Glossary
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Loss Aversion
Higher likelihood of selecting a choice option that avoids a loss of the same magnitude as an alternative that promises a gain. Often called the “losses loom larger than gains” phenomenon first reported by Kahneman and Tversky and used to explain the Endowment Effect.
Loss aversion is an important concept associated with prospect theory and is encapsulated in the expression “losses loom larger than gains” (Kahneman & Tversky, 1979). It is thought that the pain of losing is psychologically about twice as powerful as the pleasure of gaining. People are more willing to take risks (or behave dishonestly; e.g. Schindler & Pfattheicher, 2016) to avoid a loss than to make a gain. Loss aversion has been used to explain the endowment effect and sunk cost fallacy, and it may also play a role in the status quo bias.
The basic principle of loss aversion can explain why penalty frames are sometimes more effective than reward frames in motivating people (Gächter et al., 2009) and has been applied in behavior change strategies. The website Stickk, for example, allows people to commit to a positive behavior change (e.g. give up junk food), which may be coupled the fear of loss—a cash penalty in the case of non-compliance.
Mental Accounting
Finding that the value of money differs depending on its origin and intended use, contrary to the concept of Fungibility, which states the opposite.
Mental accounting is a concept associated with the work of Richard Thaler (see Thaler, 2015, for a summary). According to Thaler, people think of value in relative rather than absolute terms. They derive pleasure not just from an object’s value, but also the quality of the deal – its transaction utility (Thaler, 1985). In addition, humans often fail to fully consider opportunity costs (tradeoffs) and are susceptible to the sunk cost fallacy.
Why are people willing to spend more when they pay with a credit card than cash (Prelec & Simester, 2001)? According to the theory of mental accounting, people treat money differently, depending on factors such as the money’s origin and intended use, rather than thinking of it in terms of the “bottom line” as in formal accounting (Thaler, 1999). An important term underlying the theory is fungibility, the fact that all money is interchangeable and has no labels. In mental accounting, people treat assets as less fungible than they really are.
Consumers’ tendency to work with mental accounts is reflected in various domains of applied behavioral science, especially in the financial services industry. Examples include banks offering multiple accounts with savings goal labels, which make mental accounting more explicit, as well as third-party services that provide consumers with aggregate financial information across different financial institutions (Zhang & Sussman, 2018).
Myopic Loss Aversion
Myopic loss aversion occurs when investors take a view of their investments that is strongly focused on the short term, leading them to react too negatively to recent losses, which may be at the expense of long-term benefits (Thaler et al., 1997). This phenomenon is influenced by narrow framing, which is the result of investors considering specific investments (e.g. an individual stock or a trade) without taking into account the bigger picture (e.g. a portfolio as a whole or a sequence of trades over time) (Kahneman & Lovallo, 1993). A large-scale field experiment has shown that individuals who receive information about investment performance too frequently tend to underinvest in riskier assets, losing out on the potential for better long-term gains (Larson et al., 2016).
Naive Allocation
Decision researchers have found that people prefer to spread limited resources evenly across a set of possibilities. This can be referred as naive allocation. For example, consumers may invest equal amounts of money across different investment options. Similarly, the diversification bias shows that consumers like to spread out consumption choices across a variety of goods. Research suggests that choice architects can work with these tendencies due to decision makers’ partition dependence. For example, separating healthy food menu options into different menu categories (e.g., “fruits”, “vegetables”) and combining unhealthy options into one single menu category (e.g., “candies and cookies”), one can steer consumers to choose a more healthy options and fewer unhealthy options (Johnson et al., 2012).
Nudge
According to Thaler and Sunstein (2008, p. 6), a nudge is: any aspect of the choice architecture that alters people’s behavior in a predictable way without forbidding any options or significantly changing their economic incentives. To count as a mere nudge, the intervention must be easy and cheap to avoid. Nudges are not mandates. Putting the fruit at eye level counts as a nudge. Banning junk food does not.
Perhaps the most frequently mentioned nudge is the setting of defaults, which are pre-set courses of action that take effect if nothing is specified by the decision-maker. This type of nudge, which works with a human tendency for inaction, appears to be particularly successful, as people may stick with a choice for many years (Gill, 2018).
Questions about the theoretical and practical value of nudging have been explored (Kosters & Van der Heijden, 2015) with respect to their ability to produce lasting behavior change (Frey & Rogers, 2014), as well as their assumptions of irrationality and lack of agency (Gigerenzer, 2015). There may also be limits to nudging due to non-cognitive constraints and population differences, such as a lack of financial resources if nudges are designed to increase savings (Loibl et al., 2016). Limits in the application of nudges speak to the value of experimentation in order to test behavioral interventions prior to their implementation.
Optimism Bias/Effect
Finding that choice options with positive, higher utility outcomes will be perceived as more likely to occur than those with negative consequences.
People tend to overestimate the probability of positive events and underestimate the probability of negative events happening to them in the future (Sharot, 2011). For example, we may underestimate our risk of getting cancer and overestimate our future success on the job market. A number of factors can explain unrealistic optimism, including perceived control and being in a good mood (Helweg-Larsen & Shepperd, 2001). (See also overconfidence.)
Overconfidence Bias/Effect
Finding that an individual’s subjective assessment of their performance exceeds their objective performance.
The overconfidence effect is observed when people’s subjective confidence in their own ability is greater than their objective (actual) performance (Pallier et al., 2002). It is frequently measured by having experimental participants answer general knowledge test questions. They are then asked to rate how confident they are in their answers on a scale. Overconfidence is measured by calculating the score for a person’s average confidence rating relative to the actual proportion of questions answered correctly. (See also optimism bias.)
A big range of issues have been attributed to overconfidence more generally, including the high rates of entrepreneurs who enter a market despite the low chances of success (Moore & Healy, 2008). Among investors, overconfidence has been associated with excessive risk-taking (e.g. Hirshleifer & Luo, 2001), concentrated portfolios (e.g. Odean, 1998) and overtrading (e.g. Grinblatt & Keloharju, 2009).
The planning fallacy is another example of overconfidence, where people underestimate the length of time it will take them to complete a task, often ignoring past experience (Buehler et al., 1994).
Pain of Paying
People don't like to spend money. We experience pain of paying (Zellermayer, 1996), because we are loss averse. The pain of paying plays an important role in consumer self-regulation to keep spending in check (Prelec & Loewenstein, 1998). This pain is thought to be reduced in credit card purchases, because plastic is less tangible than cash, the depletion of resources (money) is less visible and payment is deferred. Different types of people experience different levels of pain of paying, which can affect spending decisions. Tightwads, for instance, experience more of this pain than spendthrifts. As a result, tightwads are particularly sensitive to marketing contexts that make spending less painful (Rick, 2018). (See also mental accounting.)
Partitioning
The rate of consumption can be decreased by physically partitioning resources into smaller units, for example cookies wrapped individually or money divided into several envelopes. When a resource is divided into smaller units (e.g. several packs of chips), consumers encounter additional decision points—a psychological hurdle encouraging them to stop and think. In addition to the cost incurred when resources are used, opening a partitioned pool of resources incurs a psychological transgression cost, such as feelings of guilt (Cheema & Soman, 2008). Related research has found that separate mental payment accounts (i.e. envelopes with money) can disrupt a shopping momentum effect that may occur after an initial purchase (Dhar, Huber, & Khan, 2007). (For related ideas, see also mental accounting).
Peak-end Rule
Refers to findings that the pleasantness or unpleasantness of past experiences are more related to peaks, valleys and the ends of experiences.
According to the peak-end rule, our memory of past experience (pleasant or unpleasant) does not correspond to an average level of positive or negative feelings but to the most extreme point and the end of the episode (Kahneman, 2000b). The rule developed from the finding that evaluations of a past episode seem to be determined by a weighted average of ‘snapshots’ of an experience, such as moments in a film, thus neglecting its actual duration (Fredrickson & Kahneman, 1993), as well research showing that people would prefer to repeat a painful experience if it is followed by a slightly less painful one (Kahneman et al., 1993). In terms of memories, remembered utility is more important than total utility (Kahneman, 2000a). People’s memories of prototypical moments are related to the judgments made when people apply a representativeness heuristic (Kahneman, 2000b).