Decision Making
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Decision-making is based on information
Information is used to:
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Identify the fact that there is a problem in the first
place
Define and structure the problem
Explore and choose between different possible
solutions
Evaluate the effectiveness of the decision
Value of Information

The value of information used in
decision making is:
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(value of the outcome with the
Information) – (value of the outcome
without the Information)
Types of Decision
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H. A. Simon classified decisions into
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Programmed decisions
Non-Programmed decisions
Classified according to the extent to
which decision making can be preplanned
These are the extremes of a continuous
range of decision types
Programmed Decisions
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Also known as Structured Decisions
Characteristics
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Repetitive, routine, known rules or
procedures, often automated, can be
delegated to low levels in the organisation,
often involve things rather than people
Examples - Inventory control decisions,
machine loading decisions, scheduling.
Non-Programmed Decisions
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Also known as Unstructured Decisions
Characteristics
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Novel, non-routine, rules not known, high degree
of uncertainty, cannot be delegated to low levels,
more likely to involve people.
Examples - Acquisitions, mergers, launching new
products, personnel appointments.
Semi-Structured Decisions
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The most common type of decision
May be partially automated
Empowerment
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Authority to take decisions is being
delegated down the line especially in
modern service industries
This process is called empowerment
and should enable an organisation to
take a variety of decisions more quickly,
thus providing a more flexible service
Empowerment
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Decisions should be made:
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At the lowest possible level, which accords
with their nature
As close to the scene of the action as
possible
at the level that ensures none of the
activities and objectives are forgotten
Empowerment
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Enabled by systems such as
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Customer Relationship Management (CRM)
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Gives call centre staff specialist knowledge
about any customer
Expert Systems
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Assists non-experts in making complex
decisions
Uncertainty
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Uncertainty arises from incomplete
information due to:
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Incomplete forecasting models
Conflicting data from external sources
Lack of time
Internal data on particular problem not collated
The uncertainty of an outcome is expressed
as a probability
Rational Decision Making
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The rational model of decision making is
a mechanistic approach to decision
making
It assumes perfect knowledge of all
factors surrounding the decision
Rational vs. Real Decisions
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‘Users tend to explain their actions in terms
of rational behaviour, whereas their actual
performance may be governed by intuition
rather than by rational analysis. Studies of
managers at work have shown that there is a
discrepancy between how managers claim to
take decisions and their actual observed
decision-making behaviour’.
Argyris and Schon
Payoff Matrices
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The standard way to analyse simple decision
problems
These are constructed as follows:
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Identify all available options
Identify events which cause an outcome (states of
nature)
Estimate the likelihood of each state of nature
Estimate the value/payoff of each outcome
Determine the expected value for each option
Choose the option with the highest expected value
Example

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A company must decide on one of
three development projects, A, B or C
They have identified three possible
events relating to market conditions
that will effect this decision
Event
Probability
Boom
60%
Steady State
30%
Recession
10%
Example

The profit and loss figures (potential payoff)
for the three products under the possible
market conditions have been forecast as:
Decision
Event
Project A
Project B
Project C
Boom 60%
+8M
-2M
+16M
Steady State 30%
+1M
+6M
0
Recession 10%
-10M
+12M
-26M
Which one of the above projects should
the company run?
Decision Criteria
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In order to evaluate the alternatives,
managers use a number of different criteria:
Equally Likely
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The consequences of each decision are summed
and the result divided by the number of events
Useful if probabilities are not known
Maximax
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Determine the highest possible profit from each
strategy and choose that with the highest overall
profit - Usually high risk, but high gain
Example
Decision
Event
Project A
Project B
Project C
Boom 60%
+8M
-2M
+16M
Steady State 30%
+1M
+6M
0
Recession 10%
-10M
+12M
-26M

Preferred Project is?
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Equally Likely
Maximax
Decision Criteria
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Minimax
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Choose that action with the smallest maximum
possible loss, or the largest minimum profit.
Low risk, low gain.
Maximum Likelihood
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Choose the most likely event and then choose the
best strategy for that event.
Low risk, low gain. Does not make full use of
available information.
Example
Decision
Event
Project A
Project B
Project C
Boom 60%
+8M
-2M
+16M
Steady State 30%
+1M
+6M
0
Recession 10%
-10M
+12M
-26M
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Preferred Project is?
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Minimax
Max Likelihood
Example
Decision
Event
Project A
Project B
Project C
Boom 60%
+8M
-2M
+16M
Steady State 30%
+1M
+6M
0
Recession 10%
-10M
+12M
-26M
Decision Criteria
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Expected Value
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A weighted average of all outcomes
The weights are probabilities
N
EV   Poutcomei  payoffi 
i 1
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Gives the average value of the decision if it
were made repeatedly
Uses all the information concerning events
and their likelihood
Example
Decision
Event
Project A
Project B
Project C
Boom 60%
+8M
-2M
+16M
Steady State 30%
+1M
+6M
0
Recession 10%
-10M
+12M
-26M
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Calculate EV for each option/choice
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Project A (8M*0.6)+(1M*0.3)+(-10M*0.1) = 4.1M
Project B (-2*0.6)+(6*0.3)+(12*0.1) = 1.8
Project C (16*0.6)+(0*0.3)+(-26*0.1) = 7.0
Preferred Project is? C
Example 2
Alternative A Alternative B Alternative C
Outcome: Proby Profit Proby Profit Proby Profit
Optimistic
0.2
5000
0.3
4000
0.1
3000
Most Likely
0.6
7500
0.5
7000
0.7
6500
Pessimistic
0.2
9000
0.3
9500
0.2 10000
Decision Criteria
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Expected Value
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Uses all the information concerning events
and their likelihood
Does not take into account decisionmakers attitude to risk
Does not reflect the actual outcomes in the
figures
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Can the company afford to lose 26M?
Decision Trees
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Not all decisions will be taken in
isolation
A decision will have an effect of future
events and outcomes
An outcome in turn may effect future
decision making
Decision Trees
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Decision trees provide a means of
structuring the decision making process
to allow for alternative futures
Decision Tree
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Two types of Node
Decision Node
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Represent decision points
Decision are made by the organisation
Outcome Node
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Linked to possible outcomes
These are uncontrollable
Example
Boom 60%
8M
Steady 30%
Recession 10%
Project A
Boom 60%
Project B
Steady 30%
Recession 10%
Project C
Boom 60%
Steady 30%
1M
-10M
-2M
+6M
+12M
+16M
0
Recession 10%
-26M
Example
Boom 60%
4.1
Steady 30%
Recession 10%
Project A
Boom 60%
Project B
1.8
Steady 30%
Recession 10%
Boom 60%
Project C
7
8M
Steady 30%
1M
-10M
-2M
+6M
+12M
+16M
0
Recession 10%
-26M
Example
Boom 60%
4.1
Steady 30%
Recession 10%
Project A
Boom 60%
4.1
Project B
1.8
Steady 30%
Recession 10%
Boom 60%
Project C
7
8M
Steady 30%
1M
-10M
-2M
+6M
+12M
+16M
0
Recession 10%
-26M