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# Making Decisions Presentation

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### Making Decisions Presentation

1. 1. Chapter 18 “Making Decisions”By : Anant Pednekar<br />Class : Quantitative Methods for Business<br />Instructor : Fred Dalili<br />Date : 30th June 2009<br />1<br />
2. 2. Chapter 18: Making Decisions<br />Topics:<br />Decision Making with Certainty<br />Decision Making with Uncertainty<br />Decision Making with Risk<br />Sequential Decisions<br />2<br />
3. 3. Decision Making with Certainty<br />One Event<br />Compare Outcomes<br />Best Decision<br />3<br />
4. 4. Decision Making with Uncertainty<br />Several events, cannot assign probabilities<br />Decision Criteria<br />Laplace Decision Criteria<br />Wald Decision Criteria<br />Savage Decision Criteria<br />4<br />
5. 5. Decision Making with Uncertainty (contd.)<br />Laplace Decision Criteria<br />All events are equally likely<br />Find mean value of the outcomes<br />Best average outcome<br />5<br />
6. 6. Decision Making with Uncertainty (contd.)<br />Wald Decision Criteria<br />For each alternative find the worst outcome<br />Find the best of these worst outcomes<br />6<br />
7. 7. Decision Making with Uncertainty (contd.)<br />Savage Decision Criteria<br />For each event find the best possible outcome<br />Find the regret for every entry in the column<br />Put the regrets into a ‘regret’ matrix<br />Find the highest regret in each row<br />Find the best (i.e. the lowest) of the regrets<br />7<br />
8. 8. Decision Making with Risk<br />Several events, can assign probabilities<br />Find the Expected Value for each alternative<br /> Expected Value = Ʃ (Probability of Event x Value of Outcome)<br />Find the alternative with the best expected value (that is, the highest value for gains, and the lowest value for costs)<br />8<br />
9. 9. Decision Making with Risk (contd.)<br />Application of Bayes’ Theorem<br />Used for conditional probabilities, when the outcomes of events occurring are dependent on each other<br />P(a/b) = P(b/a) x P(a)<br /> P(b)<br />Utilities – U(x)<br />Expected values do not reflect real life preferences and are linear<br />Invest: 0.1 x 500,000 – 0.9 x 50,000 = \$5,000<br />Do not invest: 0.1 x 0 + 0.9 x 0 = \$0 <br />Expected utilities are non-linear functions<br />Process of choosing the best alternative is the same<br />Utilities give more accurate view of the value of money<br />9<br />
10. 10. Sequential Decision Making<br />Series of related decisions<br />Decision tree<br />A decision tree shows the sequence of alternatives (decisions) and events (uncertainty points) by using branches, squares and circles that extend from left to right (horizontal)<br />The tree is analyzed by assigning probabilities to the events and calculating the expected values at each decision node and then finding the best alternative <br />10<br />Expansion a success<br />30,000<br />P = 0.7<br />1<br />P = 0.3<br />Grant Loan<br />10,000<br />Expansion not a success<br />EV = 24,000<br />3<br />Pat continues banking<br />20,000<br />P = 0.4<br />24,000<br />2<br />Do not grant loan<br />P = 0.6<br />0<br />Max[24,000, 8,000]<br />Pat moves account<br />EV = 8,000<br />
11. 11. Questions ???<br />11<br />Happy Independence Day Weekend !!!<br />