Mumbai_Mirror Decison Making

7/31/2019 Mumbai_Mirror Decison Making

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Group NameNAMES ROLL NOS.

1. Avi Arora 11

2. Karuna Londhe 24

3. Omkar Sawant 35

4. Priyanka Dethe 41

5. Theresmona Mathew 66

6. Radhika Naik 73

7.Vikas Balmiki 68



Decision Making

Decision theory is the study of making decisions that have asignificant impact.

Problem Statement : To study effect of adding an extra

supplement with Mumbai Mirror on Readership and Sales.



Type 1: Decision Making under Certainty. Decision maker know for sure (that is, with

certainty) outcome or consequence of every decision alternative.

Type 2: Decision Making under Uncertainty. Decision maker has no information at all

about various outcomes or states of nature.

Type 3: Decision Making under Risk.

Decision maker has some knowledge regarding probability of occurrence of each

outcome or state of nature.



Decision Making Under Uncertainty

• If the decision maker does not know with certainty which state of nature will

occur, then he/she is said to be making decision under uncertainty.

The five commonly used criteria for decision making under uncertainty are:

1. the optimistic approach (Maximax)

2. the conservative approach (Maximin)

3. the minimax regret approach (Minimax regret)

4. Equally likely (Laplace criterion)

5. Criterion of realism with (Hurwicz criterion)



Consider the following problem with two decision alternatives (d1 & d2) and twostates of nature S1 and S2

States of Nature

S1 S2D1 27 23

Decisions

D2 18 32

S1- @ Free

S2- @ 50p/-

D1-MM with extra supplement on careers & hangout placesD2-MM with extra supplement for kids



Optimistic Approach

• The optimistic approach would be used by an optimistic decision maker.

•

The decision with the largest possible payoff is chosen.

• If the payoff table was in terms of costs, the decision with the lowest cost would

be chosen.



Conservative Approach

• The conservative approach would be used by a conservative decision maker.

• For each decision the minimum payoff is listed and then the decision corresponding

to the maximum of these minimum payoffs is selected. (Hence, the minimum

possible payoff is maximized.)

• If the payoff was in terms of costs, the maximum costs would be determined for each

decision and then the decision corresponding to the minimum of these maximum

costs is selected. (Hence, the maximum possible cost is minimized.)



Maxi-Min

States of Nature

S1 S2

D1 27 23Decisions

D2 18 32

Minimum 18 23

From the above approach the decision maker would choose to go with

Supplying Mumbai Mirror with additional Supplement



Minimax Regret Approach

• The minimax regret approach requires the construction of a regret table or an

opportunity loss table.

• This is done by calculating for each state of nature the difference between each

payoff and the largest payoff for that state of nature.

• Then, using this regret table, the maximum regret for each possible decision is

listed.

• The decision chosen is the one corresponding to the minimum of the maximum

regrets.



MINI-MAX

States of Nature

S1 S2

D1 0 4

DecisionsD2 14 0

Minimum 14 4





Hurwicz Criterian

• The Hurwicz alpha is a criterion for decision making under complete uncertainty that

represents a comprimise between the Maximin and Maximax criteria.

• The alpha is a number between 0 and 1.

• In the special case where it is one, the criterion reduces to Maximin and in the specialcase where it is zero the criterion reduces to Maximax.

• The decision maker can set alpha to a number between zero and one according to his

or her level of optimism.



Hurwicz Criterion

States of Nature

S1 S2

D1 27 23Decisions

D2 18 32

Maximum 18 23

Minimum 27 32

S1=(0.2*27)+(0.8*18)=19.8 S2=(0.2*32)+(0.8*23)=24.8


Supplying Mumbai Mirror with additional Cost



States of Nature

S1 S2

D1 27 23

Decisions

D2 18 32

E (S1) = ½ * 27 + ½ * 18 = 22.5

E (S2) = ½ * 23 + ½ * 32 = 27.5



Laplace Criterian

In this case the decision maker thinks that all the situation are equally

probable.



0 50 100 150 200 250 300

Times Of India

Mumbai Mirror

Mid-Day

DNA

Indian Express

Hindustan Times

280

119

142

218

190

125

Turnovers In Crores (Approx.)

In Rs. Crores

Impact on Sales and Readership



0

10

20

30

40

50 46

127 8

16

38

Readership Data

Readership in %



Conclusion

Through the above different approaches the decision maker is able to decide

whether adding an extra supplement would increase the readership andhence the sales.

Mumbai_Mirror Decison Making

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