decide how much of their resources to invest to collecting information when more information supports better decisions but is costly to obtain.) Describing sensing includes stating which variables of which entities are sensed. The description must cover what an agent knows about its own state: we need to say explicitly which of an agent’s own state variables it is assumed able to use in its behavior. When agents sense variables from other entities, such as the spatial unit they occupy or other agents, we must specify exactly how they determine which entities they sense values from. In ABMs, sensing is often assumed to be local, but can happen through networks or can even be assumed to be global. Describe: • What state variables, of themselves and other entities, agents are assumed to sense and use in their behaviors. Say exactly what defines or limits the range over which agents can sense information. • How the agents are assumed to sense each such variable: are they assumed simply to know the value accurately? Or does the model represent the mechanisms of sensing, or uncertainty in sensed values? • The rationale for sensing assumptions.

In the case of the evacuation simulation, agents sense:

• obstacles in their environment (walls)
• doors they can use to reach another room
• other agents, as they cannot walk through other agents
• other agents in case they need to communicate
• Model trout are assumed able to sense habitat conditions and select habitat from among cells within a radius that increases with their size. Specifically, a trout can sense and potentially move to all cells whose centroids are less than sensing-distance from the centroid of the trout’s current cell; the value of sensing-distance is equal to bLa where L is the trout’s current length (cm) and a and b are model parameters with standard values of 50 and 2.0. Therefore, sensing is a mechanism for positive feedback of growth: trout that grow more rapidly can sense and potentially occupy a wider range of habitat, which may allow them to grow more rapidly (or, should they choose, to use safer habitat).
• Birds are assumed able to perfectly sense the current availability of both types of prey in cells within a specified radius of their current cell. This radius is constant over time and space and among birds; its value is the model parameter forage-radius (meters).