Selection of a principle of Choice
A principle of choice is a decision regarding the acceptability of a solution
approach.
Normative Models
Normative implies that the chosen alternative is demonstrably the best of
all possible alternatives.
To find it, one should examine all alternatives and prove that the one
selected is indeed the best.
o Get the highest level of goal achievement from a given set of
resources.
o Find the alternative with the highest ratio of goal attainment to cost.
o Find the alternative with lowest cost that will fulfill required level of
goals.
Normative decision theory is based on the following assumptions related
to rational decision makers.
o Humans are economic beings whose objective is to maximize the
attainment of goals.
o In a given decision situation, all variables alternative courses of
action and heir consequences are known.
o Decision maker have an order or preferences that enables them to
rank the desirability of all consequences of the analysis.
Sub Optimization
If a sub optimal decision is made in one part of the organization without
consideration of the rest of the organization, then an optimal solution from
the point of view of that part may be inferior for the whole.
Once a solution is proposed, its potential effects on the remaining
departments of the organization can be checked.
Descriptive Models
Descriptive models describe things as they are or as they are believed to
be.
Information flow Environmental impact analysis
Scenario analysis Simulation (Different types)
Financial planning Technological forecasting
Complex inventory decisions Waiting line (queuing) management
Markov analysis (Predictions)
Good enough or satisfying
Organization or individual involves a willingness to settle for a satisfactory
solution, "something less than the best".
In a satisfying mode, the decision maker sets up an aspiration, goal, or
desired level of performance and then searches the alternatives until one
is found that achieves this level.
Developing (generating) alternatives
This can be a lengthy process that involves search and creativity.
Generating alternatives is heavily dependent on the availability and cost of
information and it requires expertise in the problem area.
Predicting the outcome of each alternative
To evaluate and compare alternatives, it is necessary to predict the future
outcome of each proposed alternative.
It is customary to classify this knowledge into 3 categories ranging from
complete knowledge to ignorance.
Specifically, these categories are...
1. Certainty
2. Risk
3. Uncertainty
Decision making under certainty
In decision making under certainty, it is assumed that complete knowledge
is available so that the decision maker knows exactly what the outcome of
each course of action will be (as in a deterministic environment).
The decision maker is viewed as a perfect predictor of the future.
Decision making under Risk (risk analysis)
A decision made under risk (also known as a probabilistic or stochastic
decision situation) is one in which the decision maker must consider
several possible outcomes for each alternative, each with a given
probability of occurrence.
Under these assumptions, the decision maker can assess the degree of
risk associated with each alternative.
Decision making under uncertainty
In decision making under uncertainty, the decision maker considers
situations in which several outcomes are possible for each course of
action.
In contrast to the risk situation, the decision maker does not know, or
cannot estimate the probability of occurrence of the possible outcomes.
Measuring outcomes
The value of an alternative is judged in terms of goal attainment;
sometimes an outcome is expressed directly in terms of goal.
Example_ Profit is an outcome, whereas profit maximization is goal and
both are expressed in terms of rupees.
Scenarios
A scenario is a statement of assumptions about the operating environment
of a particular system at a given time.
A scenario is a narrative description of the setting in which the decision
situation to be examined.
A scenario describes the decision & uncontrollable variables and
parameters for a specific modeling situation. It also may provide the
procedures & constraints of the modeling itself.
A scenario is especially helpful in simulation & in what if analysis.
Following are the examples for every decision situation.
o The worst possible scenario.
o The best possible scenario.
o The most likely scenario.
o The scenario determines the context of the analysis to be performed.
Scenario plays an important is DSS because they…
o Help to identify potential opportunities & problem areas.
o Provide flexibility in planning.
o Identify the leading edges of changes that management should
monitor
o Help validate major assumptions used in the modeling
o Help to check the sensitivity of the proposed solutions to change in the
scenarios.
o The best possible scenario.
The Choice Phase
Search Approach
1. Analytical Techniques 2. Blind & Heuristic search approaches
Algorithms A) Blind Search
I) Complete enumeration
ii) Incomplete, partial search
B) Heuristic Search
The choice phase includes search, evaluation, and recommending an
appropriate solution to the model.
A solution to a model is a specific set of values of the decision variables in
a selected alternative.
Only if this recommended solution is successfully implemented is the
problem considered to be solved.
Search approaches
The choice phase involves a search for an appropriate course of action
that will solve the problem.
For normative models, either an analytical approach can be used, or a
complete exhaustive enumeration is applied.
For descriptive models, a comparison of a limited number of alternatives is
used either blindly or by using heuristics.
A principle of choice is a decision regarding the acceptability of a solution
approach.
Normative Models
Normative implies that the chosen alternative is demonstrably the best of
all possible alternatives.
To find it, one should examine all alternatives and prove that the one
selected is indeed the best.
o Get the highest level of goal achievement from a given set of
resources.
o Find the alternative with the highest ratio of goal attainment to cost.
o Find the alternative with lowest cost that will fulfill required level of
goals.
Normative decision theory is based on the following assumptions related
to rational decision makers.
o Humans are economic beings whose objective is to maximize the
attainment of goals.
o In a given decision situation, all variables alternative courses of
action and heir consequences are known.
o Decision maker have an order or preferences that enables them to
rank the desirability of all consequences of the analysis.
Sub Optimization
If a sub optimal decision is made in one part of the organization without
consideration of the rest of the organization, then an optimal solution from
the point of view of that part may be inferior for the whole.
Once a solution is proposed, its potential effects on the remaining
departments of the organization can be checked.
Descriptive Models
Descriptive models describe things as they are or as they are believed to
be.
Information flow Environmental impact analysis
Scenario analysis Simulation (Different types)
Financial planning Technological forecasting
Complex inventory decisions Waiting line (queuing) management
Markov analysis (Predictions)
Good enough or satisfying
Organization or individual involves a willingness to settle for a satisfactory
solution, "something less than the best".
In a satisfying mode, the decision maker sets up an aspiration, goal, or
desired level of performance and then searches the alternatives until one
is found that achieves this level.
Developing (generating) alternatives
This can be a lengthy process that involves search and creativity.
Generating alternatives is heavily dependent on the availability and cost of
information and it requires expertise in the problem area.
Predicting the outcome of each alternative
To evaluate and compare alternatives, it is necessary to predict the future
outcome of each proposed alternative.
It is customary to classify this knowledge into 3 categories ranging from
complete knowledge to ignorance.
Specifically, these categories are...
1. Certainty
2. Risk
3. Uncertainty
Decision making under certainty
In decision making under certainty, it is assumed that complete knowledge
is available so that the decision maker knows exactly what the outcome of
each course of action will be (as in a deterministic environment).
The decision maker is viewed as a perfect predictor of the future.
Decision making under Risk (risk analysis)
A decision made under risk (also known as a probabilistic or stochastic
decision situation) is one in which the decision maker must consider
several possible outcomes for each alternative, each with a given
probability of occurrence.
Under these assumptions, the decision maker can assess the degree of
risk associated with each alternative.
Decision making under uncertainty
In decision making under uncertainty, the decision maker considers
situations in which several outcomes are possible for each course of
action.
In contrast to the risk situation, the decision maker does not know, or
cannot estimate the probability of occurrence of the possible outcomes.
Measuring outcomes
The value of an alternative is judged in terms of goal attainment;
sometimes an outcome is expressed directly in terms of goal.
Example_ Profit is an outcome, whereas profit maximization is goal and
both are expressed in terms of rupees.
Scenarios
A scenario is a statement of assumptions about the operating environment
of a particular system at a given time.
A scenario is a narrative description of the setting in which the decision
situation to be examined.
A scenario describes the decision & uncontrollable variables and
parameters for a specific modeling situation. It also may provide the
procedures & constraints of the modeling itself.
A scenario is especially helpful in simulation & in what if analysis.
Following are the examples for every decision situation.
o The worst possible scenario.
o The best possible scenario.
o The most likely scenario.
o The scenario determines the context of the analysis to be performed.
Scenario plays an important is DSS because they…
o Help to identify potential opportunities & problem areas.
o Provide flexibility in planning.
o Identify the leading edges of changes that management should
monitor
o Help validate major assumptions used in the modeling
o Help to check the sensitivity of the proposed solutions to change in the
scenarios.
o The best possible scenario.
The Choice Phase
Search Approach
1. Analytical Techniques 2. Blind & Heuristic search approaches
Algorithms A) Blind Search
I) Complete enumeration
ii) Incomplete, partial search
B) Heuristic Search
The choice phase includes search, evaluation, and recommending an
appropriate solution to the model.
A solution to a model is a specific set of values of the decision variables in
a selected alternative.
Only if this recommended solution is successfully implemented is the
problem considered to be solved.
Search approaches
The choice phase involves a search for an appropriate course of action
that will solve the problem.
For normative models, either an analytical approach can be used, or a
complete exhaustive enumeration is applied.
For descriptive models, a comparison of a limited number of alternatives is
used either blindly or by using heuristics.
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