Decisions I

1. Decisions I Risk and Decision Strategies

2. 1. Principles of effective decision-making: • How do individual incentives relate to organization goals? • When is it worthwhile to get more information? • How much weight should you put on future options? • How can you combine subjective evaluations to make objective group decisions?

3. 2. Interacting with others: • Game theory--Are people doomed by nature to cheat on each other? • Management styles and group decision processes. • How can you use this to work more effectively with your boss?

4. 3. The real world • How is perception affected by environment and the way information is presented? • How can we change the environment to help people make better decisions? • How can we use these models to better understand ourselves and improve "the way things work around here?"

5. 4. Business Decisions & Scientific Conclusions • Business • “Be decisive!” • “Make Rapid decisions.” • “Better to make any decision than no decision.” • Versus Science: • “Get more data.” • “Do one more experiment.” • --------> The Paralysis of Analysis • ----------- > Could there be a Science of Business Decisions?

6. Five Laws of Decision Making • This came up on my computer screen from one of those quote-of-the-day computer programs about 10 years ago • Managers Make Decisions. • Any Decision is better than no decision. • A decision is judged by the conviction with which it is uttered. • Technical analyses have no value above the mid-management level. • Decisions are justified by the benefit to the organization. • Decisions aremade by considering the benefits to the decision makers. • The one law of behavioral psychology:

7. You get what you reward • People in organizations make decisions in their own interests. • No level of controls will substitute for designing a system that gives people incentives to make the right decisions. • Threats and fear will only make it more difficult for people to make good decisions in the interests of the organization. • Rules and controls are not foolproof • Fools are too Ingenious

8. German Officer Personnel Records

9. Reasons for Poor Productivity-- • Low Output • High Input • Irrelevant Effort • Poorly Understood Goals • Sources of Human Progress- • Intelligence: • Discern the Purpose • Laziness: • Find the most efficient way to fulfill the purpose. • Avoid confusing motion with direction. • Rational Laziness is a Virtue!

10. Heirarchy of Criteria-- • The Best Alternative Is: • Ethical • Effective • Efficient • (and puts the beans in the right pots)

13. The Classical Textbook Decision Process • 1. Identify the problem • 2. Specify objectives and decision criteria • 3. Identify alternatives • 4. Analyze and compare alternatives • 5. Select the best alternative • 6. Implement • 7. Monitor results • Step 8: admit and correct mistakes • step zero: • determine that there really is a problem

14. Logistics & Operations ManagementDecision Models • “Mathematicians are like Frenchmen-you give them something and they put it in their own language, and thereafter it is understood by no one.” Goethe • Using sophisticated mathematical tools, Operations Research takes mundane problems and makes them incomprehensible, producing elegant solutions that nobody understands. • The challenge is to show the utility of logistics models so that people will appreciate the usefulness and adopt the solutions for a better, more productive world.

15. The First Complication--There are Different Decision Environments-- • Certainty--the outcome of several courses of action are known for sure. We need only pick the best alternative. • Risk--We can calculate the outcomes, but we only know probabilities they will occur • Uncertainty--We can calculate outcomes, but have no idea about relative probabilities. This is also called G.O.K. God Only Knows

16. In Considering Risk, It is important to determine whose risk • Mark Twain commented, “We can endure any amount of another person’s troubles.” • The risk that matters is the perceived risk of the decision maker. • First, let’s consider decision strategies when probabilities are unknown . . .

17. Decision Strategies under Uncertainty Not knowing the probability of success for specific products, should we build small, medium or large? Depends on what you expect from each strategy. The optimist assumes the best would happen in each case, the pessimist expects the worst. Our marketing folks and engineers came up with these projections for NPV for each combination of plant size and demands. Fill in the numbers, then click the check button. optimist Pessimist Statistician What does the optimist see as the most likely result from each strategy? How about the pessimist? And the statistician? If each wants the best outcome from what they see as most likely, which would they choose,S, M, or L? Fill in the results for each strategy, then click the corresponding check button to see if the responses are correct.

18. Is this a surprising result?With this set of numbers-- • The optimist (Maximax strategy) would build a large plant • The pessimist (Maximin strategy) would build a small plant • The statistician (LaPlace strategy) would build a medium size plant But is this always what these strategies would give as results? Consider another feasible situation: suppose having a small plant with a high demand market puts you at a disadvantage because competitors are enticed into the market, and you suffer low credibility as a supplier. Thus you have the ironic result of a lower return because demand is higher. There could be a similar result for a medium size plant. Perhaps even with low demand, a large plant could give economies.

19. Decision Strategies under UncertaintyA different set of numbers gives a different result Not knowing the probability of success for specific products, should we build small, medium or large? Depends on what you expect from each strategy. The optimist assumes the best would happen in each case, the pessimist expects the worst. Our marketing folks and engineers came up with these projections for NPV for each combination of plant size and demands. optimist Pessimist What does the optimist see as the most likely result from each strategy? How about the pessimist? And the statistician? If each wants the best outcome from what they see as most likely, which would they choose,S, M, or L? Fill in the results for each strategy, then click the corresponding check button to see if the responses are correct.

20. What a surprising result!With this set of numbers- • The optimist would build a large plant • The pessimist would also build a large plant! Pessimists and optimists both want the best result available. They differ in their views of what is available. Is it better to be an optimist or a pessimist?

22. Cognitive Psychology & Learned OptimismMartin Seligman, 1990 • People can choose to be optimistic • One measure of optimism is explanatory style: • When something good happens: • Permanent, Pervasive, Personal • When something bad happens: • Temporary, Only one aspect of life, External cause • It’s not what happens to you, it’s what you do with it. • Optimism is good or bad depending on the situation.

23. Situation: 1.Working hard at this research may give a breakthrough. 2.People at the party will be interesting & friendly. 3.The concert will be fun. 4.I will continue to live a few more years. 5.The chemical reaction won't be violent. 6.The airplane may have enough fuel. 7.The prisoners will not get violent. 8.The river would never flood. 9.I might win at the gambling boats. 10.I might win the Reader's Digest sweepstakes. Optimist . . . Pessimist Attitude affects outcome. In each situation, Is it better to be an optimist or a pessimist? Is there a pattern to your responses? Does it relate to severity of the risk?

24. Most People are not “Optimists” or “Pessimists”Rather, they make decisions to avoid perceived Personal Risk • The Rule of Thumb is “Never Risk more than you can afford to lose” Thus, real-life decision makers only want to know three things: 1. What’s the probability this will turn out okay? 2. If it goes bad, how bad could it get? 3. What can I do to affect the outcome?

25. Real life decision makers consider the potential for blame and punishment (Fear is a great motivator.) • This Strategy is called “Minimax Regret” because the selection is made by considering the worst thing that could happen in each alternative and avoiding those that have the worst possible consequences • For each possible state in the future consider how much criticism you could get from your boss. This is called “regret”. • Consider the worst thing that could happen for each alternative. • Avoid alternatives that have large potential regrets • Pick the alternative with the lowest worst regret. For example, let’s look at another case, and look at potential for regrets (blame) for each state of demand, depending on which size plant we built.

26. Decision Strategies under Uncertainty—Minimax Regret Not knowing the probability of success for specific products, should we build small, medium or large? For each possible future demand state, fill in how much criticism you would get for having built each size plant. For example, If in the future demand is low, a small plant would give the best possible return, so there would be 0 regret. On the other hand, if you had a medium-size plant, your boss might criticize you for only making $7 M instead of $10 M. The regret would be $3M. After filling in the 3 values for each demand, click the button to check your calculations for each alternative, then click the button for the one you would choose based on this approach. Max Regret select Regrets

27. Minimax Regret is the way that most people make decisions in organizations--Is this the best way? • Picture an army trying to charge while everybody tries to avoid being in the front lines. • MiniMax Regret is based on avoiding blame and has a common name. • CYA. • If this isn’t the best way to make decisions, how can we tell what it costs compared to the best option? • In order to assess this, we need some estimate of the average, or expected result of the minimax regret strategy compared to the other things that might be pursued. • To get an estimate of average outcomes, we need to add some estimate of the probabilities of the different states of demand. • The next slide shows calculation of expected values after adding probability estimates.

28. Decision Strategies under Risk--Calculation of expected Monetary Values for the Small Medium and large strategies Marketing people reviewed history and estimated Probabilities of the levels of demand for this type of product. We can’t tell for each individual case what will happen, but knowing probabilities allows us to project an average or expected return for a consistent strategy of building small, medium, or large size plants. The expected return is a weighted average of the returns under different demands or “states of nature” . This weighted average is called an “Expected Monetary Value” or EMV, and the goal in this case is to pick the strategy with the Maximum EMV EMV Max EMV Suppose we knew ahead of time what the demand would be in a specific case? Could we do better than consistently building medium?

29. Max EMV gives the best long run average result and reduces fear of criticism • Max EMV gives a process for arriving at a strategy. • This depersonalizes the process, and reduces fear of blame in case it doesn’t work out this time. • To use this process, the participants must accept that there will be good and “less good” outcomes in specific instances, but by being consistent, we can obtain the best-bet best long run average result. • What if we had information on each specific case so that we could tell in advance which size plant would be best? Then instead of taking the best bet, we could operate with certainty and build the optimum size plant every time. What would this Perfect information be worth?

30. Decision Strategies under Risk--Calculation of expected Monetary Value When we have perfect information ahead of time Max EMV, when we have risk, or only know overall probabilities gives the best result for the large size plant in this case. Having perfect information ahead of time for each individual decision would allow us to know with certainty what the best size plant would be. This allows us to pursue a specific strategy rather than a consistent general strategy. If We knew ahead of time that demand would be low, what size plant would we build and how much would we make? How about if we knew, this time, time would be moderate? What would we do in high demand? Expected Value of Perfect Information (EVPI) is the difference between EMV with the information(certainty) and EMV with only probabilities (risk) EVPI =