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When considering how knowledge affects personal decision
making and reasoning, we need to understand what knowledge is
and how it relates to information. We distinguish between knowledge
and information by recognizing that they are fundamentally
different. Information consists of data organized to characterize
a particular situation, condition, context, challenge, or opportunity.
Knowledge consists of facts, perspectives and concepts, mental reference
models, truths and beliefs, judgments and expectations,
methodologies, and know-how. In part, knowledge also consists of
understanding how to juxtapose and integrate seemingly isolated
information items to develop new meanings —to create new insights
with which to approach effective handling of the target situation.
We use information to describe and specify what things are. We
use information to describe a situation and its context as they exist
and develop. We use information in the form of data tables to
describe everything from the physical characteristics of metals to
today’s and yesterday’s stock market statistics and projections of
its future performance. Clearly, much information is created by
the application of knowledge to describe and explain. However, that
does not make information knowledge.
We use knowledge to evaluate and handle situations, decide how
we, for example, use physical tables, or assess how to trade our
investment portfolio given stock market information. We use knowledge
to assess, decide, problem-solve, plan, act, and monitor.
Actionable knowledge is possessed by humans as well as by other
active entities (agents) such as process control computers that are
programmed to take actions to manipulate process variables to
achieve a desired performance. Actionable knowledge is used to
receive information and to recognize and identify; analyze, interpret,
and evaluate; synthesize and decide; plan, implement, monitor, adapt,
and act. In other words, knowledge is used to reason to determine
what a specific situation means, how it should be handled, and to
carry out the resulting decision in action. In this context, knowledge
serves two purposes: (1) methodological knowledge controls the
reasoning process; (2) domain knowledge provides the content of
reasoning. In addition, information is needed to describe the state of
the situation that is the subject of reasoning.
Passive knowledge may exist in repositories —in systems and procedures,
books, documents, databases, and in many other forms. Structural intellectual capital consists mostly of passive
knowledge except when embedded in active agents such as computerbased
action systems. We use passive knowledge when it is obtained
by an active agent and is operationalized. It can, for example, be
operationalized and activated by a person who learns about it by
reading a description of it, reasons with it, and acts on it. In a less
obvious manner, it can be embedded in an organizational structure
through specified systems and procedures that are operationalized by
people observing managerial intents through their daily actions.
Knowledge is accumulated and integrated and held over time by
receiving new information, using prior knowledge to interpret it and
create hypotheses about its meaning, relevancy, and acceptability. If
found “believable,” the new knowledge can be accepted and internalized
by establishing its relationship (associations) and deeper
meanings relative to what already is known. This is the case with
personal knowledge when the process takes place in a person’s mind.
It is also the case with creating structural intellectual capital (organizational knowledge)
when knowledge is acquired and incorporated in repositories.
A brief, practical example portrays differences between information
and knowledge. In
this system, information on the operating state of the process is
obtained continuously by the computer. Knowledge from process
experts is embedded in, and operationalized and activated by, the
process control computer programs to automate operations. The
experts provide personal knowledge and deep understanding of physical
and operational principles and specific cases on how to deal both
with routine and undesired operating situations. They pool their
precise process knowledge with that of other experts, who have
embedded general knowledge on optimization and control principles
in teaching materials, scientific papers, textbooks, and generic computer
software used to generate the control algorithms. That knowledge
is assembled by programmers and built into control programs.
The static and dynamic operating history of the process is analyzed
by conventional, but sophisticated, statistical methods or
advanced knowledge discovery in databases (KDD) to obtain data on
selected process characteristics, including process dynamics. This historical
knowledge becomes part of the control algorithms embedded
in the control computer. Hence, the process control computer uses
historical knowledge to regulate and control the process as a “business-
as-usual” process. The computer cannot create new knowledge
or innovate or improvise even when required.
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