Information Technology Reference
In-Depth Information
D
RE1
Description of models
RE1 RE2 RE3 RE4
9
9
9
9
9
9
D
ISS-ENT-1
ISS-ENT-2
ISS-ENT-1
ISS-ENT-2
A
B3
B1
B2
(a) Controllable rule
(b) Observable rule
Fig. 1.
Example of controllable rule (a), and observable rule (b)
Definition 2.
An observable rule
r
o
that con-
sists of an original model
RM
and its potential evolution
RM
i
. The probability
that
RM
evolves to
RM
i
is a set of triples
RM, p
i
,RM
i
is
p
i
. All these probabilities should sum up to one.
RM
p
i
r
o
=
n
−→
RM
i
i
=1
Fig. 1 is a graphical representation of evolution rules taken from SWIM case
study. Left, Fig. 1(a) describes a controllable rule where a requirement model
containing IKMI (RE1) has four design choices: A, B1, B2, and B4 (see Table 1
and Table 2). Right, Fig. 1(b) shows that the initial model ISS-ENT-1 (including
RE1 and RE4) can evolve to ISS-ENT-2 (including RE1 to RE4), or remain
unchanged with probabilities of
α
and 1
−
α
. These rules are as follows:
r
c
=
RE
1
∗
B
3
A, RE
1
∗
B
1
,RE
1
∗
B
2
,RE
1
∗
→
→
→
→
r
o
=
ISS-ENT-1
α
1
−−−→
ISS-ENT-1
1
−α
1
−→
ISS-ENT-2
,
ISS-ENT-1
3.2 Game-Theoretic Account for Probability
Here, we discuss about why and how we employ game-theoretic (or betting
interpretation) to account for probabilities in observable rules.
As mentioned, each potential evolution in an observable rule has an associ-
ated probability; these probabilities sum up to 1. However, who tells us? And
what is the semantic of probability? To answer the first question, we, as sys-
tem Designers, agree that Stakeholder will tell us possible changes in a period
of time. About the second question, we need an interpretation for semantic of
probability.
Basically, there are two broad categories of probability interpretation, called
“physical” and “evidential” probabilities. Physical probabilities, in which fre-
quentist is a representative, are associated with a random process. Evidential
probability, also called Bayesian probability (or subjectivist probability), are
considered to be degrees of belief, defined in terms of disposition to gamble at
certain odds; no random process is involved in this interpretation.
To account for probability associated with an observable rule, we can use the
Bayesian probability as an alternative to the frequentist because we have no event
