Dealing with Dynamic Systems:
SINUS_________ |
Blockl__________________ | ||||
Week............ |
1 I |
_2__ |
ΓΓT |
4 I |
5____ |
State: | |||||
Gaseln..... |
16∞ |
1700 |
1800 |
1900 |
2000 |
Schmorken.. |
900 |
957 |
1013 |
1055 |
1096 |
Sisen...... |
300 |
293 |
286 |
281 |
306 |
Intervention: | |||||
OIschen.... |
10 |
10 |
10 |
10 |
_? |
Mukem..... |
12 |
11 |
13 |
28 |
? |
Raskeln.... |
-1 |
-1 |
-5 |
-5 |
? |
Press "space bar" Io select an intervention, choose a value
and then press "return”
29
Figure 2. Screen display of DYNAMIS when presenting system SINUS after 4 weeks (=
trials) on the first block. The upper part shows the state of the three endogenous variables, the
lower part shows past interventions.
Each block consists of a certain number of trials (referred to as “weeks”
in the cover story) which all depend on each other. From one block to another
the system is reset to the same starting values. From time to time we measure
the knowledge that has been acquired so far by asking the subjects for a
graphical representation of their structural knowledge (“causal diagrams”). In
the second phase, called “knowledge application phase," the subject has to
reach a defined system state and try to maintain the variable values as close
as possible to the values defined as goal states. In this phase, we measure the
quality of the operator’s control by assessing the distance between the current
and the goal values for all endogenous variables. Some comments on meas-
uring structural knowledge and system performance seem necessary at this
point because this is central to our studies. A review on techniques for
knowledge assessment can be found in Kluwe (1988). Also, Rouse and
Morris (1986) discuss some of the diagnostic problems in more detail.
2.3 Measuring Structural Knowledge and System Performance^
Starting with control performance quality, the goal is to determine how well
a given goal state is approximated by the operator’s interventions. The
classical approach requires the measurement of the deviation from the target
system state in terms of the root mean squares criterion (RMS). This indicator
reflects the mean deviation, independent of sign. The weights of individual
3 This section follows the presentation as given in Funke (1991).