are that system behavior can be modeled as a task (or function) network and that the environment can be modeled as a sequence of external events.
The architecture of Micro Saint as a tool or framework is less important than the architecture of specific Micro Saint models. Here we consider the architecture and functionality of a generic model.
The initial inputs to a Micro Saint model typically include estimates (in the form of parameterized probability distributions) for task durations and accuracies. They also include specifications for the levels of workload imposed by tasks. During the simulation, additional inputs are applied as external events that occur in the simulated environment.
Both human operators and the systems with which they interact are modeled by task networks. We focus here on human task networks, with the understanding that system task network concepts form a logical subset. The nodes of a task network are tasks. Human operator tasks fall into the following categories: visual, numerical, cognitive, fine motor (both discrete and continuous), gross motor, and communications (reading, writing, and speaking). The arcs of the network are task relationships, primarily relationships of sequence. Information is passed among tasks by means of shared variables.
Each task has a set of task characteristics and has a name as an identifier. The user must specify the type of probability distribution used to model the task's duration and provide parameters for that distribution. A task's release condition is the condition(s) that must be met before the task can start. Each task can have some effect on the overall system once it starts; this is called its beginning effect. Its ending effect is how the system will change as a result of task completion. Task branching logic defines the decision on which path to take (i.e., which task to initiate) once the task has been completed. For this purpose, the user must specify the decision logic in a C-like programming language. This logic can be probabilistic (branching is randomized, which is useful for modeling error), tactical (a branch goes to the task with the highest calculated value), or multiple (several subsequent tasks are initiated simultaneously). Task duration and accuracy can be altered further by means of performance-shaping functions used to model the effects of various factors on task performance. These factors can include personnel characteristics, level of training, and environmental stressors (see Chapter 9). In practice, some performance-shaping functions are derived empirically, while some are derived from subjective estimates of subject matter experts.
The outputs of a Micro Saint model include mission performance data (task times and accuracies) and workload data.