Environmental Engineering Reference
In-Depth Information
c) Process Scheduling: How to Organize the Regular Update of Individual
Processes and Properties
The above part focussed largely on structural descriptions. Essential for the life-
history model of an organism is how exactly the update process of all individual
state variables is organized. This constitutes the dynamic part and can be referred to
as “process scheduling”. To coordinate the concurrent execution of a larger number
of entities in a model requires a loop control structure within the program code of
the particular class (respectively the instantiated objects), which is iterated as long
as the object has the internal status as a living entity during the simulation run. To
distinguish active and no longer active objects requires the introduction of a
Boolean variable, e.g. with the name “ALIVE” as one of the object's states. A
Boolean variable can store the values of either TRUE or FALSE. The control
mechanism that uses the distinction, is referred to as “life loop”.
The “life loop” of each currently active individual entity is repeated (iterated) as
long as this variable has the Boolean status TRUE. Otherwise, the object will be
terminated, deleted from the storage and the storage space released as being freely
available. From a top-down point of view, the execution of any specific activity can
be made dependent on distinctive conditions that relate to the internal state of the
individual entity (like the energetic state, age, reproductive stage) and with the
external situation (e.g. availability of food, presence of predators, daytime or time
of the year). It is thus necessary to implement an algorithm to determine which of
the possible activities is to be executed for a given situation.
These scheduling algorithms may range from very simply to very complex. A
simple scheme would e.g. execute all activity and physiology procedures in the
same order during each passing of the life loop as a “cyclic activity control”. This
activity scheduling mechanism is adequate for configurations where it is not
necessary to evaluate complex behavioural alternatives that may require changes
of the sequence of activities (e.g. models concentrating on movement behaviour,
see Sect.
12.3
, the IPP example).
To model complex behavioural patterns, a further elaborated decision algorithm
is required. For instance, with a “priority driven activity control” it is possible to
assign a variable corresponding to each activity which indicates its execution
priority. Consequently, during each life loop sequence the activity with the highest
priority value gets executed. In the course of execution, the priority of the particular
activity is reduced, while the idle activity alternatives may accumulate successively
higher priority values. Thus complex behavioural decisions (including time-energy
budgets, analysis of behavioural trade-off's in life history) can be represented by
considering external and internal states in relation to the supposed execution time
for each activity (Breckling et al. 1997).
In an individual-based model, the ecologically relevant state of any simulated
entity results from all performed activities in which the relevant inner states of the
individual have been evaluated in feedback processes with all relevant external
states (e.g. “environment”). The activities of an individual thus can alter its own
states (e.g. if hungry through food search) but can also influence the environment,
