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Decision Making

Decision Making

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Decision Making

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  1. Decision Making Chapter 10

  2. Decision Making • Phases of Decision Making ► • Basic Concepts of Probability ► • Cognitive Illusions in Decision Making ► • Utility Models of Decision Making ▶ • Descriptive Models of Decision Making ▶

  3. Phases of Decision Making • Setting Goals ► • Gathering Information ► • Structuring the Decision ► • Making a Final Choice ► • Evaluating ► A Schematic View of these Phases Back

  4. Phases of Decision Making Back

  5. Setting Goals • The goals for a decision • What are you going to accomplish? • The goals influence decision making in various ways Back

  6. Gathering Information • Consider the decision of • Buying a new computer • Buying an apartment • Choosing a college major Back

  7. Structuring the Decision • Decision structuring • To manage various information • When there are a great number of options • When there are lots of considerations to be used in making the decision Back

  8. Making a Final Choice • Decide when to stop gathering information • Decide which information is more relevant or reliable Back

  9. Evaluating • To evaluate the whole process • What went well? • What didn’t go so well? • To reflect and identify the areas of the process that can stand improvement and those that ought to be used again in future, similar decision Back

  10. Basic Concepts of Probability • The condition of uncertainty • Probability • A measurement of a degree of uncertainty • Subjective probabilities • Probabilities are influenced by the estimators • Happy or sad; optimistic or pessimistic • Objective probabilities • Not influenced by the estimators Continue

  11. Basic Concepts of Probability • A 30-year-old woman discovers a lump in her breast and goes to her physician. The physician knows that only about 5 in 100 women of the patient’s age and health have breast cancer. A mammogram (breast X-ray) is taken. It indicates cancer 80% of the time in women who have breast cancer but falsely indicates cancer in healthy patients 20% of the time. The mammogram comes out positive. What is the probability that the patient has cancer? • 5*80%÷[(100-5)*20%]=4/23=0.17 Back

  12. Cognitive Illusions in Decision Making • How people gather and access the relevance of different pieces of information • Cognitive illusions • Certain systematic and common biases under most conditions but can lead to error when misapplied Continue

  13. Cognitive Illusions in Decision Making • Availability ► • Representativeness ► • Framing Effects ► • Anchoring ► • Sunk Cost Effects ► • Illusory Correlation ► • Hindsight Bias ► • Confirmation Bias ► • Overconfidence ► Back

  14. Availability • Ten students from a nearby college have indicated a willingness to serve on a curriculum committee. Their names are Ann, Bob, Dan, Elizabeth, Gary, Heidi, Jennifer, Laura, Terri, and Valerie. • The dean wants to form a two-person committee. What is your estimate of the number of distinct committees that could be formed? (don’t use formulas; just respond intuitively) • The dean wants to form an eight-person committee. What is your estimate of the number of distinct committees that could be formed? (don’t use formulas; just respond intuitively) Continue

  15. Availability • Consider the two structures shown below: • A path in a structure is a line that connects one “x” from each row, starting with the top row and finishing at the bottom row. How many paths do you think each structure has? Continue

  16. Availability • Tversky & Kahneman, 1973 • When faced with the task of estimating probability, frequency, or numerosity, people rely on shortcuts or rules of thumb (heuristics) to help make judgments easier. • Availability heuristic • Assessing the ease with which the relevant mental operation of retrieval, construction, or association can be carried out. • Formulas: 10!/{(x!)([10-x]!)} for problem 1 xy for problem 2 Back

  17. Representativeness • Two students, Linda and Joe, are having a boring Saturday afternoon in the student union. For lack of something better to do, they each begin flipping a quarter, keeping track of the way it lands over time. Then they compare results. Linda reports that her sequence of coin flips was heads, heads, heads, tails, tails, tails. Joe gets the following results: tails, tails, heads, tails, heads, heads. • Which student has obtained a more statistically probable series of results? Continue

  18. Representativeness • Kahneman & Tversky, 1973 • Representativeness heuristic • A belief that outcomes will always reflect characteristics of the process that generated them • Conducted an experiment • Using three conditions • Base rate ▶ • Similarity ▶ • Prediction ▶ Continue

  19. Representativeness • Write down your best guesses about the percentage now enrolled in each of the following nine fields of specialization. • Business administration • Computer science • Engineering • Humanities and education • Law • Library science • Medicine • Physical and life sciences • Social science and social work Back

  20. Representativeness • How similar Tom W. is to the typical graduate student in each of the following nine fields of graduate specialization. • Tom W. is of high intelligence, although lacking in true creativity. He has a need for order and clarity, and for neat and tidy systems in which every detail finds its appropriate place. His writing is rather dull and mechanical, occasionally enlivened by somewhat corny puns and by flashes of imagination of the sci-fi type. He has a strong drive for competence. He seems to have little feel and little sympathy for other people and does not enjoy interacting with others. Self-centered, he nonetheless has a deep moral sense. Back

  21. Representativeness • Participants were given the personality sketch but were told it was written several years ago, during Tom W.’s senior year of high school, based on his response to projective tests. They were asked to predict the likelihood for each field that Tom W. was currently a graduate student in it. Back

  22. Representativeness Gambler’s fallacy

  23. Representativeness • A random process will not always produce results that look random, especially in the short run. Continue

  24. Representativeness • Tversky & Kahneman, 1971 • Law of small numbers • Misuse of representativeness • “man who” argument (Nisbett & Ross, 1980) • I know a man who smoked three packs a day and lived to be 110. Back

  25. Framing Effects • You want to buy some fuels for your car, and you see two service stations, both advertising gasoline. Station A’s price is $1.00 per gallon; station B’s, $0.95. Station A’s sign also announces, “5 cents/gallon discount for cash!” Station B’s sign announces, “5 cents/gallon surcharge for credit cards.” All other factors being equal, to which station would you choose to go? Continue

  26. Framing Effects • Tversky & Kahneman, 1981 • People evaluate outcomes as changes from a reference point--- their current state • Depending on how their current state is described, they perceive certain outcomes as gains or losses. • The description “frame”s the decision or provides a certain context • Context effect Continue

  27. Framing Effects • Simply changing the description of a situation can lead people to adopt different reference points and therefore to see the same outcome as a gain in one situation and a loss in the other. Back

  28. Anchoring • Estimate the population of Philadelphia in April 2000 • Tim and Kim were given a starting value respectively: 1 million & 2 million • Tim: 1.25 million • Kim: 1.75 million • The starting point have huge effects on their final estimates • Correct value: 1.5 million Continue

  29. Anchoring • Definition • A decision-making heuristic in which final estimates are heavily influenced by initial value estimates • Estimate values • 8x7x6x5x4x3x2x1 • 1x2x3x4x5x6x7x8 • 2250 • 512 Back

  30. Sunk Cost Effects • A major educational initiative is begun in your hometown; $3 million is invested to help students stay away from cigarettes, liquor, and other drugs. In the third of four years, evidence begins to accumulate that the program is not working. A local legislator proposes ending funding to the program before the scheduled date. Howls of protest go up from some individuals, who claim that to stop a program after a large expenditure of funds has been spent would be a waste. Continue

  31. Sunk Cost Effects • A bias in decision making in which already “spent” costs unduly influence decision making to continue. Back

  32. Illusory Correlation • Hair twisting • The person pinches a strand of hair between thumb and forefinger and proceeds to twist it around the forefinger. • If you believe this behavior is especially likely in people undergoing a great deal of stress. • Observe 150 students • The results: Continue

  33. Illusory Correlation • People report seeing data associations that seem plausible even when associations are not present • Illusory correlation • An association between factors that is not supported by data but seems plausible Back

  34. Hindsight Bias • A tendency to exaggerate the certainty of what could have been anticipated ahead of time • Once you know how a decision has turned out, you look back on the events leading up to the outcome as being more inevitable than they really were Back

  35. Confirmation Bias • The tendency to search only for information that will confirm one’s initial hunch or hypothesis, and to overlook or ignore any other information Back

  36. Overconfidence • Choose one answer for each question, and rate your confidence in your answer on a scale from .5 (just guessing) to 1.0 (complete certain) • Which magazine had the largest circulation in 1978? • Time Reader’s Digest • Which city had the larger population in 1953? • St. Paul, MN New Orleans, LA • Who was the 21st president of the United States? • Arthur Cleveland • Who began the profession of nursing? • Nightingale Barton Back

  37. Overconfidence • Typical findings Calibration curve Continue

  38. Overconfidence • An overly positive judgment of one’s own decision-making abilities and performance • Confidence ratings are higher than actual accuracy Back

  39. Utility Models of Decision Making • What people are doing when they structure a decision and choose from alternatives? • Normative models • Ideal performance under ideal circumstances • Prescriptive models • How we ought to make decisions under non-ideal circumstances • Descriptive models • Detail what people actually do when they make decisions Continue

  40. Utility Models of Decision Making • Expected-utility theory • A normative model of decision making in which the decision maker weights the personal importance and the probabilities of different outcomes in choosing among alternatives in order to maximize overall satisfaction of personal goals • Expected value = ∑(Pi x Vi) • P=probability of the ith outcome • V=the monetary value of that outcome Continue

  41. Utility Models of Decision Making • Imaging a lottery with ten tickets numbered 1 through 10. If the ticket drawn is numbered 1, you win $10. If the ticket drawn is numbered 2, 3, or 4, you win $5. Any other numbers drawn are worth nothing. The EV of this lottery is • (.1 x $10) + (.3 x $5) + (.6 x $0) =$1.60 Continue

  42. Utility Models of Decision Making • Expected utility (EU) = ∑(pi x ui) Continue

  43. Utility Models of Decision Making • Multiattribute Utility Theory • A normative model of decision making that provides a means of integrating different dimensions and goals of a complex decision. • It involves six steps • Breaking a decision down into independent dimensions • Determining the relative weights of each of those dimensions • Listing all the alternatives • Ranking all the alternatives along the dimensions • Multiplying the rankings by the weightings to determine a final value for each alternative • Choosing the alternative with the highest value Continue

  44. Utility Models of Decision Making Weightings of five dimensions in the decision “choosing” a major. Continue

  45. Utility Models of Decision Making Continue

  46. Utility Models of Decision Making Continue

  47. Utility Models of Decision Making Continue

  48. Utility Models of Decision Making • Payne, 1976 • People do not always spontaneously use MAUT. • How people chose apartments when given different amounts of information about different numbers of alternatives. • Participants were presented with an “information board” carrying a number of cards. ▶

  49. Utility Models of Decision Making Continue

  50. Utility Models of Decision Making • Two factors were varied in the experiment • The number of alternatives presented • The number of factors of information available per alternative • When participants had to decide among 6 to 12 apartments, they used another strategy. • They eliminated some alternatives on the basis of only one or a few dimensions. • Elimination by aspects • A descriptive model Back