DECISION MAKING

# DECISION MAKING

## DECISION MAKING

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##### Presentation Transcript

1. DECISION MAKING - Decision Making and problem solving are used in all management functions, although usually they are considered a part of the planning phase.

2. Relation to Planning • Decision Making: Process of making a conscious choice between 2 or more alternatives producing most desirable consequences (benefits) relative to unwanted consequences (costs). • Decision Making is essential part of Planning. • Planning: Deciding in advance what to do, how to do it, when to do it and who is to do it.

3. Categories of Decision Making • Decision Making Under Certainty: Linear Programming • Decision Making Under Risk: expected value, decision trees, queuing theory, and simulation • Decision Making Under Uncertainty: Game Theory

4. State of Nature / Probability N1 N2 ……… Nj ……… Nn Alternative P1 P2 ………Pj ……… Pn A1 O11 O12 ……… O1j ……… O1n A2 O21 O22 ……… O2j ………O2n Outcome …. …. … ………… ……… Ai Oi1 Oi2 ……… Oij ………Oin …. …. … ………… ……… Am Om1 Om2 ……… Omj …… Omn Sum of n values of pjmust be 1 Payoff Table (Decision Matrix)

5. Decision Making Under Certainty Implies that we are certain of the future state of nature (or assume we are) This means:- the probability of pj of future Nj is 1 and all other futures have zero probability.

6. Decision Making Under Risk This means:- Each Nj has a known (or assumed) probability of pj and there may not be one state that results best outcome.

7. Decision Making Under Uncertainty This means:- Probabilities pj of future states are unknown.

8. n  j=1 (pjOij) Ei= Decision Making Under Risk - Calculate Expected Values (Ei) - Choose the Alternative Ai giving the highest expected value

9. Example of Decision Making Under Risk Not Fire in your house Fire in your house State of Nature Alternatives Probabilities P1 =0.999 P2=0.001 n  j=1 Insure house \$-200 \$-200 (pjOij) Do not Insure house 0 \$-100,000 Ei= Would you insure your house or not? E1=\$-200 E1=0.999*(-200)+0.001*(-200) E2=\$-100 E2=0.999*0+0.001*(-100,000)

10. Decision Trees Decision node Ai Chance node Nj Outcome (Oij) Probability (Pj) Expected Value Ei x = No Fire: (-200) x (0.999) (-199.8) = + = \$-200 Insure (-200) x (0.001) (-0.2) = Fire: (0) x (0.999) (0) No Fire: = Don’t Insure + =\$-100 (-100,000) x (0.001) (-100) Fire: = Mathematical solution is identical, visual representation is different

11. Well Drilling Example-Decision Making Under Risk State of Nature / Probability Alternative N1:Dry Hole N2 :Small Well N3:Big Well Expected Value P1=0.6 P2=0.3 P3=0.1 \$0 A1:Don’t Drill\$0 \$0 \$0 A2:Drill Alone\$-500,000 \$300,000 \$9,300,000 \$720,000 \$162,000 A3:Farm Out\$0 \$125,000 \$1,250,000 E1=0.6*0+0.3*0+ 0.1*0 E2=0.6*(-500,000)+0.3*(300,000)+ 0.1*(9,300,000) E3=0.6*0+0.3*(125,000)+ 0.1*(1,250,000) \$720,000 A2 is the solution if you are willing to risk \$500,000

12. Decision Making Under Uncertainty We do not know the probabilities pj of future states of nature Nj

13. State of Nature / Probability Alternative N1:Dry Hole N2 :Small Well N3:Big Well A1:Don’t Drill\$0 \$0 \$0 A2:Drill Alone\$-500,000 \$300,000 \$9,300,000 A3:Farm Out\$0 \$125,000 \$1,250,000 Decision Making Under Uncertainty