*Participants*^{2} refer to the objects of interest,
which are involved in the scenario or its model. These participants
may be real-world objects or conceptual objects, such as variables
that express features of real-world objects in a mathematical model.
For instance, a population of a species is a typical example of a
real-world object, and a variable that expresses the number of
individuals of this species forms an example of a conceptual object.
It is natural to group objects that share something in common into
classes. Participants are herein grouped into *participant
classes*, with each representing a set of participants that share
certain common features. Each class will be given a name for easy
reference.

*Relations* describe how the participants are related to one
another. As with participants, some relations represent a real-world
relationship, such as:

Other relations may be conceptual in nature, such as equation (2), which describes an important textbook model of logistic population growth [11]:

To be consistent with other compositional modelling approaches, this paper employs a LISP-style notation for relations. As such, the above two sample relations become:

*Assumptions* form a special type of relation that are employed
to distinguish between alternative model design decisions.
Internally, assumptions will be stored in the form of assumption nodes
in the ATMS (see section 3.3.1), but in the knowledge
base, assumptions appear as relations with a specific syntax and
semantics.

Two *types* of assumptions are employed in this article.
*Relevance assumptions* state what phenomena are to be included
in or excluded from the scenario model. Typical examples of
phenomena are the population growth and predation phenomena. The general format of a
relevance assumption is shown in
(3). The
phenomenon that is incorporated in the scenario model when describing
a relevance assumption is identified by
*name* and is specific to the subsequent
participants or relations. For example, relevance assumption
(4) states that the growth of
participant `?population` is to be included in the model.

*Model assumptions* specify which type of model is utilised to
describe the behaviour of a certain participant or relation. Typical
examples of model types include the exponential [26] and the logistic
[43] model types of population growth. The
formal specification of a model assumption is given in
(5). Often the
*name* in
(5) corresponds to the name of a
known (partial) model of the phenomenon or process being described.
The example in (6) states that the
population `?population` is being modelled using the logistic
approach.