Decision Support Systems
Much of this section is summarized from:
Barrett, J.R. and C.H. Castore. 1989. Decision making and
decision support. In: Barrett, J.R. and D.D. Jones (eds.)
Knowledge Engineering in Agriculture. ASAE Monograph No. 8,
ASAE, St. Joseph, MI.
Decision making involves the mind, the brain, sensory mechanisms,
perception, cognition and the expression of results. An
feels, perceives, thinks, remembers and reasons in an adaptive
conscious and unconscious manner. Knowledge is reviewed,
perhaps intuitively, ordered according to importance,
and alternatives identified and ranked. Short- and long-term
consequences are explored and choices made. Systems analysis
often provides key inputs to the above process.
The human decision making process uses symbolic reasoning.
It is not done by solving sets of equations as in LP
or performing other complicated mathematical computations.
Instead, symbols chosen to represent problem concepts are
used and then various strategies are applied and heuristics
are invoked to manipulate these symbols.
A symbol is a string of characters that stands for some
real-world entity or concept. Symbols may be likened
to pictures that the mind sees or has fixed in memory.
This is in contrast to values that are measured and quantified
numerically. Of course, a number may be a symbol, but a
symbol does not have to be a number.
Much of the focus of artificial intelligence
and expert systems has been on the definition of
symbols and their manipulation for decision making purposes.
When an individual is confronted with a problem, several things
Some happen immediately while others are delayed.
If the situation has not been experienced before, there
are three possibilities that are explored to see
if something might be done differently.
The circumstances may be comparable to others faced,
but since dissatisfaction occurred with the results of
actions in those cases, the decision is made to
investigate other alternatives.
The problem is seen as somehow different enough from
any dealt with in the past that examination of options
available is warranted.
Portions of situations are recognized as having been
dealt with either successfully or unsuccessfully,
but there are additional new elements in the problem
at hand; and it may not be clear if previously successful
actions are appropriate, or how they might be
effectively combined to produce success.
In all three cases, the individual will begin a sequence of
At all stages along the way in this chain of actions,
the individual is confronted with feedback leading to
a revision of views on what
actions are available, and their relative merits
defining the problem to be solved or establishing
the parameters of the decision to be made
generating a list of possible alternative actions
evaluating, as best as possible, the relative risks,
costs and benefits associated with the various options
carrying out the alternative(s) chosen
Experts and Their Decision-Making Process
Experts follow much of these same processes,
only they have more experience and have more knowledge
than those less expert.
When experts make intelligent decisions they systematically
consider knowledge that has been experientially acquired
along with information that is sensed and observed.
An expert might be defined as:
An expert is a person who, because of training and experience,
is able to do things the rest of us cannot; experts are not
only proficient but smooth and efficient in the actions
they take. Experts know a great many things and have tricks
and caveats for applying what they know to problems and tasks;
they are also good at plowing through irrelevant information
to get at basic issues and they are good at recognizing problems
they face as familiar instances. Underlying the behavior of
is the body of operative knowledge we have termed expertise. It
is reasonable to suppose, therefore, that experts are the
ones to ask when we wish to represent the expertise that
make their behavior possible.
How Decision Support Systems Work
The conceptual components of decision support expert
systems are given in the figure below. No problem
encompasses all the blocks, yet all fit
within the structure.
The end user determines the question that is addressed
by the program. Different users will have different questions.
The program developers' dilemma is to develop a program
that makes sense to the end user, whomever they may be.
Good advice is "know thy user for he is not thyself."
Problem Definition and Formulation
Development of a clearly defined problem and the establishment
of objectives are discussed in
Building Expert Systems. Two sets of information are
needed to start a decision support program: one characterizes
the situation and the other describes the general
domain where the problem resides.
Description of Situation
This block represents the inclusion of information,
through symbol definition, describing the specific
conditions of the situation. For example,
the condition of a diseased plant or animal, the specific
field description, the farm status, the economic picture, etc.
Description of Domain
This represents the inclusion of information describing
the more generalized domain of knowledge that will be needed
to allow solution of the problem. This is descriptive
on a time basis.
Processing by Computer
This block represents the flow through the program
as ordered by the logic of the expert and the programmer
and accomplished in the machine.
Experts' Reasoning and Logic
The knowledge structure that represents the problem
solving logic of the expert is usually kept separate
from the coding that arranges the machine specific
input and output.
Knowledge, Information, Data Sources
The facts that are used in the logic of the solution
process may be in a variety of forms, including tables,
algorithms, images, other programs, sensors, etc.
Description of Status. These symbolically describe the
circumstances under consideration.
Data Sets. These are databases, assumed to be valid
Experientially Learned Information. This has been accumulated
over time case-by-case and has associated levels of certainty
Neighbors, Family Members, Associates.
These facts and factors are confounding, critical and given
Deterministic Factors. Clearly, without question, yes-no
Algorithmic Relationships. These are experimentally derived
functions and have associated probabilistic accuracies.
Models and Simulations. These are descriptions of
systems that may interface and can be mathematical
or otherwise descriptive.
Knowledge, as assimilated information, is structured
through knowledge engineering to help solve ill-structured,
ill-defined agricultural problems.
Presentation of Alternatives
The usual way of bringing possibility and uncertainty into
the process is to present ranked alternatives to the user
for a choice to be made.
The correctness of the proposed choice as evaluated
by the expert and the end user, can be expressed relatively.
This handling places the decisions that involve spending
resources in the minds of the managers, or other users
and not in the hands of the modelers!
Selection by User
Here the user selects an alternative for processing
through the remainder of the program.
Projection of Consequences
The projection of the short- and long-term effects of a decision
is an area that needs more development. Work is being done
to bring the human considerations
into the process that are necessary to make accurate,
believable projections. These projections would allow
the possibility of examining the effects of choices through
Social and Economic Factors
The interaction of social and economic factors are inputs that
are used in the projection of consequences.
Information for Future Use
This may be as hard copy of the output
from the running of the program
or as information stored in memory for use
in initiating the next run of the program.