Value of Information in DM

8/2/2019 Value of Information in DM

1/29

Decision Making Decision-making is based on information

Information is used to: 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


2/29

Value of Information The value of information used in

decision making is: (value of the outcome with the

Information) (value of the outcomewithout the Information)


3/29

Types of Decision H. A. Simon classified decisions into

Programmed decisions

Non-Programmed decisions Classified according to the extent to

which decision making can be pre-

planned These are the extremes of a continuous

range of decision types


4/29

Programmed DecisionsAlso known as Structured Decisions Characteristics

Repetitive, routine, known rules orprocedures, often automated, can bedelegated to low levels in the organisation,often involve things rather than people

Examples - Inventory control decisions,machine loading decisions, scheduling.


5/29

Non-Programmed Decisions Also known as Unstructured Decisions Characteristics

Novel, non-routine, rules not known, high degreeof uncertainty, cannot be delegated to low levels,more likely to involve people.

Examples - Acquisitions, mergers, launching newproducts, personnel appointments.

Semi-Structured Decisions The most common type of decision May be partially automated


6/29

EmpowermentAuthority 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


7/29

Empowerment Decisions should be made:

At the lowest possible level, which accords

with their natureAs close to the scene of the action as

possible

at the level that ensures none of theactivities and objectives are forgotten


8/29

Empowerment Enabled by systems such as

Customer Relationship Management (CRM) Gives call centre staff specialist knowledge

about any customer

Expert Systems

Assists non-experts in making complex decisions


9/29

Uncertainty Uncertainty arises from incomplete

information due to:

Incomplete forecasting models Conflicting data from external sources Lack of time Internal data on particular problem not collated

The uncertainty of an outcome is expressedas a probability


10/29


11/29

Rational vs. Real Decisions Users tend to explain their actions in terms of

rational behaviour, whereas their actualperformance may be governed by intuitionrather than by rational analysis. Studies ofmanagers at work have shown that there is adiscrepancy between how managers claim totake decisions and their actual observeddecision-making behaviour.

Argyris and Schon


12/29

Payoff Matrices The standard way to analyse simple decision

problems

These are constructed as follows: 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


13/29

Example A company must decide on one of three

development projects, A, B or C They have identified three possible

events relating to market conditions thatwill effect this decision

Event Probability

Boom 60%Steady State 30%

Recession 10%


14/29


15/29

Decision Criteria In order to evaluate the alternatives,

managers use a number of different criteria:

Equally Likely The consequences of each decision are summed

and the result divided by the number of events Useful if probabilities are not known

Maximax Determine the highest possible profit from each

strategy and choose that with the highest overallprofit - Usually high risk, but high gain


16/29

ExampleDecision

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? Equally Likely Maximax


17/29

Decision Criteria Minimax

Choose that action with the smallest maximum

possible loss, or the largest minimum profit. Low risk, low gain.

Maximum Likelihood 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.


18/29

ExampleDecision


Boom 60% +8M -2M +16M



Preferred Project is? Minimax Max Likelihood


19/29

ExampleDecision


Boom 60% +8M -2M +16M




20/29

Decision Criteria Expected Value

A weighted average of all outcomes

The weights are probabilities

Gives the average value of the decision if itwere made repeatedly Uses all the information concerning events

and their likelihood

( ){ }=

=N

i

ii payoffoutcomePEV1


21/29

ExampleDecision


Boom 60% +8M -2M +16M



Calculate EV for each option/choice 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


22/29

Example 2A lt e rna t ive AA lte rna t ive BA lt e rn a t ive C

O u tc o m e :P ro by

P ro f i t P roby

P ro f i t P r oby

Pro f i tO p t im is t ic 0 . 2 5 0 0 0 0 .3 4 0 0 0 0 .1 3 0 0 0

M o s t L ik e ly0 . 6 7 5 0 0 0 .5 7 0 0 0 0 . 7 6 5 0 0

P e s s im is t ic0 . 2 9 0 0 0 0 .3 9 5 0 0 0 . 2 1 0 0 0 0


23/29

Decision Criteria Expected Value

Uses all the information concerning events

and their likelihood Does not take into account decision-makers

attitude to risk

Does not reflect the actual outcomes in thefigures Can the company afford to lose 26M?


24/29

Decision Trees Not all decisions will be taken in

isolation

A decision will have an effect of futureevents and outcomes

An outcome in turn may effect future

decision making


25/29

Decision Trees Decision trees provide a means of

structuring the decision making process

to allow for alternative futures


26/29

Decision Tree Two types of Node

Decision Node Represent decision points Decision are made by the organisation

Outcome Node Linked to possible outcomes These are uncontrollable


27/29

Example

Project A

Project B

Project C

Boom 60%

Steady 30%

Recession 10%

Boom 60%

Boom 60%

Steady 30%

Steady 30%

Recession 10%

Recession 10%

8M

1M

-10M

-2M

+6M

+12M

+16M

0

-26M


28/29

Example

Project A

Project B

Project C

4.1

1.8

7

Boom 60%

Steady 30%

Recession 10%

Boom 60%

Boom 60%

Steady 30%

Steady 30%

Recession 10%

Recession 10%

8M

1M

-10M

-2M

+6M

+12M

+16M

0

-26M


29/29

Example

4.1

Project A

Project B

Project C

4.1

1.8

7

Boom 60%

Steady 30%

Recession 10%

Boom 60%

Boom 60%

Steady 30%

Steady 30%

Recession 10%

Recession 10%

8M

1M

-10M

-2M

+6M

+12M

+16M

0

-26M

Documents

Value of Information in DM