Information Technology Reference
In-Depth Information
table 14.4
Future Vision of iA
2
(Continued)
FVF
Description
IA Future
Connections
Butterfly effect and
pinball effect; the subtle
and sometimes frantic
results from seemingly
innocuous actions
IA may assist to distinguish
correlations in emerging events. The
nature of these correlations provides
insight in how to address the trigger
event (e.g., cause) and the resulting
event (e.g., effect). Models will
emerge to represent complex
behavior for IA as a method to
reduce business risk. Models that
include direct cause and effect as
well as indirect and more subtle
relationships of trigger events and
resulting events, e.g., systems
dynamics models.
Chaos
An attempt to bound and
explain the complexities
of random behavior
IA provides a foundation to develop
more sophisticated uncertainty
management models to anticipate
and accommodate the random
factor of events. The objective will
not be uncertainty elimination (this
is unrealistic), but rather heightened
awareness of issues for contingency
planning to manage uncertainty.
Bayesian probability is a mathematical concept that applies a
degree of belief
fac-
tor to the probability calculations. This is an overlap with the concept of managing
uncertainty, where Bayesian probability provides a variable with which to insert
uncertainty (or certainty) into a probability calculation. The use of Bayesian prob-
ability will contribute to the modeling of risk and how to address risk. These formal
risk models will use the IA
2
Frameworks to define the problem space as well as to
formulate solution options.
Operations research (OR) is a mathematical tool to assist in generating optimal
solutions to complex problems. OR is a decision support tool for many complex
business problems. It focuses on specific elements of the problem at a static point in
time; that is, OR does not factor in time delays inherent in a complex system.
Systems dynamics is a methodology for studying complex feedback systems
where the results of one process (A) affect another process (B) that is also affected
by processes C and D, and then the results of process B affect process E, which in
turn affects process A. The results of process E take time to reach process A, but
as they do the new results of process A kick of another set of dynamic feedback.
