Affective Forecasting

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hedonic treadmill

Affective forecasting (also known as the ‘hedonic forecasting mechanism’) is the prediction of one’s affect (emotional state) in the future. As a process that influences preferences, decisions, and behavior, affective forecasting is studied by both psychologists and economists, with broad applications.

Psychologist Daniel Kahneman and business school professor Jackie Snell began research on hedonic forecasts in the early 1990s, examining its impact on decision making. The term ‘affective forecasting’ was later coined by psychologists Timothy Wilson and Daniel Gilbert. Early research focused solely on measuring emotional forecasts, while subsequent studies examined accuracy, revealing that people are surprisingly poor judges of their future emotional states. For example, in predicting how events like winning the lottery might affect their happiness, people are likely to overestimate future positive feelings, ignoring the numerous other factors that might contribute to their emotional state outside of the single lottery event.

Some of the cognitive biases related to systematic errors in affective forecasts are focalism (or anchoring, relying too heavily on the first piece of information offered when making decisions), empathy gap (when one is angry, it is difficult to understand what it is like for one to be happy, and vice versa), and impact bias (tendency for people to overestimate the length or the intensity of future feeling states).

A perspective that overrides impact bias is ‘mindfulness,’ a skill that individuals can learn to help them prevent overestimating their feelings. Being mindful helps the individual understand that they may currently feel negative emotions, but the feelings are not permanent. The Five Factor Mindfulness Questionnaire (FFMQ) can be used to measure an individual’s mindfulness. The factors are observing, describing, acting with awareness, non-judging of inner experience, and non-reactivity to inner experience. The two most important factors for improving forecasts are observing and acting with awareness. The observing factor assesses how often an individual attends to their sensations, emotions, and outside environment. The ability to observe allows the individual to avoid focusing on one single event, and be aware that other experiences will influence their current emotions. Acting with awareness requires assessing how individuals tend to current activities with careful consideration and concentration. Being able to observe the current event can help individuals focus on pursuing future events that provide long-term satisfaction and fulfillment.

While affective forecasting has traditionally drawn the most attention from economists and psychologists, their findings have in turn generated interest from a variety of other fields, including happiness research, law, health care, and ethics. For example, the tendency to underestimate our ability to adapt to life-changing events has led to legal theorists questioning the assumptions behind tort damage compensation. Behavioral economists have incorporated discrepancies between forecasts and actual emotional outcomes into their models of different types of utility and welfare. This discrepancy also concerns healthcare analysts, in that many important health decisions depend upon patients’ perceptions of their future quality of life.

Affective forecasting can be divided into four components: predictions about emotional valence (i.e. positive or negative), the specific emotions experienced, their duration, and their intensity. While errors may occur in all four components, research overwhelmingly indicates that the two areas most prone to bias, usually in the form of overestimation, are duration and intensity. Immune neglect is a form of impact bias in response to negative events, in which people fail to predict how much their psychological immune system will hasten their recovery.

On average, people are fairly accurate about predicting which emotions they will feel in response to future events. However, some studies indicate that predicting specific emotions in response to more complex social events leads to greater inaccuracy. For example, one study found that while many women who imagine encountering gender harassment predict feelings of anger, in reality, a much higher proportion report feelings of fear. Other research suggests that accuracy in affective forecasting is greater for positive affect than negative affect, suggesting an overall tendency to overreact to perceived negative events. Gilbert and Wilson posit that this is a result of our psychological immune system.

Because forecasting errors commonly arise from literature on cognitive processes, many affective forecasting errors derive from and are often framed as cognitive biases, some of which are closely related or overlapping constructs. Errors may arise due to extrinsic factors, such as framing effects (presentation changes how people react to choices), or intrinsic ones, such as expectation effects; previously formed expectations can alter emotional responses to the event itself, motivating forecasters to confirm or debunk their initial forecasts. A self-fulfilling prophecy can lead to the perception that forecasters have made accurate predictions. Inaccurate forecasts can also become amplified by expectation effects. For example, a forecaster who expects a movie to be enjoyable will, upon finding it dull, like it significantly less than a forecaster who had no expectations.

Because accuracy is often measured as the discrepancy between a forecaster’s present prediction and the eventual outcome, researchers also study how time affects affective forecasting. For example, the tendency for people to represent distant events differently from close events is captured in the construal level theory, which describes the relation between psychological distance and the extent to which people’s thinking (e.g., about objects and events) is abstract or concrete. The finding that people are generally inaccurate affective forecasters has been most obviously incorporated into conceptualizations of happiness and its successful pursuit, as well as decision making across disciplines. Findings in affective forecasts have stimulated philosophical and ethical debates, for example, on how to define welfare. On an applied level, findings have informed various approaches to healthcare policy, tort law, consumer decision making, and measuring utility.

Major life events can have a huge impact on people’s emotions for a very long time but the intensity of that emotion tends to decrease with time, a phenomenon known as ’emotional evanescence.’ When making forecasts, forecasters often overlook this phenomenon. Psychologists have suggested that emotion does not decay over time predictably like radioactive isotopes but that the mediating factors are more complex. People have psychological processes that help dampen the emotion. Psychologists have proposed that surprising, unexpected, or unlikely events cause more intense emotional reaction. Research suggests that people are unhappy with randomness and chaos and that they automatically think of ways to make sense of an event when it is surprising or unexpected. This is related to immune neglect in that when these unwanted acts of randomness occur people become upset and try to find meaning or to cope with the event. The way that people try to make sense of the situation can be considered a coping strategy made by the body. This idea differs from immune neglect due to the fact that it is more of momentary idea. Immune neglect tries to cope with the event before it even happens.

One study documents how sense-making processes decrease emotional reaction. The study found that a small gift produced greater emotional reactions when it was not accompanied by a reason than when it was, arguably because the reason facilitated the sense-making process, dulling the emotional impact of the gift. Researchers have summarized that pleasant feelings are prolonged after a positive situation if people are uncertain about the situation. People fail to anticipate that they will make sense of events in a way that will diminish the intensity of the emotional reaction. This error is known as ‘ordinization neglect.’ For example, (‘I will be ecstatic for many years if my boss agrees to give me a raise’) an employee might believe, especially if the employee believes the probability of a raise was unlikely. Immediately after having the request approved, the employee may be thrilled but with time the employees make sense of the situation (e.g., ‘I am a very hard worker and my boss must have noticed this’) thus dampening the emotional reaction.

One study found that coping strategies vary between individuals and are influenced by their personalities. The study assumed that since people generally do not take their coping strategies into account when they predict future events, those with better coping strategies should have a bigger impact bias, or a greater difference between their predicted and actual outcome. For example, asking someone who is afraid of clowns how going to a circus would feel may result in an overestimation of fear because the anticipation of such fear causes the body to begin coping with the negative event.

Another study examined this further by recording college students’ emotions for football games. They found that students who generally coped with their emotions instead of avoiding them had a greater impact bias when predicting how they’d feel if their team lost the game. Those with better coping strategies recovered more quickly. Since the participants did not think about their coping strategies when making predictions, those who actually coped had a greater impact bias. Those who avoided their emotions, felt very closely to what they predicted they would. In other words students who were able to deal with their emotions were able to recover from their feelings. The students were unaware that their body was actually coping with the stress and this process made them feel better than not dealing with the stress.

A variant of immune neglect also proposed by Gilbert and Wilson is the ‘region-beta paradox,’ where recovery from more intense suffering is faster than recovery from less intense experiences because of the engagement of coping systems. This complicates forecasting, leading to errors. Contrarily, accurate affective forecasting can also promote the region-beta paradox. For example, a series of studies investigated the relationship between affective forecasting and the collapse of compassion phenomenon, which refers to the tendency for people’s compassion to decrease as the number of people in need of help increases. Researchers found that people who are skilled at regulating their emotions tended to experience less compassion in response to stories about eight children from Darfur compared to stories about only one child. These participants appeared to collapse their compassion by correctly forecasting their future affective states and proactively avoiding the increased negative emotions resulting from the story. This study suggests that in some cases accurate affective forecasting can actually promote unwanted outcomes such as the collapse of compassion phenomenon by way of the region-beta paradox.

Focalism (anchoring) occurs when people focus too much on certain details of an event, ignoring other factors. Research suggests that people have a tendency to exaggerate aspects of life when focusing their attention on it. A well-known example originates from a paper by Kahneman and David Schkade, who coined the term ‘focusing illusion’ in 1998. They found that although people tended to believe that someone from the Midwest would be more satisfied if they lived in California, results showed equal levels of life satisfaction in residents of both regions. In this case, concentrating on the easily observed difference in weather bore more weight in predicting satisfaction than other factors. There are many other factors that could have contributed to the desire to move to the Midwest but the focal point for their decisions was weather.

Various studies have attempted to ‘defocus’ participants, meaning instead of focusing on that one factor they tried to make the participants think of other factors or to look at the situation in a different lens. There were mixed results dependent upon methods used. One successful study asked people to imagine how happy a winner of the lottery and a recently diagnosed HIV patient would be. The researchers were able to reduce the amount of focalism by exposing participants to detailed and mundane descriptions of each person’s life, meaning that the more information the participants had on the lottery winner and the HIV patient the less they were able to only focus on few factors, these participants subsequently estimated similar levels of happiness for the HIV patient as well as the lottery-winner. As for the control participants, they made unrealistically disparate predictions of happiness. This could be due to the fact that the more information that is available the less likely it is one will able to ignore contributory factors.

Time discounting (or time preference) is the tendency to weigh present events over future events. Immediate gratification is more preferable than delayed gratification, especially over longer periods of time and with younger children or adolescents. For example a child may prefer one piece of candy now instead of five pieces of candy in four months. The longer the duration of time, the more people tend to forget about the future effects. Drawing again from the candy example, even though five pieces of candy are more gratifying than one, the fact that it will take four months to receive such gratification may cause a child to overlook the fact that he/she will be much more satisfied in the future. It is expected that affective reactions to an event will be less intense in the future than in the present. This pattern is sometimes referred to as ‘hyperbolic discounting’ or ‘present bias’ because people’s judgements are bias toward present events.

‘Future anhedonia’ is an affective forecasting error which occurs when a person believes that they will experience less intense affects of an event that will occur in the future, than if the same event occurred in the present, meaning that if a bad thing happens right now it would be considered much worse than if it happens next year. Forecasts of the duration of feelings often capture the tendency for emotions to fade over time, but underestimate the speed in which this happens. For example, in a study participants were asked to predict their happiness after receiving $20. They rated how happy they would feel if they were to receive $20 tomorrow or if they were to receive it in a year. They found that participants predicted that they would be happier receiving $20 sooner, compared to receiving it in the future. Applied to affective forecasting, this helps explain why people underestimate the intensity of future events. They consider how they are feeling now much more than how they would feel in the future. Economists often cite time discounting as a source of mispredictions of future utility.

Affective forecasters often rely on memories of past events. When people report memories for past events they may leave out important details, change things that occurred, and even add things that have not happened. This suggests the mind constructs memories based on what actually happened, and other factors including the person’s knowledge, experiences, and existing schemas. Using highly available, but unrepresentative past memories, increases the impact bias. Baseball fans, for example, tend to use the best game they can remember as the basis for their affective forecast of the game they are about to see. Commuters are similarly likely to base their forecasts of how unpleasant it would feel to miss a train on their memory of the worst time they missed the train.

Various studies indicate that retroactive assessments of past experiences are prone to various errors, such as duration neglect or decay bias. People tend to overemphasize the peaks and ends of their experiences when assessing them (peak/end bias), instead of analyzing the event as a whole. For example, in recalling painful experiences, people place greater emphasis on the most discomforting moments as well as the end of the event, as opposed to taking into account the overall duration. Retroactive reports often conflict with present-moment reports of events, further pointing to contradictions between the actual emotions experienced during an event and the memory of them. In addition to producing errors in forecasts about the future, this discrepancy has incited economists to redefine different types of utility and happiness.

Another problem that can arise with affective forecasting is that people tend to misremember their past predictions, often reporting they thought their predictions were the same as their actual emotions. Because of this, people do not realize that they made a mistake in their predictions, and will then continue to misforecast similar situations in the future.

When predicting future emotional states people must first construct a good representation of the event. If people have a lot of experience with the event then they can easily picture the event. For example, if people were asked how they would feel if they lost one hundred dollars in a bet, gamblers are more likely to easily construct an accurate representation of the event. Construal level theory’ argues that distant events are conceptualized more abstractly than immediate ones. Thus, psychologists suggest that a lack of concrete details prompts forecasters to rely on more general or idealized representations of events, which subsequently leads to simplistic and inaccurate predictions. For example, when asked to imagine what a ‘good day’ would be like for them in the near future, people often describe both positive and negative events. When asked to imagine what a ‘good day’ would be like for them in a year, however, people resort to more uniformly positive descriptions. Framing effects, environmental context, and heuristics (such as schemas) can all affect how a forecaster conceptualizes a future event. For example, the way options are framed affects how they are represented: when asked to forecast future levels of happiness based on pictures of dorms they may be assigned to, college students use physical features of the actual buildings to predict their emotions. In this case, the framing of options highlighted visual aspects of future outcomes, which overshadowed more relevant factors to happiness, such as having a friendly roommate.

Projection bias is the tendency to falsely project current preferences onto a future event. When people are trying to estimate their emotional state in the future they attempt to give an unbiased estimate. However, their assessments are contaminated by the current emotional state and thus it may be difficult for them to predict their emotional state in the future, an occurrence known as ‘mental contamination.’ For example, if a college student was currently in a negative mood because he just found out he failed a test, and if the college student forecasted how much he would enjoy a party two weeks later, his current negative mood may influence his forecast. In order to make an accurate forecast the student would need to be aware that his forecast is biased due to mental contamination, be motivated to correct the bias, and be able to correct the bias in the right direction and magnitude.

Projection bias can arise from empathy gaps (or hot/cold empathy gaps), which occur when the present and future phases of affective forecasting are characterized by different states of physiological arousal, which the forecaster fails to take into account. For example, forecasters in a state of hunger are likely to overestimate how much they will want to eat later, overlooking the effect of their hunger on future preferences. As with projection bias, economists use the visceral motivations that produce empathy gaps to help explain impulsive or self-destructive behaviors, such as smoking.

Economic research in affective forecasting errors complicate conventional interpretations of utility maximization, which presuppose that to make rational decisions, people must be able to make accurate forecasts about future experiences or utility. Whereas economics formerly focused largely on utility in terms of a person’s preferences (decision utility), the realization that forecasts are often inaccurate suggests that measuring preferences at a time of choice may be an incomplete concept of utility. Whereas a current forecast reflects expected or predicted utility, the actual outcome of the event reflects experienced utility. Predicted utility is the ‘weighted average of all possible outcomes under certain circumstances.’ Experienced utility refers to the perceptions of pleasure and pain associated with an outcome. Take, for example, the case of the ‘hungry shopper,’ in which the shopper takes pleasure in the purchase of food due to their current state of hunger. The usefulness of such purchasing is based upon their current experience and their anticipated pleasure in fulfilling their hunger.

Research in affective forecasts and economic decision making include investigations of durability bias in consumers and predictions of public transit satisfaction. In relevance to the durability bias in consumers, a study was conducted that showed that people make decisions regarding the consumption of goods based on the predicted pleasure, and the duration of that pleasure, that the goods will bring them. Overestimation of such pleasure, and its duration, increases the likelihood that the good will be consumed. Knowledge on such an effect can aid in the formation of marketing strategies of consumer goods. Studies regarding the predictions of public transit satisfaction reveal the same bias. However with a negative impact on consumption, due to their lack of experience with public transportation, car users predict that they will receive less satisfaction with the use of public transportation than they actually experience. This can lead them to refrain from the use of such services, due to inaccurate forecasting.

Broadly, the tendencies people have to make biased forecasts deviate from rational models of decision making. Rational models of decision making presume an absence of bias, in favor of making comparisons based on all relevant and available information. Affective forecasting may cause consumers to rely on the feelings associated with consumption rather than the utility of the good itself. One application of affective forecasting research is in economic policy. Knowledge that forecasts, and therefore, decisions, are affected by biases as well as other factors (such as framing effects), can be used to design policies that maximize the utility of people’s choices.[53] This approach is not without its critics, however, as it can also be seen to justify economic paternalism.

Prospect theory describes how people make decisions. It differs from expected utility theory in that it takes into account the relativity of how people view utility and incorporates loss aversion, or the tendency to react more strongly to losses rather than gains. Some researchers suggest that loss aversion is in itself an affective forecasting error, since people often overestimate the impact of future losses.

Economic definitions of happiness are tied to concepts of welfare and utility, and researchers are often interested in how to increase levels of happiness in the population. The economy has a major influence on the aid that is provided through welfare programs because it provides funding for such programs. Many welfare programs are focused on providing assistance with the attainment of basic necessities such as food and shelter. This is may be due to the fact that happiness and well-being is best derived from personal perceptions of one’s ability to provide these necessities. This statement is supported by research showing that after basic needs have been met, income has less of an impact on perceptions of happiness. Additionally, the availability of such welfare programs can enable those that are less fortunate to have additional discretionary income. Discretionary income can be dedicated to enjoyable experiences, such as family outings, and in turn, provides an additional dimension to their feelings and experience of happiness.

Affective forecasting provides a unique challenge to answering the question regarding the best method for increasing levels of happiness, and economists are split between offering more choices to maximize happiness, versus offering experiences that contain more objective or experienced utility. Experienced utility refers to how useful an experience is in its contribution to feelings of happiness and well-being. Experienced utility can refer to both material purchases and experiential purchases. Studies show that experiential purchases, such as a bag of chips, result in forecasts of higher levels of happiness than material purchases, such as the purchase of a pen. This prediction of happiness as a result of a purchase experience exemplifies affective forecasting. It is possible that an increase in choices, or means, of achieving desired levels of happiness will be predictive of increased levels of happiness. For example, if one is happy with their ability to provide themselves with both a choice of necessities and a choice of enjoyable experiences they are more likely to predict that they will be more happy than if they were forced to choose between one or the other. Also, when people are able to reference multiple experiences that contribute to their feelings of happiness, more opportunities for comparison will lead to a forecast of more happiness. Under these circumstances, both the quantity of choices and the quantity of experienced utility have the same effect on affective forecasting, which makes it difficult to chose a side of the debate on which method is most effective in maximizing happiness.

Applying findings from affective forecasting research to happiness also raises methodological issues: should happiness measure the outcome of an experience, or the satisfaction experienced as result of the choice made based upon a forecast? For example, although professors may forecast that getting tenure would significantly increase their happiness, research suggests that in reality, happiness levels between professors who are or are not awarded tenure are insignificant. In this case happiness is measured in terms of the outcome of an experience. Affective forecasting conflicts such as this one have also influenced theories of hedonic adaptation, which compares happiness to a treadmill, in that it remains relatively stable despite our forecasts.

Affective forecasting also has implications in health decision making and medical ethics and policy. Research in health-related affective forecasting suggests that nonpatients consistently underestimate the quality of life associated with chronic health conditions and disability. The so-called ‘disability paradox’ states the discrepancy between self-reported levels of happiness amongst chronically ill people versus the predictions of their happiness levels by healthy people. The implications of this forecasting error in medical decision making can be severe, because judgments about future quality of life often inform health decisions. Inaccurate forecasts can lead patients, or more commonly their health care agent, to refuse life-saving treatment in cases when the treatment would involve a drastic change in lifestyle, for example, the amputation of a leg. A patient, or health care agent, who falls victim to focalism would fail to take into account all the aspects of life that would remain the same after losing a limb.

Research also indicates that affective forecasts about future quality of life are influenced by the forecaster’s current state of health. Whereas healthy individuals associate future low health with low quality of life, less healthy individuals do not forecast necessarily low quality of life when imagining having poorer health. Thus, patient forecasts and preferences about their own quality of life may conflict with public notions. Because a primary goal of healthcare is maximizing quality of life, knowledge about patients’ forecasts can potentially inform policy on how resources are allocated.

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