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 1 : Decision Analysis
 2 : Basic Terms Decision Alternatives (eg. Production quantities) States of Nature (eg. Condition of economy) Payoffs (\$ outcome of a choice assuming a state of nature) Criteria (eg. Expected Value)
 3 : What kinds of problems? Alternatives known States of Nature and their probabilities are known. Payoffs computable under different possible scenarios
 4 : Decision Environments Ignorance – Probabilities of the states of nature are unknown, hence assumed equal Risk / Uncertainty – Probabilities of states of nature are known Certainty – It is known with certainty which state of nature will occur. Trivial problem.
 5 : Example – Decisions under Ignorance Assume the following payoffs in \$ thousand for 3 alternatives – building 100, 200, or 400 condos. The payoffs (\$ million) depend on how many are sold, which depends on the economy. Three scenarios are considered - a Poor, Average, or Good economy at the time the condos are completed.
 6 : Maximax - Risk Seeking Behavior What would a risk seeker decide to do? Maximize payoff without regard for risk. In other words, use the MAXIMAX criterion. Find maximum payoff for each alternative, then the maximum of those. The best alternative under this criterion is A3, with a potential payoff of 12.
 7 : Maximin – Risk Averse Behavior What would a risk averse person decide to do? Make the best of the worst case scenarios. In other words, use the MAXIMIN criterion. Find minimum payoff for each alternative, then the maximum of those. The best alternative under this criterion is A1, with a worst case scenario of 3, which is better than other worst cases.
 8 : LaPlace – the Average What would a person somewhere in the middle of the two extremes choose to do? Take an average of the possible payoffs. In other words, use the LaPlace criterion (named after mathematician Pierre LaPlace). Find the average payoff for each alternative, then the maximum of those. The best alternative under this criterion is A2, with an average payoff of 4, which is better than the other two averages.
 9 : Minimax Regret – Lost Opportunity What would a person choose who wanted to minimize the worst mistake possible? For each state of nature, find the maximum payoff, and subtract each of the payoffs from it to compute the lost opportunities (regrets). Then find maximum values for each alternative, and the minimum of those. The best alternative under this criterion is A2, with a maximum regret of 5, which is better than the other two maximum regrets.
 10 : Example – Decisions under Risk Assume now that the probabilities of the states of nature are known, as shown below.
 11 : Expected Values When probabilities are known, compute a weighed average of payoffs, called the Expected Value, for each alternative and choose the maximum value. The best alternative under this criterion is A2, with a maximum EV of 4.00, which is better than the other two EVs.
 12 : Expected Opportunity Loss (EOL) Compute the weighted average of the opportunity losses for each alternative to yield the EOL. The best alternative under this criterion is A2, with a minimum EOL of 1.70, which is better than the other two EOLs. Note that EV + EOL is constant for each alternative! Why?
 13 : EVUPI: EV with Perfect Information If you knew everytime with certainty which state of nature was going to occur, you would choose the best alternative for each state of nature every time. Thus the EV would be the weighted average of the best value for each state. Take the best times the probability, and add them all. 3*0.3 = 0.90 6*0.6 = 3.60 12*0.1 = 1.20 _____________ Sum = 5.70 Thus EVUPI = 5.70
 14 : EVPI: Value of Perfect Information If someone offered you perfect information about which state of nature was going to occur, how much is that information worth to you in this decision context? Since EVUPI is 5.70, and you could have made 4.00 in the long run (best EV without perfect information), the value of this additional information is 5.70 – 4.00 = 1.70. Thus, EVPI = EVUPI – Evmax = EOLmin
 15 : 0.6 3.50 4.00 -1.00 6.00 7.00 -10.00 -2.00 12.00 3.00 0.1 0.3 0.3 0.6 0.1 0.1 0.6 0.3 3.40 4.00 -3.00 A2 4.00 A1 A2 A3 Decision Tree
 16 : Sequential Decisions Would you hire a consultant (or a psychic) to get more info about states of nature? How would additional info cause you to revise your probabilities of states of nature occuring? Draw a new tree depicting the complete problem.
 17 : Consultant’s Track Record
 18 : Probabilities P(F/S1) = 0.2 P(U/S1) = 0.8 P(F/S2) = 0.6 P(U/S2) = 0.4 P(F/S3) = 0.7 P(U/S3) = 0.3 F= Favorable U=Unfavorable
 19 : Joint Probabilities
 20 : Posterior Probabilities P(S1/F) = 0.06/0.49 = 0.122 P(S2/F) = 0.36/0.49 = 0.735 P(S3/F) = 0.07/0.49 = 0.143 P(S1/U) = 0.24/0.51 = 0.47 P(S2/U) = 0.24/0.51 = 0.47 P(S3/U) = 0.03/0.51 = 0.06
 21 : Solution Solve the decision tree using the posterior probabilities just computed.