electronic warfare self-protection of battlefield helicopters - Aaltodoc

COLLECTION DELAY
NEW information
CONVERSION
EFFICIENCY
150 150 pieces Pieces
3,000 Pieces
3,000 pieces
112.5 pieces
2,250 pieces
112.5 Pieces
2,250 Pieces
75 75 pieces Pieces
1,500 1,500 pieces Pieces
37.5 37.5 pieces Pieces
750 Pieces
750 pieces
0 pieces
0 pieces
rate of
information gain
+
ability to utilize new
information
INITIAL INFORMATION
+
41
0 Pieces
0 Pieces
0 8 16 24 32 40 48
56 64 72 80 88 96 104 112 120 128 136 114 144
Time (Month)
Time (Month)
rate of information
degradation
+
RATE OF
OBSOLESCENCE
Information
Dynamic behavior of Information and Knowledge
Dynamic behavior of Information and Knowledge
+
LEARNING DELAY
Information : NEW INFORMATION is constant Pieces
Information: Information : NEW NEW INFORMATION INFORMATION switches is constant Pieces
Knowledge : NEW INFORMATION is constant Pieces
Information: NEW INFORMATION switches Pieces
Knowledge : NEW INFORMATION switches Pieces
Knowledge: NEW INFORMATION is constant Pieces
Knowledge: NEW INFORMATION switches Pieces
INITIAL KNOWLEDGE
rate of knowledge
change
LEARNING
EFFICIENCY
Knowledge
Comment:
The simulation of the levels
”Information” and ”Knowledge” is
run for two cases of ”NEW
INFORMATION”. In the first case
”NEW INFORMATION” has a
constant value of 10 Pieces/Month.
In the second case it starts at +10,
switches to -10 at t=36 months and
returns to +10 at t=42 months
(dash-dot lines). The initial value
of ”Information” is 100 Pieces, and
the initial value of ”Knowledge” is
1000 Pieces. Due to the delays
(”COLLECTION DELAY”=12
months, ”TRANSFORMATION
DELAY= 24 months) the effect of
the switch on ”Information”
reaches its maximum only at
approx. t=57 months, whereas its
effect on ”Knowledge” is minimal.
Figure 8: FSD model of the information process/system in Figure 7, with dynamic behavior for
hypothetical parameter values. FSD simulation requires additional parameters (exogenous
variables) that clutter the model compared with the qualitative models in Figure 7. Assumed
parameter values are given in Appendix 2, Table 2-1.
There are aspects that negate the advantages of FSD. First, FSD is a controversial
method with a small group of practitioners, primarily at some business schools. For
the present work the obscurity of FSD introduces the risk of having ideas rejected as
being too enigmatic. 38 This risk is pointed out in LeFèvre, which claims that because
of the unnatural appearance of FSD models they fail to capture the interest of the
people who have the real knowledge of the system. Thus, knowledge extraction
becomes a bottleneck and the credibility of the result is seen as quite dubious by the
end-users. [Fev97] 39 Second come weaknesses in the FSD paradigm that may limit
38 An example of the criticism against FSD is the claim that the treatise Limits to Growth, by
Meadows et al., substitutes elaborate causal structures for missing data, and that the models lack
empirical validity [And01]. A critical review of Forrester’s Urban Dynamics claims that the
conclusions of the book are outright wrong (including its “counterintuitive” results), that there is a
gulf between what the urban model is and what Forrester claims it to be, and that Forrester’s
knowledge of cities is limited to what he has learned from the former mayor of Boston [Lee73]. A true
dialogue between the differing views does not seem to have emerged, and thus in 1999 Meadows
[Mea99] claims that “Forrester was right”. A systems engineering view on FSD is that it is “(…) a
technique viewed with the gravest suspicion in some industrial circles, owing to its potentially
imprecise approach to modelling—although it is that very imprecision which makes system dynamics
potentially useful for addressing softer issues” [Hit92 p.22].
39 A) The comment in LeFèvre [Fev97] points to different usages in the traditional FSD community,
which consists mainly of individuals with background in sociology and management, and in the