Decision theory


Published on

Decision Theory/ Decision Tree

Published in: Education
No Downloads
Total views
On SlideShare
From Embeds
Number of Embeds
Embeds 0
No embeds

No notes for slide

Decision theory

  1. 1. Decision Theory/ Decision Tree<br />Operations Research<br />
  2. 2. Introduction to Decision Theory<br />Decision making is an integral part of management planning, organizing, controlling and motivation processes. The decision maker selects one strategy (course of action) over others depending on some criteria, like utility, sales, cost or rate of return.<br />Is used whenever an organization or an individual faces a problem of decision making or dissatisfied with the existing decisions or when alternative selection is specified.<br />
  3. 3. Types of Decisions<br />Strategic Decision<br />Concerned with external environment of the organization.<br />Administrative Decision<br />Concerned with structuring and acquisition of the organization’s resources so as to optimize the performance of the organization.<br />Operating Decision<br />Concerned with day to day operations of the organization such as pricing, production scheduling, inventory levels, etc.<br />
  4. 4. Elements related to all decisions<br />Goals to be achieved: Objectives which the decision maker wants to achieve by his actions<br />The decision maker: Refers to an individual or an organization<br />Courses of action: Also called “Action” or “Decision Alternatives”. They are under the control of decision maker<br />States of nature: Exhaustive list of possible future events. Decision maker has no direct control over the occurrence of particular event.<br />
  5. 5. Elements related to all decisions<br />The preference or value system: Criteria that the decision maker uses in making a choice of the best course of action.<br />Payoff: Effectiveness associated with specified combination of a course of action and state of nature. Also known as profits or conditional values.<br />Opportunity loss table: Incurred due to failure of not adopting most favorable course of action or strategy. Found separately for each state of nature.<br />Payoff table<br />
  6. 6. Types of environment<br />Decision making under certainty<br />Decision making under risk<br />Decision making under uncertainty<br />Decision making under conflict (Game Theory)<br />
  7. 7. Expected Monetary Value (EMV)<br />EMV for the specified course of action is the weighted average payoff ie. The sum of the product of the payoff for the several combination of courses of action and states of nature multiplied by the probability of occurrence of each outcome.<br />
  8. 8. Expected Profit with Perfect Information (EPPI)<br />EPPI is the maximum attainable expected monetary value (EMV) based on perfect information about the state of nature that will occur. <br />The expected profit with perfect information may be defined as the sum of the product of best state of nature corresponding to each optimal course of action and its probability.<br />
  9. 9. Expected Value of Perfect Information (EVPI)<br />EVPI is defined as the maximum amount one would pay to obtain perfect information about the state of nature that would occur. <br />EMV* represents the maximum attainable expected monetary value given only the prior outcome probabilities, with no information as to which state of nature will actually occur.<br />(EVPI) = EPPI – EMV*<br />
  10. 10. Expected Opportunity Loss (EOL)<br />Another useful way of maximizing monetary value is to maximize the expected opportunity loss or expected value of regret.<br />The conditional opportunity loss (COL) for a particular course of action is determined by taking the difference between the payoff value of the most favourable course of action and some other course of action.<br />Thus, opportunity loss can be obtained separately for each course of action by first obtaining the best state of nature for the prescribed course of action and then taking the difference between that best outcome and each outcome for those courses of action.<br />
  11. 11. Eg. Calculating EMV, EPPI, EVPI, EOL<br />An electric manufacturing company has seen its business expanded to the point w here it needs to increase production beyond its existing capacity. It has narrowed the alternatives to two approaches to increase the maximum production capacity.<br />(a) expansion, at a cost of Rs.8 million, or<br />(b) Modernization at a cost of Rs.5 million. Both approaches would require the same amount of time for implementation. Management believes that over the required payback period, demand will either be high or moderate. Since high demand is considered to be somewhat less likely than moderate demand, the probability of high demand has been setup at 0.35. If the demand is high, expansion would gross an estimated additional Rs.12 million but modernization only an additional Rs.6 million, due to lower maximum production capability. On the other hand, If the demand is moderate, the comparable figures would be Rs.7 million for expansion and Rs.5 million for modernization.<br />
  12. 12. Eg. Calculating EMV, EPPI, EVPI, EOL<br />States of Nature: <br />O1 = High Demand<br />O2 = Moderate demand<br />Courses of action:<br />S1 = Expand<br />S2 = Modernize<br />
  13. 13. Eg. Calculating EMV, EPPI, EVPI, EOL<br />Calculation of EMV’s for courses of action S1 and S2 are given below:<br />EMV (S1) = (0.35) X (4) + (0.65) X (-1) = 1.40 – 0.65 = Rs. 0.75 million<br />EMV (S2) = (0.35) X (1) + (0.65) X (0) = Rs. 0.35 million<br />To compute EVPI, we calculate EPPI.<br />EVPI = EPPI – EMV* = 1.40 – 0.75 = Rs. 0.65 million<br />
  14. 14. Eg. Calculating EMV, EPPI, EVPI, EOL<br />Calculation of EOL’s for courses of action S1 and S2 are given below:<br />EOL (S1) = (0.35) X (0) + (0.65) X (1) = Rs. 0.65 million<br />EOL (S2) = (0.35) X (3) + (0.65) X (0) = Rs. 1.05 million<br />Therefore, we select course of action S1 to produce the smallest expected opportunity loss.<br />
  15. 15. Decision making under uncertainty<br />Criterion of Pessimism or Maximin<br />Criterion of Optimism or Maximax<br />Minimax Regret Criterion (Regret payoff)<br />Hurwicz Criterion<br />Laplace Criterion (Equally Likelihood)<br />
  16. 16. Example<br />A food product company is contemplating introduction of a revolutionary new product with new packaging to replace the existing product at much price (S1) or a moderate change in the composition of the existing product with a new packaging at a small increase in price (S2) or a small change in the composition of the existing except the word ‘new’ with a negligible increase in price (S3). The three possible states of nature of events are:<br />(i) High increase in sales (N1)<br />(ii) No change in sales (N2)<br />(iii) Decreases in sales (N3)<br />The marketing department of the company worked out the payoffs in terms of yearly net profit for each course of action for these events (expected sales).<br />
  17. 17. Contd…<br />
  18. 18. Decision Tree<br />A decision tree is a decision support tool that uses a tree-like graph or model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility. It is one way to display an algorithm. Decision trees are commonly used in operations research, specifically in decision analysis, to help identify a strategy most likely to reach a goal. Another use of decision trees is as a descriptive means for calculating conditional probabilities. When the decisions or consequences are modelled by computational verb, then we call the decision tree a computational verb decision tree <br />
  19. 19. Introduction<br />A decision Tree consists of 3 types of nodes:-<br />1. Decision nodes - commonly represented by squares2. Chance nodes - represented by circles3. End nodes - represented by triangles<br />a decision tree has only burst nodes (splitting paths) but no sink nodes (converging paths). <br />
  20. 20. Advantages<br />Amongst decision support tools, decision trees (and influence diagrams) have several advantages:<br />Decision trees:<br />Are simple to understand and interpret. People are able to understand decision tree models after a brief explanation. <br />Have value even with little hard data. Important insights can be generated based on experts describing a situation (its alternatives, probabilities, and costs) and their preferences for outcomes. <br />Use a white box model. If a given result is provided by a model, the explanation for the result is easily replicated by simple math. <br />Can be combined with other decision techniques. The following example uses Net Present Value calculations, PERT 3-point estimations (decision #1) and a linear distribution of expected outcomes (decision #2): <br />
  21. 21. How to Draw a Decision Tree<br />You start a Decision Tree with a decision that you need to make. <br />Draw a small square to represent this towards the left of a large piece of paper. <br />From this box draw out lines towards the right for each possible solution, and write that solution along the line. <br />At the end of each line, consider the results. If the result of taking that decision is uncertain, draw a small circle. If the result is another decision that you need to make, draw another square. Write the decision or factor above the square or circle. If you have completed the solution at the end of the line, just leave it blank. <br />Keep on doing this until you have drawn out as many of the possible outcomes and decisions as you can see leading on from the original decisions. <br />`<br />
  22. 22. Calculating Tree Values<br />Start by assigning a cash value or score to each possible outcome. Estimate how much you think it would be worth to you if that outcome came about. <br />Next look at each circle (representing an uncertainty point) and estimate the probability of each outcome. <br />
  23. 23. Calculating The Value of Uncertain Outcome Nodes<br />Where you are calculating the value of uncertain outcomes (circles on the diagram), do this by multiplying the value of the outcomes by their probability. The total for that node of the tree is the total of these values.<br />In the example in Figure 2, the value for 'new product, thorough development' is:<br />0.4 (probability good outcome) x $1,000,000 (value) =$400,0000.4 (probability moderate outcome) x $50,000 (value) =$20,0000.2 (probability poor outcome) x $2,000 (value) =$400+$420,400<br />
  24. 24. Calculating the Value of Decision Nodes<br />When you are evaluating a decision node, write down the cost of each option along each decision line. Then subtract the cost from the outcome value that you have already calculated. This will give you a value that represents the benefit of that decision.<br />Note that amounts already spent do not count for this analysis – these are 'sunk costs' and (despite emotional counter-arguments) should not be factored into the decision.<br />When you have calculated these decision benefits, choose the option that has the largest benefit, and take that as the decision made. This is the value of that decision node.<br />Result - By applying this technique we can see that the best option is to develop a new product. It is worth much more to us to take our time and get the product right, than to rush the product to market.<br />
  25. 25. Thank You!<br />~ Aditya Mahagaonkar<br />~ Ankita Singh<br />~ Sneha Nair<br />~ Vinita Gibson<br />