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Heuristics

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  • 1. Please share your email address with us! We’d like to send you a link to this webinar’s recording and resources, and notifications for future webinars. Provide feedback and earn CE Credit with one link: We will provide this link at the end of the webinar Welcome to the Military Families Learning Network Webinar Heuristics, Anchoring & Financial Management This material is based upon work supported by the National Institute of Food and Agriculture, U.S. Department of Agriculture, and the Office of Family Policy, Children and Youth, U.S. Department of Defense under Award Numbers 2010-48869-20685 and 2012-48755-20306.
  • 2. This material is based upon work supported by the National Institute of Food and Agriculture, U.S. Department of Agriculture, and the Office of Family Policy, Children and Youth, U.S. Department of Defense under Award Numbers 2010-48869-20685 and 2012-48755-20306. Research and evidenced-based professional development through engaged online communities. eXtension.org/militaryfamilies Welcome to the Military Families Learning Network
  • 3. POLL
  • 4. Connect with the Personal Finance Team » Facebook: PersonalFinance4PFMs » Twitter: #MFLNPF
  • 5. Personal Finance Twitter Cohort A 2-week learning experience beginning June 9 presented by the MFLN Personal Finance team and the Network Literacy Community of Practice. • Become a part of a community of learners that will form and build your online network. • Engage in conversations within the Twitter community centered around your interests. • Learn from guides that help new users maximize their Twitter experience. • For more information and to register: https://twittercohort.wordpress.com/
  • 6. For Resources, Recording, and More Information: https://learn.extension.org/events/1555#.U4S4Va1dXrU
  • 7. Dr. Michael Gutter Dr. Michael Gutter is an Assistant Professor and Financial Management State Specialist for the Department of Family, Youth, and Community Sciences, in the Institute for Food and Agricultural at the University of Florida. Dr. Gutter is also the Principle Investigator for the Military Families Learning Network’s Personal Finance Community of Practice. Dr. Gutter is the current Vice President of the Florida Jumpstart Coalition and serves on the editorial boards for the Journal of Consumer Affairs, Journal of Consumer Education, and the Journal of Financial Counseling and Planning. Dr. Gutter’s outreach projects include Managing in Tough Times, Florida Saves, Get Checking, and the Master Money Mentor. His projects focus on enabling access to resources and services as well as improving people’s knowledge and understanding about family resource management. These projects have had funding from the Consumer Federation of America and Bank of America.
  • 8. Heuristics, Anchoring, Narrowing Choice Dr. Michael S Gutter Interim Family and Consumer Science Program Leader MLFN Personal Finance Team @mikegutter msgutter@ufl.edu
  • 9. Thinking About How Our Mind Works • GPA Example – http://youtu.be/KyM3d4gQGhM Mammalian Einstein-ian
  • 10. Interesting Idea • So how do we view ourselves? • Our status? • What we have? • Depends on what others have around us… • http://youtu.be/_ERQEVdIinc
  • 11. Are we predictably irrational? • It is not surprising that we are not always perfectly rational • But are our departures from perfect rationality completely random? • Or are these departures predictable? • If we can find predictable patterns of irrationality in human behavior, then we can improve economic theory
  • 12. Motivations and Objectives • The two main motivations for behavioral economics concern apparent weaknesses in standard economic theory: – People sometimes make choices that are difficult to explain with standard economic theory – Standard economic theory can lead to seemingly unreasonable conclusions about consumer welfare • Behavioral economics grew out of research in psychology • The objective is to modify, supplement, and enrich economic theory by adding insights from psychology – Suggesting that people care about things standard theory typically ignores, like fairness or status – Allowing for the possibility of mistakes 13-13
  • 13. Methods • Behavioral economics uses many of the same tools and frameworks as standard economics – Assumes individuals have well-defined objectives, that objectives and actions are connected, and actions affect well-being – Relies on mathematical models – Subjects theories to careful empirical testing • Important difference is use of experiments using human subjects • Behavioral economists tend to use experimental data to test their theories rather than drawing data from the real world 13-14
  • 14. A Representativeness Example • Consider the following description: “Steve is very shy and withdrawn, invariably helpful, but with little interest in people, or in the world of reality. A meek and tidy soul, he has a need for order and structure, and a passion for detail.” • Is Steve a farmer, a librarian, a physician, an airline pilot, or a salesman?
  • 15. Rules of Thumb/Heuristics • Thinking through every alternative for complex economic decisions is difficult • May rely on simple rules of thumb that have served well in the past • Popular rules may be choices that are nearly optimal, using one is not necessarily a mistake • Allow judgment and decision making in cases where specific and accurate solutions are either unknown or unknowable 13-16
  • 16. Rules of Thumb/Heuristics • Example: saving – In economic models finding the best rate of savings involves complex calculations – In practice people seem to follow rules of thumb such as 10% of income – These rules appear to ignore factors that theory says should be important, such as expected future income • Availability, anchoring and adjustment, and representativeness are frequently considered “metaheuristics” since they engender many specific effects
  • 17. Three Major Human Probability-Assessment Heuristics/Biases (Tversky and Kahneman, 1974) • Representativeness – The more object X is similar to class Y, the more likely we think X belongs to Y • Availability – The easier it is to consider instances of class Y, the more frequent we think it is • Anchoring – Initial estimated values affect the final estimates, even after considerable adjustments
  • 18. The Representativeness Heuristic • We often judge whether object X belongs to class Y by how representative X is of class Y • For example, people order the potential occupations by probability and by similarity in exactly the same way • The problem is that similarity ignores multiple biases
  • 19. Representative Bias (1): Insensitivity to Prior Probabilities • The base rate of outcomes should be a major factor in estimating their frequency • However, people often ignore it (e.g., there are more farmers than librarians) – E.g., the lawyers vs. engineers experiment: • Reversing the proportions (0.7, 0.3) in the group had no effect on estimating a person’s profession, given a description • Giving worthless evidence caused the subjects to ignore the odds and estimate the probability as 0.5 – Thus, prior probabilities of diseases are often ignored when the patient seems to fit a rare-disease description
  • 20. Representative Bias (2): Insensitivity to Sample Size • The size of a sample withdrawn from a population should greatly affect the likelihood of obtaining certain results in it • People, however, ignore sample size and only use the superficial similarity measures • For example, people ignore the fact that larger samples are less likely to deviate from the mean than smaller samples
  • 21. Representative Bias (3): Misconception of Chance • People expect random sequences to be “representatively random” even locally – E.g., they consider a coin-toss run of HTHTTH to be more likely than HHHTTT or HHHHTH • The Gambler’s Fallacy – After a run of reds in a roulette, black will make the overall run more representative (chance as a self-correcting process??) • Even experienced research psychologists believe in a law of small numbers (small samples are representative of the population they are drawn from)
  • 22. Representative Bias (4): Insensitivity to Predictability • People predict future performance mainly by similarity of description to future results • For example, predicting future performance as a teacher based on a single practice lesson – Evaluation percentiles (of the quality of the lesson) were identical to predicted percentiles of 5-year future standings as teachers
  • 23. The Availability Heuristic • The frequency of a class or event is often assessed by the ease with which instances of it can be brought to mind • The problem is that this mental availability might be affected by factors other than the frequency of the class
  • 24. Availability Biases (1): Ease of Retrievability • Classes whose instances are more easily retrievable will seem larger – For example, judging if a list of names had more men or women depends on the relative frequency of famous names • Salience affects “retrievability” – E.g., watching a car accident increases subjective assessment of traffic accidents
  • 25. The Anchoring and Adjustment Heuristic • People often estimate by adjusting an initial value until a final value is reached • Initial values might be due to the problem presentation or due to partial computations • Adjustments are typically insufficient and are biased towards initial values, the anchor
  • 26. Anchoring and Adjustment Biases (1): Insufficient Adjustment • Anchoring may occur due to incomplete calculation, such as estimating by two high-school student groups – the expression 8x7x6x5x4x3x2x1 (median answer: 512) – with the expression 1x2x3x4x5x6x7x8 (median answer: 2250) • Anchoring occurs even with outrageously extreme anchors (Quattrone et al., 1984) • Anchoring occurs even when experts (real-estate agents) estimate real-estate prices (Northcraft and Neale, 1987)
  • 27. Anchoring and Adjustment Biases (2): Evaluation of Conjunctive and Disjunctive Events • People tend to overestimate the probability of conjunctive events (e.g., success of a plan that requires success of multiple steps) • People underestimate the probability of disjunctive events (e.g. the Birthday Paradox) • In both cases there is insufficient adjustment from the probability of an individual event Probability that at least two people in N share a birthday Hint think of the # of possible pairing not people
  • 28. Anchoring • http://youtu.be/HefjkqKCVpo
  • 29. Anchoring • 55 subjects were shown a series of six common products with average retail price of $70 • For each product, the experiment had three steps: Each participant was asked – his/her SSN – whether he/she would buy the product at a price equal to the last 2 digits of SSN – The maximum he/she would be willing to pay
  • 30. Incoherent Choices: Anchoring• Anchoring occurs when someone’s choices are linked to prominent but irrelevant information • Suggests that some choices are arbitrary and can’t reflect meaningful preferences • Example: experiment showing subjects’ willingness to pay for various goods was closely related to the last two digits of their social security number, by suggestion – Skeptics note that subjects had little experience purchasing the goods in the experiment – Might have been less sensitive to suggestion if used familiar products • Significance of anchoring effects for many economic choices remains unclear 13-32
  • 31. Changing the Anchor: Getting in Line Behind Yourself • Why does someone pay so much for Starbuck’s Coffee? • http://youtu.be/FaO3aGmuNFc • Can we lower the anchor?
  • 32. Have merchants like Starbucks influenced our thinking?
  • 33. Thinking About Coffee • Have marketers shifted how we think about coffee and our price point • To what extent can we filter external influences?
  • 34. Anchoring Source: Dan Ariely, Predictably Irrational: Chapter 2 Supply and Demand video at http://www.youtube.com/watch?v=FaO3aGmuNFc&feature= youtu.be The process of seeding a thought in a person’s mind and having that thought influence their later actions.
  • 35. Anchoring • Is the height of the tallest redwood tree more or less than 1,200 feet? • What is your best guess about the height of the tallest redwood? Source: Daniel Kahneman, “Thinking, Fast and Slow”
  • 36. Results of Redwood Experiment • Is the height of the tallest redwood tree more or less than 1,200 feet? – Mean answer: 844 feet • Is the height of the tallest redwood tree more or less than 180 feet? – Mean answer: 282 feet • Anchoring Index = ratio between differences • Anchoring index = 0 for people able to ignore anchor
  • 37. Results of Redwood Experiment • height more or less than 1,200 feet? – Mean answer: 844 feet • height more or less than 180 feet? – Mean answer: 282 feet • Anchoring index = 844-282 / 1200 – 180 = 55% • Anchoring index = 0% for people able to ignore anchor and 100% controlled by it
  • 38. Anchoring • Is the average price of a German car in the US more or less than $100,000? • What type of cars does this bring to mind? Source: Daniel Kahneman http://youtu.be/HefjkqKCVpo
  • 39. Real- Estate Experiment • Real-estate agents asked to assess the value of a house actually on the market • Visited house • Given booklets about house that include ap price – ½ of agents saw booklets w/price higher than actual listed price – ½ saw price that was lower than listed price Source: Daniel Kahneman, “Thinking, Fast and Slow”
  • 40. Real-estate Experiment • Viewed house & booklet • Gave opinion about what they thought was a reasonable buying price and selling price • Also asked what factors influenced their opinion – Said listing price did not influence
  • 41. Real-Estate Experiment Results • Anchoring index for real-estate professionals was 41% • Anchoring index for business school students was found to be 48%
  • 42. Negotiation and Anchoring • Sellers point of view – anchor your thinking to a higher price • Price presented • Focus attention and search memory for arguments against the anchor
  • 43. Incoherent Choices: Anchoring • Anchoring occurs when someone’s choices are linked to prominent but irrelevant information • Suggests that some choices are arbitrary and can’t reflect meaningful preferences Source: Dr. Michael Gutter, Behavioral Economics, PowerPoint
  • 44. Incoherent Choices: Anchoring • Example: Experiment showing subjects’ willingness to pay for various goods was closely related to the last two digits of their social security number, by suggestion – Skeptics note that subjects had little experience purchasing the goods in the experiment – Might have been less sensitive to suggestion if used familiar products Source: Dr. Michael Gutter, Behavioral Economics, PowerPoint
  • 45. Anchoring • Significance of anchoring effects for many economic choices remains unclear • What do you think?
  • 46. Endowment Effect • Half the participants were given mugs available at the campus bookstore for $6 • The other half were allowed to examine the mugs • Each student who had a mug was asked to name the lowest sale price • Each student who did not have a mug was asked to name the highest purchase price • Supply and demand curves were constructed and the equilibrium price was obtained • Trade followed • There were four rounds of this
  • 47. Bias Toward the Status Quo: Endowment Effect • The endowment effect is people’s tendency to value something more highly when they own it than when they don’t • Example: experiment in which median owner value for mugs was roughly twice the median non-owner valuation • Some economists think this reflects something fundamental about the nature of preferences • Incorporating the endowment effect into standard theory implies an indifference curve kinked at the consumer’s initial consumption bundle – Smooth changes in price yield abrupt changes in consumption 13-50
  • 48. A Special Type of Bias: Framing • Risky prospects can be framed in different ways- as gains or as losses • Changing the description of a prospect should not change decisions, but it does, in a way predicted by Tversky and Kahneman’s (1979) Prospect Theory • In Prospect Theory, the negative effect of a loss is larger than the positive effect of a gain • Framing a prospect as a loss rather than a gain, by changing the reference point, changes the decision by changing the evaluation of the same prospect • May resolve a number of puzzles related to risky decisions
  • 49. A Value Function in Prospect Theory GainsLosses - +
  • 50. Default effect: retirement • Prior to April 1, 1998, the default option was nonparticipation in the retirement plan • After April 1, 1998, all employees were by default enrolled in a plan that invested 3% of salary in money market mutual funds • Only the default option changed
  • 51. Bias Toward the Status Quo: Default Effect • When confronted with many alternatives, people sometimes avoid making a choice and end up with the option that is assigned as a default • Example: Experiment showing that more subjects kept $1.50 participation fee rather than trading it for a more valuable prize when the list of prizes to choose from was lengthened • Possible explanation is that psychological costs of decision-making rise as number of alternatives rises, increasing number of people who accept the default • Retirement saving example illustrates the default effect when the stakes are high • OPT OUT strategy 13-55
  • 52. Lets Explore A Subscription • http://youtu.be/xOhb4LwAaJk
  • 53. Choice Architecture: Narrow Framing • Narrow framing is the tendency to group items into categories and, when making choices, to consider only other items in the same category • Can lead to behavior that is hard to justify objectively • Examples: – Far more people are willing to pay $10 to see a play after losing $10 entering a theater vs. losing the ticket on the way in – Calculator and jacket example, decisions about whether to drive 20 minutes to save $5 • These choices may be mistakes or may reflect the consumers’ true preferences 13-57
  • 54. Please put your notes down for a moment
  • 55. Narrow Framing • Q1: Imagine you have decided to see a play where admission is $10. As you enter the theatre you discover that you have lost a $10 bill. Would you still buy a ticket to see the play? • Q2: Imagine you have bought a $10 ticket to see a play. As you enter the theatre you discover that you have lost the ticket. Would you buy a new ticket to see the play?
  • 56. • 88% say yes to Q1 • 56% say yes to Q2
  • 57. Narrow Framing • Q1: Imagine you are about to buy a jacket for $125 and a calculator for $15. The calculator salesman informs you that a store 20 minutes away offers the same calculator for $10. Would you make the trip to the other store? • Q2: Imagine you are about to buy a jacket for $15 and a calculator for $125. The calculator salesman informs you that a store 20 minutes away offers the same calculator for $120. Would you make the trip to the other store?
  • 58. • 68% say yes to Q1 • 29% to Q2
  • 59. Framing Experiment (I) • Imagine the US is preparing for the outbreak of an Asian disease, expected to kill 600 people (N = 152 subjects): – If program A is adopted, 200 people will be saved – If program B is adopted, there is one third probability that 600 people will be saved and two thirds probability that no people will be saved
  • 60. Framing Experiment (I) • Imagine the US is preparing for the outbreak of an Asian disease, expected to kill 600 people (N = 152 subjects): – If program A is adopted, 200 people will be saved (72% preference) – If program B is adopted, there is one third probability that 600 people will be saved and two thirds probability that no people will be saved (28% preference)
  • 61. Framing Experiment (II) • Imagine the US is preparing for the outbreak of an Asian disease, expected to kill 600 people (N = 155 subjects): – If program C is adopted, 400 people will die – If program D is adopted, there is one third probability that nobody will die and two thirds probability that 600 people will die
  • 62. Framing Experiment (II) • Imagine the US is preparing for the outbreak of an Asian disease, expected to kill 600 people (N = 155 subjects): – If program C is adopted, 400 people will die (22% preference) – If program D is adopted, there is one third probability that nobody will die and two thirds probability that 600 people will die (78% preference)
  • 63. What Choices Do we Give? • How can our programs work with this? – Encourage default savings rates? – Provide ranges for people to select using narrow choice – If we want to increase savings by workers, we could ask employers to ... enroll them automatically [in a 401k plan] unless they specifically choose otherwise.
  • 64. – If we want to increase the supply of transplant organs in the United States, we could presume that people want to donate, rather than treating non-donation as the default. ... – If we want to increase charitable giving, we might give people the opportunity to join a ... plan, in which some percentage of their future wage increases are automatically given to charities... – If we want to respond to the recent problems in [credit markets], we might design disclosure policies that ensure consumers can see exactly what they are paying and make easy comparisons among the possible options.
  • 65. Subscription Choice • Dan Ariely demonstration • Economist.com subscription choices: 1. 1 year online access - $59.00 2. 1 year print subscription - $125 3. 1 year online & print - $125
  • 66. Subscription Choice • Example demonstrated by Dan Ariely • Experiment with MIT students asked what they would choose Economist.com subscription choices: 1. 1 year online access - $59.00. 16% 2. 1 year print subscription - $125. 0% 3. 1 year online & print - $125. 84%
  • 67. Subscription Choice • Experiment 2, with students. Removed option 2, print subscription Economist.com subscription choices 1. 1 year online access - $59.00. 16% 68% 2. 1 year online & print - $125. 84% 32%
  • 68. Framing Example: Which sounds more attractive? • Cold Cuts • 90% Fat Free • Cold Cuts • 10% Fat Source: TFS, Kahneman
  • 69. Product Placement • How will the placing of a product influence what you buy?
  • 70. Product Placement • An in-store experiment was performed to investigate the effects of shelf placement (high, middle, low) on consumers' purchases of potato chips. • Placement of potato chips on the middle shelf was associated with the highest percentage of purchases. Source: Valdimar Sigurdsson, Hugi Saevarsson, and Gordon Foxall, J Appl Behav Anal. 2009 Fall; 42(3): 741–745. doi: 10.1901/jaba.2009.42-741
  • 71. Influence of Emotional Arousal • People have 2 sides – Emotional side – Unemotional side • Effects decision making • Appeal to side making decisions Source: Dr. Dan Ariely http://www.youtube.com/watch?v=mFMDgW0wDeI
  • 72. Why Free is Not Free • “Why Free is Dangerous” • http://www.youtube.com/watch?v=TlXjdW 0xQco
  • 73. Credit Card Choice • Card X • 9% APR • $100 annual fee • Card Y • 14% APR • $0 annual fee What do you think?
  • 74. Free • Examples: – Free Banking Services, • Free checking, free online services – Credit Cards with points and rewards • Are they free for everybody? • Who pays?
  • 75. Choices Involving Time • Many behavioral economists see standard theory of decisions involving time as too restrictive, it rules out patterns of behavior that are observed in practice • For example, theory rules out these three observed behaviors – Preferences over a set of alternatives available at a future date are dynamically inconsistent if the preferences change as the date approaches – The sunk cost fallacy is the belief that, if you paid more for something, it must be more valuable to you – Projection bias is the tendency to evaluate future consequences based on current tastes and needs 13-80
  • 76. The Problem of Dynamic Inconsistency • Thought to reflect a bias toward immediate gratification, know as present bias – A person with present bias often suffers from lapses of self-control • Laboratory experiments have documented the existence of present bias • Precommitment is useful in situations in which people don’t trust themselves to follow through on their intentions • Precommitment is a choice that removes future options – Example: A student who wants to avoid driving while intoxicated hands his car keys to a friend before joining a party 13-81
  • 77. The Problem of Dynamic Inconsistency • People often waste expensive gym memberships – The LIU gym plan for faculty
  • 78. We should ignore sunk costs but often do not • Uncomfortable shoes • Bad movie rentals • Season ticket discounts lead to lower initial attendance
  • 79. Projection bias in forecasting future tastes and needs • Hungry shoppers tend to buy more than sated shoppers when shopping for the week ahead – We often remind people to not shop when they are hungry. – Do not shop for other things when you need immediately (when possible to plan ahead) • People tend to underestimate their adaptability to change – Giving up some spending to save or pay more to debt • Giving up cable, etc.
  • 80. • How does this affect planning for the future? • SMART Goals that are longer term?
  • 81. Prospect Theory Revisited: Trouble Assessing Probabilities • People tend to make specific errors in assessing probabilities • Hot-hand fallacy is the belief that once an event has occurred several times in a row it is more likely to repeat – Arises when people can easily invent explanations for streaks, e.g., basketball 13-86
  • 82. • Gambler’s fallacy is the belief that once an event has occurred it is less likely to repeat – Arises when people can’t easily invent explanations for streaks, e.g., state lotteries • Both fallacies have important implications for economic behavior, e.g., clearly relevant in context of investing • Overconfidence causes people to: – Overstate the likelihood of favorable events – Understate the uncertainty involved
  • 83. Hot-hand fallacy • Philadelphia 76ers, 48 home games, 1980- 81 season
  • 84. Gambler’s fallacy • A study of nearly 1800 daily drawings between 1988 and 1992 in a New Jersey lottery showed that after a number came up a winner, bettors tended to avoid it • Do we see this bias in investors? – Many investor’s chase returns…
  • 85. Overconfidence • In one study of US students with an average age of 22, 82% ranked their driving ability among the top 30% of their age group – Well I was a great drive at 16… • In the manufacturing sector, more than 60% of new entrants exit within five years; nearly 80% exit within ten years – Yet people start businesses…
  • 86. Please put your notes down again
  • 87. Preferences Toward Risk • Two puzzles involving observed behavior and risk preferences • Low probability events: – Experimental subjects exhibit aversion to risk in gambles with moderate odds – However, some subjects appear risk loving in gambles with very high payoffs with very low probabilities • Aversion to very small risks: – Many people also appear reluctant to take even very tiny shares of certain gambles that have positive expected payoffs – Implies a level of risk aversion so high it is impossible to explain the typical person’s willingness to take larger financial risks 13-92
  • 88. Pick one: • Option A: Win $2,500 • Option B: Win $5,000 with 1/2 probability
  • 89. Now Pick • Option C: Win $5 • Option D: Win $5,000 with 1/1000 probability
  • 90. Low probability events grab all the attention • Option A: Win $2,500 • Option B: Win $5,000 with 1/2 probability • Most choose Option A over B, suggesting risk- averse preferences • Option C: Win $5 • Option D: Win $5,000 with 1/1000 probability • A sizable majority picks Option D over C, which is puzzling because the choice suggests risk- loving preferences
  • 91. Extreme risk aversion • Option A: Win $1,010 with 50% probability and lose $1,000 with 50% probability • Option B: Win $10.10 with 50% probability and lose $10.00 with 50% probability
  • 92. Extreme risk aversion • Option A: Win $1,010 with 50% probability and lose $1,000 with 50% probability • Most people refuse this gamble • Option B: Win $10.10 with 50% probability and lose $10.00 with 50% probability • Most people refuse this gamble too, suggesting extreme risk aversion
  • 93. Choices Involving Strategy • Some of game theory’s apparent failures may be attributable to faulty assumptions about people’s preferences – May not be due to fundamental problems with the theory itself • Many applications assume that people are motivated only by self-interest • Players sometimes make decisions that seem contrary to their own interests 13-98
  • 94. Voluntary Contribution Games • In a voluntary contribution game: – Each member of a group makes a contribution to a common pool – Each player’s contribution benefits everyone 13-99
  • 95. • Creates a conflict between individual interests and collective interests • Like a multi-player version of the Prisoners’ Dilemma • Game theory predicts the behavior of experienced subjects reasonably well • For two-stage voluntary contribution game, predictions based on standard game theory are far off • Assumptions about players’ preferences may be incorrect
  • 96. Importance of Social Motives: The Dictator Game • In the dictator game: – The dictator divides a fixed prize between himself and the recipient – The recipient is a passive participant – Usually no direct contact during the game – Strictly speaking, not really a game! 13- 101
  • 97. • Most studies find significant generosity, a sizable fraction of subjects divides the prize equally • Illustrates the importance of social motives: altruism, fairness, status
  • 98. Importance of Social Motives: The Ultimatum Game • In the ultimatum game: – The proposer offers to give the recipient some share of a fixed prize – The recipient then decides whether to accept or reject the proposal – If she accepts, the pie is divided as specified; if she rejects, both players receive nothing 13- 103
  • 99. • Theory says the proposer will offer a tiny fraction of the prize; the recipient will accept • Studies show that many subjects reject very low offers; the threat of rejection produces larger offers • In social situations, emotions such as anger and indignation influence economic decisions
  • 100. Importance of Social Motives: The Trust Game • In the trust game: – The trustor decides how much money to invest – The trustee divides up the principal and earnings 13- 105
  • 101. • If players have no motives other than monetary gain, theory says that trustees will be untrustworthy and trustors will forgo potentially profitable investments • Studies show that – Trustors invested about half of their funds – Trustees varied widely in their choices – Overall, trustors received about $0.95 in return for every dollar invested • Many (but not all) people do feel obligated to justify the trust shown in them by others, thus many are willing to extend trust • This game helps us understand why business conducted on handshakes and verbal agreements works
  • 102. Why is Saving So Difficult? • We focus on what we give up? • We are not really wired to focus on the future – takes energy to do so • Money is abstract – Having more in retirement by investing? – But money today money tomorrow is confusing choice for people – Critical to present values in purchasing power or real terms – Talk to people in terms of annuities – http://youtu.be/-Cw4PiCB8X8
  • 103. Example • Instead of saying one needs 350,000 in savings? – Present as annuity – If you save XYZ you can have ABC in retirement income • PV 350,000, FV = 0, N = 20, I/Yr = 5 • PMT = $28K per year • Want more income? Save more…
  • 104. Smart Couponing • Are you familiar with prices? • Comparison shop • Shop with a list • What is the goal? – Try new products? – Save money?
  • 105. Couponing • Does buying more save you money? • Coupons – Usually for non-generic, non-staples
  • 106. Financial Habits • What do you spend money on? • How much is allocated for different expenses? • Where do you buy? • When do you go shopping? • What effect do your purchases have on your goals?
  • 107. Marketing to Your Personality • Marketers study our habits • Market to our perceived needs • They also create needs and wants
  • 108. Advertising & Emotional Appeals • Peer Approval or Social Acceptance • Status • Excitement • Fear • Other types?
  • 109. Before Spending • Why am I making this purchase? – Is there more than one reason? • How will it effect me in the short & long term? • What will I be getting & what will I be giving up?
  • 110. Before Shopping • Comparison shop – Online – big ticket items • Keep track of what you spend • Be aware of your surroundings & marketing influences – Brick & Mortar • Design & Ambience – Online
  • 111. Mental Checklist • What should you consider before you go shopping?
  • 112. Sources: • Dan Ariely, Predictably Irrational, Videos on You Tube • Daniel Kahneman, Thinking, Fast & Slow, 2011 • Valdimar Sigurdsson, Hugi Saevarsson, and Gordon Foxall, J Appl Behav Anal. 2009 Fall; 42(3): 741– 745. doi: 10.1901/jaba.2009.42-741
  • 113. What is the problem with free? • When free is dangerous… – http://youtu.be/TlXjdW0xQco
  • 114. Additional Issues • Influence of Arousal • http://youtu.be/MuTP1XJWKmA • Cost of Social Norms • http://youtu.be/AIqtbPKjf6Q
  • 115. Some Additional Cool Videos • http://danariely.com/videos/#TOC24 • Are We In Control of Our Decisions – http://youtu.be/9X68dm92HVI • The IKEA Effect – http://youtu.be/VQ_CncrR-uU • Paying More For Less – http://youtu.be/vIS-OLgA8p4
  • 116. Next Virtual Learning Event Webinar The Culture of Personal Finance • June 5, 11 a.m. – 1 p.m. ET • Speaker: Dr. Barbara O’Neill • 2 AFC CEUs available • More information: https://learn.extension.org/events/1556#.U4S6F a1dXrU
  • 117. Military Families Learning Network This material is based upon work supported by the National Institute of Food and Agriculture, U.S. Department of Agriculture, and the Office of Family Policy, Children and Youth, U.S. Department of Defense under Award Numbers 2010-48869-20685 and 2012-48755-20306. Family Development Military Caregiving Personal Finance Network Literacy Find all upcoming and recorded webinars covering: http://www.extension.org/62581

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