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If Designer does not believe Stakeholder, she will borrow money from National
Bank, and has to return
M
(1 +
r
). Then, Stakeholder is willing to buy
C
with
M
(1 +
p
). In this case, Designer can earn
M
(
p
r
).
Suppose that Designer has an initial capital of
K
0
. After round i-
th
of the
game, she can accumulate either
K
i
=
K
i−
1
+
M
(
r
−
r
),
depend on whether she believes Stakeholder or not. Designer has a winning
strategy if she can select the values under her control (the
M
$) so that she
always keeps her capital never decrease, intuitively,
K
i
>
=
K
i−
1
for all rounds.
The law of large numbers here corresponds to say that if unlikely events
happen then Designer has a strategy to multiply her capital by a large amount.
In other words, if Stakeholder estimates Reality correctly then Designer has a
strategy for costs not to run over budget.
−
p
)or
K
i
=
K
i−
1
+
M
(
p
−
4 Making Decision: What Are the Best Things to
Implement
One of the main objectives of modeling evolution is to provide a metric (or set of
metrics) to indicate how well a system design can adapt to evolution. Together
with other assessment metrics, designers have clues to decide what an “optimal”
solution for a system-to-be is.
The major concern in assessment evolution is answering the question: “Whether
a model element (or set of elements) becomes either useful or useless after evolu-
tion?”. Since the occurrence of evolution is uncertain, so the usefulness of an ele-
ment set is evaluated in term of probability. In this sense, this work proposes two
metrics to measure the usefulness of element set as follows.
Max Belief.
(MaxB): of an element set X is a function that measures the max-
imum belief supported by Stakeholder such that X is useful to a set of top
requirements after evolution happens. This belief of usefulness for a set of
model element is inspired from a game in which Stakeholder play a game
together with Designer and Reality to decide which elements are going to
implementation phase.
Residual Risk.
(RRisk): of an element set X is the complement of total belief
supported by Stakeholder such that X is useful to set of top requirements
after evolution happens. In other words, residual risk of X is the total belief
that X is not useful to top requirements with regard to evolution. Impor-
tantly, do not confuse this notion of residual risk with the one in risk analysis
studies which are different in nature.
Given
an
evolutionary
requirement
model
RM
=
RM, r
o,
r
c
where
RM
p
i
is an observable rule, and
r
c
=
ij
RM
i
is a
r
o
=
i
∗
→
−→
RM
i
RM
ij
controllable rule, the calculation of max belief and residual risk is illustrated in
Eq. 1, Eq. 2 as follows.
