Questions & explanations
1. Give an example where original PT violates dominance but CPT does not.
Consider gamble A: $100 with probability 0.01, $0 with 0.99. Gamble B: $100 with probability 0.02, $0 with 0.98. B dominates A because it gives a higher chance of $100. With original PT and weighting, w(0.01) might be 0.07, w(0.02) might be 0.10. But if w(0.02) is not exactly double? Actually, the violation can occur when sum of weights for complementary events is less than 1? Let's do: For A, value = w(0.01)*v($100). For B, value = w(0.02)*v($100). Since w(0.02) < 2w(0.01) due to subadditivity? Actually, w(0.02) might be 0.10 and w(0.01)=0.07, so B is higher. But a classic violation uses three outcomes. I'll use: Gamble A: $200 with 0.1, $100 with 0.9; Gamble B: $200 with 0.1, $100 with 0.8, $0 with 0.1. B is dominated by A? Actually A gives $100 with 0.9, B gives $100 with 0.8, so A dominates. With PT, if w(0.9) is underweighted and w(0.1) overweighted, B might be chosen? Need a clear example from literature: Usually, the violation requires the weighting function to be non-linear. A known example: two-outcome gambles can violate if w(p)+w(1-p) < 1. So for A: $200 with p=0.5, $0 wit
2. How does the planning fallacy differ in monochronic vs polychronic cultures?
The planning fallacy is the tendency to underestimate task completion time. In monochronic cultures, like the US, people see time as linear and schedules as fixed, so they plan optimistically by focusing on the best case. In polychronic cultures, like many Latin American or Middle Eastern societies, time is more flexible and multitasking is common, so plans are looser. However, the planning fallacy may appear differently: in polychronic cultures, people might allow more buffer time because they expect interruptions. But also, they may underestimate how long tasks take due to optimism. Both cultures show the fallacy, but the expression changes due to time perception. Monochronic cultures may feel more stress when plans fail because they value punctuality. Polychronic cultures may accept delays more easily.
3. Compare overconfidence and optimism bias – how are they similar and different?
Both overconfidence and optimism bias involve unrealistic beliefs. Overconfidence is about thinking you are better than you are, such as in skills or knowledge. Optimism bias is about thinking the future will be better than it is likely to be. For example, an overconfident person might think they are great at driving, while an optimistic person thinks they will never have an accident. They are similar because both can lead to taking too much risk. The difference is that overconfidence focuses on the self, while optimism bias focuses on outcomes. Overconfidence often involves an illusion of control, while optimism bias involves a rosy view of events. Both are common and can work together, like when a startup founder is overconfident about their ability and optimistic about their business's success.
4. Compare hindsight bias across cultures with different views on fate or destiny.
Hindsight bias is the tendency to see past events as predictable after they happen. In cultures that believe in fate, people may show stronger hindsight bias because they think events were destined. For example, after a natural disaster, someone in a fatalistic culture might say, 'It was meant to happen.' In cultures that emphasize human control, people might still say 'I knew it all along' but focus on their own choices. Studies show that people in collectivist, fate-oriented cultures often have stronger hindsight bias. They interpret outcomes as inevitable, reducing regret. In contrast, individualistic cultures may experience more counterfactual thinking, like 'if only I had done something different.' The bias serves to make sense of the world, but cultural beliefs shape how it manifests.
5. Why do framing effects matter for public policy communication?
Framing effects matter because the way a policy is described can influence public support. For example, a tax cut can be framed as 'you get a $200 rebate' (gain) or 'the government is reducing its spending' (neutral or loss). People are more likely to support the rebate frame. Similarly, public health messages about vaccination can be framed as '99% chance of no side effects' (gain) or '1% chance of mild side effects' (loss) – the gain frame increases uptake. Policymakers need to be aware that framing can change behavior, and they should use it ethically to promote beneficial choices. For instance, framing exercise as 'add 5 years to your life' (gain) is more effective than 'reduce your risk of early death' (loss). Understanding framing helps design better communication.
6. Give an example where expected utility theory would recommend a different choice than 'pick the safest option'.
Suppose you can either take a sure $100 or a gamble with 10% chance to win $1,000 and 90% chance to win $0. The sure option is safe. Expected utility theory, if we assume utility = money, gives the gamble an expected value of $100 (0.1 × $1000 + 0.9 × $0 = $100). So the theory says both options have the same expected value, so a person might be indifferent. However, if the person is risk-neutral, they see no difference. But if the person is risk-seeking, they might prefer the gamble. The theory does not always recommend the safest option; it depends on the person's utility function. In this case, a risk-neutral person would be fine with either, while a risk-averse person would take the sure $100. So the theory can match different preferences, not a single rule.
7. Compare expected utility theory with the idea that people simply choose the option with the highest average payoff.
Expected utility theory is similar to choosing the highest average payoff if we treat utility as equal to money. But the theory is more flexible: it allows that the value of money can change – for example, $100 might be worth more to a poor person than to a rich one. The theory says people maximize the expected value of utility, not money. In contrast, the simple average payoff rule ignores personal preferences and diminishing sensitivity. For instance, a gamble offering a 50% chance to win $1 million and 50% to lose $900,000 has an average gain of $50,000, but many would refuse it because the pain of loss is larger. Expected utility can capture this if the utility of losing is steep. So the theory is more realistic than just averaging dollar amounts.
8. Why is expected utility theory considered a 'normative' model?
Expected utility theory is called normative because it describes how people should make decisions to be rational, not necessarily how they actually decide. It assumes that people have clear, consistent preferences and always choose the option that gives the highest expected utility. In theory, this leads to the best long-term outcomes if the probabilities are known. However, real people often violate the theory due to emotions, biases, or limited thinking. For instance, someone might buy a lottery ticket even though its expected value is negative, which is irrational under the theory. So the theory sets a standard for rational choice, but it does not capture all real behavior. Behavioral economists use it as a starting point to understand deviations.
9. What is a practical implication for asset allocation when investors have prospect theory preferences instead of mean-variance preferences?
In practice, prospect theory implies that many investors do not follow the mean-variance efficient frontier. They may demand higher expected returns for assets that have a small chance of a big loss, like those with left-tail risk. This can create mispricing: assets with positive skew (lottery-like) become overpriced, and assets with negative skew (crash risk) become underpriced. Financial advisors, knowing this, might design portfolios that accommodate loss aversion, for example by using a 'safety-first' approach that protects against large losses relative to a reference point. Also, robo-advisors and asset-allocation models often incorporate behavioral insights, such as setting a floor for losses, to better match how real investors think.
10. What is uncertainty avoidance and how does it relate to availability bias?
Uncertainty avoidance is a cultural dimension describing how uncomfortable people feel with ambiguity. In high uncertainty avoidance cultures, people prefer clear rules and structure. This can make availability bias stronger because they rely on memorable, concrete examples to judge risks. For instance, in such a culture, after a rare plane crash, people may overestimate the danger of flying because the event is vivid and available. In low uncertainty avoidance cultures, people are more comfortable with uncertainty and may be less swayed by dramatic but rare events. The availability bias is the tendency to judge frequency by how easily examples come to mind. So culture influences which examples are available and how much they are trusted.
11. Compare loss aversion with the idea that people are simply 'risk-averse'.
Risk aversion and loss aversion are related but distinct. Risk aversion is about preferring a sure outcome over a risky one with the same expected value. For example, a risk-averse person picks $50 for sure over a 50% chance of $100. Loss aversion is about the different weight people put on losses versus gains. A loss-averse person might reject a 50-50 bet to win $110 or lose $100 because the loss of $100 feels more than the gain of $110. Risk aversion alone wouldn't necessarily cause that, because the expected value is positive ($5). Loss aversion can lead to risk-seeking behavior in some situations, like gambling to recover a loss. So loss aversion is a separate, more specific bias that explains many decisions that risk aversion cannot.
12. Give an example of the 'planning fallacy' – a form of optimism bias.
The planning fallacy is when people underestimate how long a task will take, even when they have experience with similar tasks. For example, a team might think they can build a software app in 3 months, but it ends up taking a year. This happens because they focus on the best-case scenario and ignore past delays. They also forget that unexpected problems always occur. The same bias affects personal projects like renovating a kitchen – people think it will take two weeks but it takes two months. The planning fallacy is a classic example of optimism bias because people are overly optimistic about their own timeline. It can be reduced by thinking about how long similar projects actually took, called 'reference class forecasting'.