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Anchoring effect
Anchoring effect
Anchoring effect
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The anchoring effect is a psychological phenomenon in which an individual's judgments or decisions are influenced by a reference point or "anchor" which can be completely irrelevant. Both numeric and non-numeric anchoring have been reported through research. In numeric anchoring, once the value of the anchor is set, subsequent arguments, estimates, etc. made by an individual may change from what they would have otherwise been without the anchor. For example, an individual may be more likely to purchase a car if it is placed alongside a more expensive model (the anchor). Prices discussed in negotiations that are lower than the anchor may seem reasonable, perhaps even cheap to the buyer, even if said prices are still relatively higher than the actual market value of the car.[1] Another example may be when estimating the orbit of Mars, one might start with the Earth's orbit (365 days) and then adjust upward until they reach a value that seems reasonable (usually less than 687 days, the correct answer).

The original description of the anchoring effect came from psychophysics. When judging stimuli along a continuum, it was noticed that the first and last stimuli were used to compare the other stimuli (this is also referred to as "end anchoring"). This was applied to attitudes by Muzafer Sherif et al. in their 1958 article "Assimilation and Contrast Effects of Anchoring Stimuli on Judgments".[2]

Experimental findings

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Daniel Kahneman, one of the first researchers to study anchoring

The anchoring and adjustment heuristic was first theorized by Amos Tversky and Daniel Kahneman.[3] In one of their first studies, participants were separated into one of two conditions, and either asked to compute, within 5 seconds, the product of the numbers one through to eight, either as 1 × 2 × 3 × 4 × 5 × 6 × 7 × 8 or reversed as 8 × 7 × 6 × 5 × 4 × 3 × 2 × 1. Because participants did not have enough time to calculate the full answer, they had to make an estimate after their first few multiplications. When these first multiplications gave a small answer – because the sequence started with small numbers – the median estimate was 512; when the sequence started with the larger numbers, the median estimate was 2,250. (The correct answer is 40,320.) In another study by Tversky and Kahneman, participants were asked to estimate the percentage of African countries in the United Nations. Before estimating, the participants first observed a roulette wheel that was predetermined to stop on either 10 or 65. Participants whose wheel stopped on 10 guessed lower values (25% on average) than participants whose wheel stopped at 65 (45% on average).[3] The pattern has held in other experiments for a wide variety of different subjects of estimation.

As a second example, in a study by Dan Ariely, an audience is first asked to write the last two digits of their social security number and consider whether they would pay this number of dollars for items whose value they did not know, such as wine, chocolate and computer equipment. They were then asked to bid for these items, with the result that the audience members with higher two-digit numbers would submit bids that were between 60 percent and 120 percent higher than those with the lower social security numbers, which had become their anchor.[4] When asked if they believed the number was informative of the value of the item, quite a few said yes.[5] Trying to avoid this confusion, a small number of studies used procedures that were clearly random, such as Excel random generator button[6] and die roll,[7] and failed to replicate anchoring effects.

The anchoring effect was also found to be present in a study[8] in the Journal of Real Estate Research in relation to house prices. In this investigation, it was established that the 2-year and 9-year highs on the Case-Shiller House Price Index could be used as anchors in predicting current house prices. The findings were used to indicate that, in forecasting house prices, these 2-year and 9-years highs might be relevant.

The anchoring effect was also found to be present in a study in the Journal of Behavioral Finance in relation to stock purchase behavior.[9] The study found that when using an app-based stock brokerage, an investor’s first stock purchase price serves as an anchor for future stock purchases. The findings indicate that when investors start by making only a small stock purchase, they end up with less accumulated investments in the long run.

Characteristics

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Difficulty of avoiding

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Various studies have shown that anchoring is very difficult to avoid. For example, in one study students were given anchors that were wrong. They were asked whether Mahatma Gandhi died before or after age 9, or before or after age 140. Clearly neither of these anchors can be correct, but when the two groups were asked to suggest when they thought he had died, they guessed significantly differently (average age of 50 vs. average age of 67).[10]

Other studies have tried to eliminate anchoring much more directly. In a study exploring the causes and properties of anchoring, participants were exposed to an anchor and asked to guess how many physicians were listed in the local phone book. In addition, they were explicitly informed that anchoring would "contaminate" their responses, and that they should do their best to correct for that. A control group received no anchor and no explanation. Regardless of how they were informed and whether they were informed correctly, all of the experimental groups reported higher estimates than the control group. Thus, despite being expressly aware of the anchoring effect, most participants were still unable to avoid it.[11] A later study found that even when offered monetary incentives, most people are unable to effectively adjust from an anchor.[12]

Although it has been found through many research and experiments that attempt to mitigate the decision heuristic of anchoring bias is either marginally significant or not successful at all, it can be found that the consider-the-opposite (COS strategy) has been the most reliable in mitigating the anchoring bias (Adame, 2016). In short, the COS strategy is proposed to an individual by asking them to consider the possibilities the opposite of their perceptions and beliefs. Therefore, depriving the individual of their preexisting attitudes and limiting the decision bias. [13]

Durability of anchoring

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Anchoring effects are also shown to remain adequately present given the accessibility of knowledge pertaining to the target. This, in turn, suggests that despite a delay in judgement towards a target, the extent of anchoring effects have seen to remain unmitigated within a given time period. A series of three experiments were conducted to test the longevity of anchoring effects. It was observed that despite a delay of one week being introduced for half the sample population of each experiment, similar results of immediate judgement and delayed judgement of the target were achieved. The experiments concluded that external information experienced within the delayed judgement period shows little influence relative to self-generated anchors even with commonly encountered targets (temperature) used in one of the experiments, showing that anchoring effects may precede priming in duration especially when the anchoring effects were formed during the task.[14] Further research to conclude an effect that is effectively retained over a substantial period of time has proven inconsistent.[15][16][17]

Pervasiveness across contexts

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One notable characteristic of the anchoring effect is its pervasiveness across diverse judgment scenarios. Furnham and Boo (2011) highlight that anchoring occurs not only in abstract estimation tasks (like guessing the height of Mount Everest) but also in real-world contexts such as legal sentencing, consumer purchasing, salary negotiations, and forecasting. Anchoring persists even when the anchor is implausible or clearly irrelevant (e.g., spinning a random wheel), demonstrating that anchoring can operate automatically, outside of conscious awareness or logical evaluation.[18]

Anchoring bias in groups

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It is often presumed that groups come to a more unbiased decision relative to individuals.[19] However, this assumption is supported with varied findings that could not come to a general consensus.[20][21][22][23][24][25][26] Nevertheless, while some groups are able to perform better than an individual member, they are found to be just as biased or even more biased relative to their individual counterparts.[21][22] A possible cause would be the discriminatory fashion in which information is communicated, processed and aggregated based on each individual's anchored knowledge and belief.[27][28][29][30] This results in a diminished quality in the decision-making process and consequently, amplifies the pre-existing anchored biases.

The cause of group anchoring remains unsure. Group anchors may have been established at the group level or may simply be the culmination of several individual's personal anchors. Prior studies have shown that when given an anchor before the experiment, individual members consolidated the respective anchors to attain a decision in the direction of the anchor placed.[31] However, a distinction between individual and group-based anchor biases does exist, with groups tending to ignore or disregard external information due to the confidence in the joint decision-making process.[32][33][34] The presence of pre-anchor preferences also impeded the extent to which external anchors affected the group decision, as groups tend to allocate more weight to self-generated anchors, according to the 'competing anchor hypothesis'.[35][31]

Recently, it has been suggested that the group member who speaks first often has an unproportionally high impact on the final decision.[36][37]

A series of experiments were conducted to investigate anchoring bias in groups and possible solutions to avoid or mitigate anchoring. The first experiment established that groups are indeed influenced by anchors while the other two experiments highlighted methods to overcome group anchoring bias. Methods that were utilized include the use of process accountability[38][39] and motivation through competition instead of cooperation[40] to reduce the influence of anchors within groups.

Business intelligence

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A peer-reviewed study[41] sought to investigate the effect of business intelligence (BI) systems on the anchoring effect. Business intelligence denotes an array of software and services used by businesses to gather valuable insights into an organisation's performance.[42] The extent to which cognitive bias is mitigated by using such systems was the overarching question in this study. While the independent variable was the use of the BI system, the dependent variable was the outcome of the decision-making process. The subjects were presented with a 'plausible' anchor and a 'spurious' anchor in a forecasting decision. It was found that, while the BI system mitigated the negative effects of the spurious anchor, it had no influence on the effects of the plausible anchor. This is important in a business context, because it shows that humans are still susceptible to cognitive biases, even when using sophisticated technological systems. One of the subsequent recommendations from the experimenters was to implement a forewarning into BI systems as to the anchoring effect.

Causes

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The present literature does not reach a consensus as to the cause of anchoring.[43] However, scholars agree that anchoring is a phenomenon that can be easily demonstrated but is hard to explain.[44] Some scholars suggest that anchoring is, in fact, caused by a combination of factors.

Anchoring-and-adjusting

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The most prevalent explanation of the anchoring effect is the argument originally made by Tversky and Kahneman, termed anchoring-as-adjusting.[45] Based on this theory, individuals set an anchor according to available information, whether it is provided or individuals already have an anchor in mind, and use this anchor as a point of reference to adjust their answers. This theory explains inaccuracy in guessing by suggesting that people adjust insufficiently, rendering their final guess closer to the anchors.[46] This phenomenon was further investigated in other studies and used as an explanation for biases such as hindsight bias and egocentric bias.[47]

For instance, when asked to guess the price of a drink in a coffee shop, individuals often look for the price of other drinks or recall the price of similar drinks at other stores, and base their answer on the anchor that they set.[48] Specifically, the original study found that by simply providing an arbitrary anchor to each group, people provided significantly different responses, suggesting that their answers were generated through anchoring and adjusting. In addition, this finding suggests that individuals are gullible when setting an anchor, and almost automatically begin the anchoring-and-adjusting process.

On the other hand, Epley and Gilovich found that when anchors are self-generated, people will stop adjusting once they believe they have adjusted their answers to an acceptable range. Although this process does not guarantee insufficient adjustments, it does result in an answer that is as close to the anchor as possible in the acceptable range. However, insufficient adjustments are diminished when individuals are able and motivated by external factors, such as monetary compensation, to continue adjusting for a more accurate answer, thereby reducing the anchoring effect.[49] The anchoring effect is also reduced when individuals know which way to adjust from the anchor because it eliminates the possible answers by half.[50]

Besides general knowledge, anchoring is also observed in social settings. The simulation theory of empathy suggests that people use their own mental state and reasoning to infer the actions of others. People assume that those who are similar to us will act in a similar way. Aligned with this idea, Tamir and Mitchell found that judgments of the attitude of others are made more quickly for those who are similar to the judge, and greater self-other discrepancy resulted in longer reaction time.[51] In this case, individuals use their own attitudes as an anchor and make adjustments to predict the attitude of others. Note that this process only takes place when the judge perceives great similarity between self and others.

Overall, this theory describes the process of anchoring and adjusting away from the anchor, as well as the phenomenon of inaccurate responses due to insufficient adjusting. However, proponents of alternative theories argued that adjusting is only possible when the original anchor lies outside the acceptable range. According to Epley and Gilovich, individuals will not adjust at all and give an answer that is identical to their anchor if the anchor is already within the acceptable range.[49] Using the previous example, the actual price of the drink can be identical to other drinks served at that store, meaning that people should theoretically consider their anchor as a potential answer rather than adjusting. When a reasonable anchor is given, there will be no adjustment. Therefore, this theory does not explain all cases of anchoring.

Selective accessibility

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An alternative explanation of the anchoring effect is the idea that the accessibility of anchor-consistent information is enhanced when individuals consider it as a potential answer. After determining that the initial anchor is not the correct answer, they move on to consider other possibilities. However, because the anchor was just made salient and accessible to them, they will take the anchor into consideration while evaluating other possibilities. As a result, this comparative assessment can result in answers that are disproportionally consistent with the anchor.[52] In this sense, anchoring is a special case of semantic priming, where the anchor acts as the prime.[44]

Unlike anchoring-and-adjusting, this theory suggests that people consider the relevant attributes of the initial anchor to determine if the anchor is plausible.[44] Rather than accepting any plausible anchor as their answer and insufficiently adjusting anchors outside of the acceptable range, people will remain motivated to find a more accurate answer after rejecting the initial anchor.[44] The preceding adjustments that individuals make are relevant to the anchor because people evaluate hypotheses through attempting to confirm them.[53] In line with this idea, when investigating whether the plausibility of an anchor affects comparative and absolute judgments, Strack and Mussweiler found that individuals take longer to provide an absolute judgment when the anchor is implausible.[44] Comparative judgments refer to the process of using an anchor to determine the final answer. Most paradigms used in anchoring studies also ask participants to provide an absolute judgment (i.e., provide a concrete number as their answer) after comparative judgments. When plausible anchors were used in comparative judgments, the anchor became more accessible, shortening the response time for consequent absolute judgments. In contrast, implausible anchors result in longer response time because there is no relevant information that can be used as primes.

Although selective accessibility and anchoring-and-adjusting provide conflicting explanations for the anchoring effect, some scholars have argued that anchoring is influenced by multiple factors, and these theories complement each other in explaining the anchoring effect. For instance, Simmons and colleagues proposed an integrative theory suggesting that an anchor can result in selective reliance on anchor-consistent information, rendering the range of plausible answers closer to the anchor. On the other hand, people adjust away from (or possibly back toward) anchors before settling on their final estimate. This integrative theory is more parsimonious because it suggests that neither the source of the anchor (self-generated or provided) nor the plausibility of the anchor has a significant effect on judgments.[50]

Attitude change

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On the contrary of the two previous theories, the attitude change view suggests that individuals' attitude towards an anchor, specifically provided anchors, can heavily affect the extent of the anchoring effect. Recall that individuals often seek to confirm their hypothesis rather than objectively evaluating all information.[53] When individuals disagree with the provided anchor, they will selectively seek evidence that supports their own attitudes instead of the provided anchor, resulting in an unobserved anchoring effect.[54] This theory highlights the idea that the distinct anchoring effect observed with self-generated and provided anchors roots from individual attitude (i.e., does the individual believe in the anchor) rather than the anchors themselves, supporting the integrative theory proposed by Simmons and colleagues.[50] Furthermore, supporters of this view have argued that attitude change is an alternative explanation of the anchoring effect. Providing an anchor generates favorable attitudes in individuals toward the anchor, biasing consequent judgments.[55]

Extremeness aversion

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Extremeness aversion is a robust phenomenon where people try to avoid the extremes during decision-making, such as selecting the middle options more often than other extreme options and avoiding reporting the maximum or minimum on a Likert scale.[56] This desire to avoid extremes is at least partially responsible for the anchoring effect. For instance, upon setting an anchor, participants who were told they could adjust up to 6 units made significantly smaller adjustments compared to those who were told that they could adjust up to 15 units, suggesting that people avoid extremes when making decisions.[57] Importantly, the maximum allowable adjustment acted as an anchor that affected the final judgment, highlighting the prevalent aversion to extremes. As a result, the final judgment is close to the anchor because people do not want to adjust too close to the extremes.

Influencing factors

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Mood

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A wide range of research has linked sad or depressed moods with more extensive and accurate evaluation of problems.[58] As a result of this, earlier studies hypothesized that people with more depressed moods would tend to use anchoring less than those with happier moods. However, more recent studies have shown the opposite effect: sad people are more likely to use anchoring than people with happy or neutral mood.[59] In a study focusing on medical practitioners, it was found that physicians that possess positive moods are less susceptible to anchoring bias, when compared to physicians with neutral moods. This was specifically found to be because a positive mood leads to information processing that is more systematic which leads to more efficient problem solving. This leads to a decreased anchoring effect.[60]

Personal experience

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Early research found that experts (those with high knowledge, experience, or expertise in some field) were more resistant to the anchoring effect. However, anchoring happens unconsciously which means that unless someone who is knowledgeable is warned prior, they are still susceptible to anchoring.[11] Since then, however, numerous studies have demonstrated that while experience can sometimes reduce the effect, even experts are susceptible to anchoring. In a study concerning the effects of anchoring on judicial decisions, researchers found that even experienced legal professionals were affected by anchoring. This remained true even when the anchors provided were arbitrary and unrelated to the case in question.[61] Also, this relates to goal setting, where more experienced individuals will set goals based on their past experiences which consequently affects end results in negotiations.[62]

Expertise is when a judge has relevant knowledge. In a study using price estimation of cars, it was found that relevant knowledge positively influenced anchoring. Expertise in cognitive bias is related to experience however the two are not exclusively exhaustive. In a study using stock return estimates, it was found that expertise decreases behavioural bias significantly. It was found that other factors like cognitive ability and experience where there is no susceptibility to anchoring or a susceptibility as it increases, tend to become factors that decrease the effects of anchoring when they are an expert.[63]

Personality

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The Big Five personality traits

Correlational research on anchoring bias and personality traits yielded mixed results, with emphasis on the Big Five personality traits which includes: Conscientiousness (orderly and responsible), neuroticism (uneasy and anxious), extraversion (sociable and outgoing), openness (intelligence and creativity) and agreeableness (polite and trusting). One study found that participants who were high in the Openness trait were more influenced by anchors set by others when estimating the length of the Mississippi river, with no other personality traits correlating to the effects of anchoring.[64]However, other research showed that it was conscientiousness and agreeableness that increased anchoring biases, while anchoring effects were diminished in participants high in the extraversion trait. [65][66]

Cognitive ability

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The impact of cognitive ability on anchoring is contested. A recent study on willingness to pay for consumer goods found that anchoring decreased in those with greater cognitive ability, though it did not disappear.[67] Another study, however, found that cognitive ability had no significant effect on how likely people were to use anchoring.[68] In a poker-like experiment that included people of differing academic achievement and psychometric reasoning scoring, it has been found that anchoring is not related to education level. It also found that numerical reasoning and reflection scores had a negative association with anchoring susceptibility.[69]

Overconfidence

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Although overconfidence emanates from the heuristic and refers more specifically to a behavioural tendency to take their initial assessment and put more emphasis on it during making their initial assessment leading to cognitive conceit.[70] Cognitive conceit or overconfidence arises from other factors like personal cognitive attributes such as knowledge and decision-making ability, decreasing the probability to pursue external sources of confirmation. This factor has also been shown to arise with tasks with greater difficulty. Even within subject matter experts, they were also prey to such behaviour of overconfidence and should more so, actively reduce such behaviour. Following the study of estimations under uncertain, despite several attempts to curb overconfidence proving unsuccessful,[71] Tversky and Kahneman (1971) suggest an effective solution to overconfidence is for subjects to explicitly establish anchors to help reduce overconfidence in their estimates.[72]

Motivation/rewards

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The motivation to be accurate in one's judgements seem to have mixed effects on the strength of anchoring. On one hand, According to Wegener's attitude change theory, it was widely accepted that the prevalent effects of anchoring was due to the pathway of low-elaboration, non-thoughtful processes.[73] The lack of reward or consequences results in the assumption that anchors are a reasonable hint to the correct answer without considering contextual differences, categorical differences, or even the relevance of the anchor. There is also evidence that the effects of anchoring is diminished when there is prior warning about the phenomenon of insufficient adjustment [74] and self-generated anchors.[75][76]

However, there is also conflicting evidence where increases in motivation does not correlate to a lowered rate of anchoring. There were no differences in the effects of anchoring when comparing participants who were offered monetary rewards for accurate answers to those who weren't.[77] Moreover, findings by Wilson et al. (1996) concluded that incentives and forewarnings did not eliminate anchoring effects.[11]

This could be explained by high elaborative anchoring - When motivated to be accurate, participants engage in more cognitively demanding thought processes, searching for existing information, including prior experiences and established anchors.[76] The high need for accuracy lead to more effortful thought processes, and putting a heavier emphasis on anchors since they are representations of prior knowledge in what we perceive as similar categories. Findings have demonstrated that both a high and low need to be accurate result in susceptibility to the influence of anchoring effects, even when one is motivated to explicitly avoid them.[76]

Culture

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Culture has been identified as an influencing factor in susceptibility to the anchoring effect. A study comparing students from Poland and India found that while both groups were affected by anchoring, the degree of susceptibility varied significantly by cultural background. Polish students demonstrated lower susceptibility to anchoring compared to Indian students.[78] Researchers proposed that individuals from cultures characterized by holistic thinking styles—more common in East and South Asian societies—are more prone to contextual influences and thus more susceptible to anchoring.[79][80] Additionally, cultural differences in overconfidence were observed, with Indian students displaying a higher rate of overprecision compared to Polish students. This is explained by the differences in cultural difference in tolerances for ambiguity and risk (uncertainty avoidance), with Poland scoring high and India scoring medium to low on Hofstede’s Uncertainty Avoidance Index (UAI).[81]

Applications

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Anchoring in negotiation

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In the negotiation process anchoring serves to determine an accepted starting point for the subsequent negotiations. As soon as one side states their first price offer, the (subjective) anchor is set. The counterbid (counter-anchor) is the second-anchor.[82]

In addition to the initial research conducted by Tversky and Kahneman, multiple other studies have shown that anchoring can greatly influence the estimated value of an object.[83] For instance, although negotiators can generally appraise an offer based on multiple characteristics, studies have shown that they tend to focus on only one aspect. In this way, a deliberate starting point can strongly affect the range of possible counteroffers.[84] The process of offer and counteroffer results in a mutually beneficial arrangement. However, multiple studies have shown that initial offers have a stronger influence on the outcome of negotiations than subsequent counteroffers.[85]

An example of the power of anchoring has been conducted during the Strategic Negotiation Process Workshops. During the workshop, a group of participants is divided into two sections: buyers and sellers. Each side receives identical information about the other party before going into a one-on-one negotiation. Following this exercise, both sides debrief about their experiences. The results show that where the participants anchor the negotiation had a significant effect on their success.[86][page needed]

Anchoring affects everyone, even people who are highly knowledgeable in a field. Northcraft and Neale conducted a study to measure the difference in the estimated value of a house between students and real-estate agents. In this experiment, both groups were shown a house and then given different listing prices. After making their offer, each group was then asked to discuss what factors influenced their decisions. In the follow-up interviews, the real-estate agents denied being influenced by the initial price, but the results showed that both groups were equally influenced by that anchor.[87]

Anchoring can have more subtle effects on negotiations as well. Janiszewski and Uy investigated the effects of precision of an anchor. Participants read an initial price for a beach house, then gave the price they thought it was worth. They received either a general, seemingly nonspecific anchor (e.g., $800,000) or a more precise and specific anchor (e.g., $799,800). Participants with a general anchor adjusted their estimate more than those given a precise anchor ($751,867 vs $784,671). The authors propose that this effect comes from difference in scale; in other words, the anchor affects not only the starting value, but also the starting scale. When given a general anchor of $20, people will adjust in large increments ($19, $21, etc.), but when given a more specific anchor like $19.85, people will adjust on a lower scale ($19.75, $19.95, etc.).[88] Thus, a more specific initial price will tend to result in a final price closer to the initial one.

As for the question of setting the first or second anchor, the party setting the second anchor has the advantage in that the counter-anchor determines the point midway between both anchors.[82] Due to a possible lack of knowledge the party setting the first anchor can also set it too low, i.e. against their own interests. Generally negotiators who set the first anchor also tend to be less satisfied with the negotiation outcome, than negotiators who set the counter-anchor.[89] This may be due to the regret or sense that they did not achieve or rather maximise the full potential of the negotiations. However, studies suggest that negotiators who set the first offer frequently achieve economically more advantageous results.[90]

Anchoring in pricing

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According to the theory, consumers’ shopping experiences are influenced by factors such as time restriction and specific environment. Enterprises design would set anchor values for consumers in order to get them to buy the products. When persuading consumers to purchase a particular product, sellers might use anchoring. Sellers often influence consumers’ price perception by anchoring a high reference price and that is an anchor value.[91] Following are three ways to set the anchor value for consumers.

Sorting the prices of products

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Sellers usually sort the prices of products from high to low and this method is common seen on the menus of restaurants.[92] The high prices at the top of the menu act as anchor values in this situation. Consumers will have an expectation that the products are all expensive when knowing the relatively high prices of products on the top of the list. As a result, they will be pleased to see the cheaper products at the middle and bottom of the list and regard these prices as acceptable or cheaper than expected. Therefore, they are more likely to buy these products.[93]

Decoy

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Decoy effect is defined as a situation where people tend to have a change in preference between two choices when they are showed with a third choice. The third choice is called a decoy which is designed to induce consumers to change their preferences. The decoy is usually considered as inferior. For example, it might be more expensive than option A while having lower quality than option B. In this case, the anchor is the decoy.[94]

One decoy effect example is the bundle sales. For example, many restaurants often sell set meals to their consumers, while simultaneously having the meals’ components sold separately. The prices of the meals’ components are the decoy pricing and act as an anchor which enables to make the set meal more valuable to consumers. With the decoy effect it generates, the anchor increases consumers’ willingness to pay for the set meals, or the mixed bundles.[95]

Incidental prices

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Incidental price is defined as the prices offered or showed by a seller for products which the consumers are not interested in. According to the theory, the incidental price serves as an anchor which increases consumers’ willingness to pay. This effect has been widely used in areas such as auctions, online vendors and retailers.[96]

Individual differences in anchoring

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Personality

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When examining the relationship between personality and individual determinants, such as extroversion and introversion, a relationship between high levels of conscientiousness and extraversion with anchoring biases was identified.[97] Nonetheless, when measuring the Big Five personality traits and anchoring susceptibility, no significant correlation was found between personality and anchoring.[98] The anchoring effect seems to be present regardless of personality.

Cognitive capability and intelligence

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Predrag Teovanović’s [99] investigated whether intelligence, cognitive reflection and personality traits affects the presence of anchoring effect in decision-making. Although measures of individual differences in susceptibility to anchoring were reliable, individual differences only explain a small portion of the variation. However, intelligence is negatively correlated with anchoring for participants who are more reflective. By critically thinking about their process of decision-making, reflective individuals might realize the unreasonable reliance on anchors and insufficient adjustments.[100] Similarly, Welsh and colleagues found a weak, negative correlation between aptitude for rationality and overall cognitive measures and anchoring susceptibility.[101]

Research supporting individual differences in anchoring suggests that individuals who recognize the potential bias of anchoring and actively reflect on their decision-making process tend to be less susceptible to its effects. By critically assessing whether their judgments are based on reliable data or influenced by arbitrary anchors, they are more likely to identify and correct for insufficient adjustments, or choose not to use unreliable anchors at all.

Affective factors

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Susceptibility to anchoring is influenced by mood. Individuals in a sad mood were more susceptible to anchoring compared to those in a neutral or happy mood. According to the attitude change theory, people in a sad mood will engage in more effortful processing, in a similar fashion as hypothesis confirmation.[53] On the other hand, based on the selective accessibility theory, sad mood results in more thorough information processing, thereby promoting the search for anchoring-consistent information to confirm their hypothesis. [100]

See also

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References

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Further reading

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Revisions and contributorsEdit on WikipediaRead on Wikipedia

Anchoring effect

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from Grokipedia
The anchoring effect is a whereby individuals rely too heavily on an initial piece of information—known as the "anchor"—when making subsequent judgments or decisions, even if that anchor is arbitrary or irrelevant. First systematically demonstrated by psychologists and in their seminal 1974 study, the effect arises from the anchoring-and-adjustment heuristic, in which people start from the anchor and make insufficient adjustments, leading to estimates biased toward the initial value. This bias has been replicated across diverse contexts, including numerical estimations, probability assessments, and real-world decision-making scenarios. Key mechanisms underlying the anchoring effect include selective accessibility, where the anchor activates related knowledge that skews and processing. According to the Selective Accessibility Model proposed by Mussweiler and Strack in 1997, individuals compare the target judgment to the , making anchor-consistent more cognitively accessible and thus more influential. Factors such as , expertise, and emotional state can moderate the effect's magnitude; for instance, higher knowledge levels reduce susceptibility, while adolescents experiencing sadness show heightened vulnerability. Classic experiments illustrate the anchoring effect's robustness. In Tversky and Kahneman's foundational -of-fortune task, participants who spun a rigged to show 10 or 65 estimated the of African countries in the as 25% or 45%, respectively, despite knowing the spin was random. Similarly, when high school students estimated the product of 1×2×3×4×5×6×7×8 based on ascending or descending sequences, those starting with larger numbers (8×7×...×1) provided higher medians (2,250 vs. 512), anchoring on partial products. These findings persist even under accuracy incentives, underscoring the heuristic's . The anchoring effect manifests in practical domains, influencing economic, legal, and social decisions. In negotiations, initial offers serve as anchors that shape final agreements, often favoring the proposer. Consumer behavior is affected by reference prices; for example, a high suggested retail price can make a discounted item seem like a bargain, increasing purchase likelihood. In judicial settings, arbitrary anchors like suggested sentence lengths bias sentencing recommendations. Recent highlights its relevance in adolescent decision-making, where arbitrary anchors (e.g., social security number digits) can distort for goods.

Definition and Overview

Core Definition

The anchoring effect is a in which an individual's judgments or decisions are influenced by an initial piece of , known as the "anchor," which serves as a reference point even when it is arbitrary or irrelevant. This leads people to rely too heavily on the anchor, resulting in estimates or evaluations that are biased toward it rather than being based solely on available . In the basic process, exposure to the anchor shapes subsequent estimates through an initial reliance that is not fully corrected. For instance, in a study where participants spun a wheel of fortune rigged to show either 10 or 65 and then estimated the percentage of African countries in the , the median estimates were 25% and 45%, respectively, demonstrating how random anchors systematically biased responses despite their irrelevance. Unlike , which involves selectively seeking or interpreting information to support preexisting beliefs, the anchoring effect specifically concerns the undue weight given to the initial anchor without necessarily involving active confirmation-seeking. A simple illustrative example is estimating the height of the after being asked whether it is higher or lower than a low value (e.g., 200 meters) versus a high value (e.g., 1,000 meters); responses to the low tend to be lower overall than those to the high , showing the anchor's pull on judgment. This effect is part of broader heuristics used in judgment under uncertainty.

Significance in Cognitive Biases

The anchoring effect serves as a foundational example of heuristic-based thinking within the heuristics and biases program developed by and , illustrating how individuals rely on mental shortcuts that systematically deviate from rational norms in judgment and . This underscores the limitations of human cognition in processing uncertainty, where initial reference points unduly influence subsequent estimates, even when those anchors are arbitrary or irrelevant. In the context of , the anchoring effect exemplifies how cognitive constraints lead to predictable errors in probability , valuation, and tasks, challenging the assumptions of classical economic models that presume fully rational agents. Herbert Simon's concept of , which posits that decision-makers operate under limited information and cognitive capacity, is vividly demonstrated through anchoring, as people insufficiently adjust from initial anchors, resulting in biased outcomes that persist across diverse domains. These systematic deviations highlight the gap between normative rationality and actual , informing by revealing how anchors can distort and . The anchoring effect aligns closely with dual-process theory, particularly as articulated by Kahneman, where it predominantly operates through —fast, intuitive, and automatic thinking—that generates quick judgments without deliberate scrutiny. In contrast, System 2 involves slower, analytical processing that could mitigate anchoring if engaged, but often fails to do so under time pressure or , allowing intuitive biases to prevail. This interplay explains why anchoring infiltrates everyday decisions, reinforcing the theory's emphasis on the default dominance of effortless . Beyond individual judgments, the anchoring effect contributes to overconfidence in forecasts by anchoring predictions to initial values, leading to narrower intervals than warranted and inflated in probabilistic assessments. In economic markets, this manifests as inefficiencies, such as anchored stock valuations that delay price corrections and exacerbate bubbles or crashes. Similarly, in policy-making, anchoring can skew public estimates of costs or benefits, resulting in suboptimal resource distribution, as seen in negotiations where initial proposals final agreements.

History and Development

Origins in Psychological Research

The anchoring effect was discovered by psychologists Amos Tversky and Daniel Kahneman through a series of experiments conducted between 1971 and 1974, marking a key contribution to cognitive psychology. Their findings were first formally presented in the influential 1974 paper "Judgment under Uncertainty: Heuristics and Biases," published in the journal Science. This work built on their ongoing collaboration, which began in the early 1970s, to explore how people make probabilistic judgments in uncertain situations. The research emerged within broader studies on systematic errors in human judgment, directly challenging the prevailing economic assumption of human rationality as posited in classical . Tversky and Kahneman demonstrated that individuals often rely on simplifying mental shortcuts, or heuristics, rather than precise calculations, leading to predictable biases that deviate from optimal reasoning. This perspective shifted focus from viewing judgment errors as random noise to recognizing them as consequences of adaptive cognitive processes. In their early theoretical framing, the anchoring effect was integrated into a trio of core heuristics—representativeness, , and —that govern intuitive judgments under . Specifically, was described as a process where an initial value, or , influences subsequent estimates, even when the anchor is arbitrary or irrelevant, due to insufficient mental adjustment from that starting point. The initial experiments illustrating this involved tasks requiring numerical estimates. In one seminal demonstration, participants were asked to estimate the percentage of African countries in the after spinning a rigged wheel of fortune that landed on either 10 or 65. Those exposed to the lower anchor provided median estimates of 25% and 45%, respectively, revealing a clear pull toward the irrelevant starting value despite incentives for accuracy.

Evolution and Key Milestones

Following the foundational work on the anchoring effect in the , research in the and increasingly integrated the bias into , highlighting its implications for under and challenging traditional rational choice models. This period saw anchoring applied to economic judgments, such as and valuation, demonstrating its robustness across numerical and non-numerical domains. A pivotal recognition came in 2002 when was awarded the Nobel Memorial Prize in Economic Sciences for integrating psychological research, including the anchoring effect, into economic , thereby elevating its status as a core mechanism in and behavioral . This accolade underscored anchoring's role in explaining systematic deviations from economic rationality. Theoretical progress advanced in 1999 with Mussweiler and Strack's selective accessibility model, which posits that anchors influence judgments by selectively increasing the availability of consistent information in memory, rather than solely through adjustment. Building on this, Epley and Gilovich's 2001 study revived the anchoring-and-adjustment heuristic by showing that insufficient adjustment from self-generated anchors produces the effect, emphasizing cognitive effort limitations in the process. The 2000s marked a shift toward neuroscientific investigation, with (fMRI) studies revealing activation, particularly in the , during anchor-based adjustments in mentalizing tasks. These findings linked anchoring to executive control processes, providing biological evidence for the heuristic's operation. By the , research extended to ethics, with 2024 studies on large language models (LLMs) demonstrating anchoring biases in generated judgments, such as numerical estimates influenced by initial prompts. Complementary 2025 analyses showed that chain-of-thought reasoning techniques mitigate this bias in LLMs by promoting step-by-step deliberation, akin to debiasing strategies.

Experimental Evidence

Seminal Studies

One of the foundational demonstrations of the anchoring effect came from Tversky and Kahneman's 1974 study, where participants were first asked to spin a rigged wheel of fortune that always stopped at either 10 or 65, ostensibly to determine group assignment but serving as an irrelevant anchor. They then estimated the percentage of African countries in the ; those who saw 10 provided a estimate of 25%, while those who saw 65 estimated 45%, despite the wheel's number having no relation to the target question. In the same 1974 paper, Tversky and Kahneman illustrated anchoring in numerical tasks by asking separate groups to estimate the product of the numbers 1 through 8, presented in ascending order (1 × 2 × 3 × 4 × 5 × 6 × 7 × 8) or descending order (8 × 7 × 6 × 5 × 4 × 3 × 2 × 1), with only 5 seconds allowed for calculation. The ascending anchored participants to lower partial products, yielding a estimate of 512, whereas the descending led to a of 2,250; the is 40,320 in both cases. A seminal replication emphasizing the robustness of anchoring with extreme and irrelevant values was Strack and Mussweiler's 1997 experiment on estimates of Gandhi's age at death. Participants in one condition were asked if Gandhi died before or after age 9 before providing an absolute estimate, resulting in a mean of 50 years; the other condition used age 140 as the anchor, yielding a mean estimate of 67 years, showing assimilation toward even implausible anchors. Early studies, building directly on these findings, confirmed that anchoring persists with unrelated or arbitrary information, such as random numbers unrelated to task, demonstrating the bias's influence beyond informative starting points.

Research Methodologies

Research on the anchoring effect employs two primary experimental paradigms: absolute anchoring and relative anchoring. In absolute anchoring, participants are presented with a numeric —often a random or arbitrary value—and asked to provide a direct estimate for a target quantity, such as the length of the or the population of a , without explicit to the . This paradigm isolates the influence of the on standalone judgments. In contrast, relative anchoring involves an initial comparative judgment, where participants assess whether the target quantity is higher or lower than the provided , followed by an absolute estimate; this sequential process, as introduced in seminal work, heightens the anchor's impact by engaging participants more deeply with the value. Measurement of the anchoring effect typically compares pre-anchor baseline estimates (obtained without exposure to an ) or no-anchor control conditions against post-anchor estimates from the same or parallel participant groups. The magnitude of the bias is quantified as the difference in mean estimates between high-anchor and low-anchor conditions, standardized using Cohen's d to assess , where values around 0.5 indicate medium effects and higher values reflect robust biases. This approach allows researchers to evaluate the directional pull of the anchor while controlling for individual variability in knowledge or estimation tendencies. To mitigate demand characteristics—cues that might lead participants to infer and conform to experimental hypotheses—studies incorporate irrelevant anchors, such as unrelated numbers (e.g., ID codes or zip codes) presented without direct ties to the target, or blinded procedures where anchors are generated randomly via tools like a wheel of fortune or dice to obscure their purpose. Self-report questions post-experiment further probe perceived influence, confirming that effects persist even when participants deny awareness of the anchor's role, thus isolating genuine from strategic responding. Quantitative metrics focus on the adjustment magnitude from the , often modeled using a logarithmic pull where the final estimate approximates a : log⁡(estimate)=αlog⁡([anchor](/page/Anchor))+(1−α)log⁡(true value)\log(\text{estimate}) = \alpha \log(\text{[anchor](/page/Anchor)}) + (1 - \alpha) \log(\text{true value}), with α\alpha (the anchoring index, ranging from 0 to 1) representing the degree of insufficient adjustment; values closer to 1 indicate stronger anchoring, derived via regression on log-transformed across multiple trials. This model captures the nonlinear assimilation toward the , prioritizing conceptual fit over exhaustive parameter estimation. Recent methodologies, including large-scale online surveys for scalable participant recruitment and as in the 2022 Open Anchoring Quest , enable replication with diverse demographics while maintaining random assignment. Additionally, AI-simulated using large models (e.g., GPT-4o) in virtual scenarios like price negotiations generate dynamic, context-specific , facilitating simulations of human-like interactions and assessment of bias in automated systems at unprecedented volumes.

Underlying Mechanisms

Anchoring-and-Adjustment Heuristic

The anchoring-and-adjustment heuristic posits that individuals form judgments under uncertainty by starting with an initial value, or anchor, and then making adjustments to reach a final estimate, but these adjustments are typically insufficient due to cognitive limitations. This process occurs because decision-makers mentally represent the anchor as a starting point and incrementally adjust upward or downward based on available information, yet they often stop prematurely when the mental effort required for further adjustment outweighs perceived benefits. Empirical support for this comes from studies demonstrating that the extent of adjustment varies with and cognitive resources; for instance, when participants are incentivized for accuracy or given more time, they adjust further from the , reducing the , as outlined in the effort-accuracy framework. In this model, adjustment is an effortful process akin to a search for plausible reasons to deviate from the , and insufficient adjustment arises because cease searching once a estimate is reached, even if it remains biased. Mathematically, the final judgment JJ can be represented as J=A+ΔJ = A + \Delta, where AA is the anchor value and Δ\Delta is the adjustment magnitude, which is often smaller in absolute value than needed for an unbiased estimate due to underadjustment. This underadjustment leads to asymmetric effects, particularly in estimation tasks where a low anchor results in underestimates because adjustments are "lazy" and fail to fully compensate for the initial bias. A classic example is the estimation of the percentage of African countries in the , where participants spun a to generate a random (e.g., 10% or 65%); those with the low estimated around 25%, insufficiently adjusting upward, while those with the high estimated around 45%, insufficiently adjusting downward.

Selective Accessibility

The selective accessibility model posits that anchoring effects arise from a memory-based process in which exposure to an prompts hypothesis-testing, specifically evaluating whether the target stimulus shares similarities with the . This confirmatory strategy, rather than disconfirmatory testing, leads to the selective activation and retrieval of -consistent knowledge from networks. For instance, a high numerical for estimating a value, such as the price of a , triggers consideration of whether the target matches that high value, thereby priming and increasing the of related high-value exemplars like luxury brands. In this process, anchors function as primes that spread activation through associative memory structures, skewing the information retrieved during subsequent judgments toward content that aligns with the anchor's implications. This semantic priming occurs automatically and influences the content of thought without necessarily altering the perceived extremity of the anchor itself, resulting in biased absolute judgments that assimilate toward the anchor. Empirical evidence from reaction time studies supports this mechanism: in lexical decision tasks following an anchoring task, participants exhibited faster recognition of anchor-consistent words (e.g., "Mercedes" after a high anchor of 40,000 Deutsche Marks for car value) compared to inconsistent ones (e.g., "Fiat"), indicating heightened accessibility of primed knowledge. Unlike the anchoring-and-adjustment , which emphasizes a sequential correction from an initial anchor value, selective accessibility highlights pre-adjustment priming effects where the anchor biases the very information considered in forming the judgment. This model integrates with adjustment processes by suggesting that the skewed accessibility shapes the direction and extent of any subsequent adjustments, though the core originates from the initial hypothesis-consistent retrieval.

Complementary Theories

Attitude change theories provide a supplementary framework for understanding anchoring by positing that anchors function as reference points that alter attitudes through contrast effects, where extreme anchors are discounted or counterargued, leading to weaker influences on judgments compared to moderate ones. This perspective contrasts with assimilation-based models, suggesting that perceived implausibility of anchors triggers metacognitive processes that mitigate their impact, as demonstrated in experiments where more extreme numerical anchors produced smaller shifts in estimates than moderate ones. Extremeness aversion offers another complementary explanation, rooted in the preference for compromise options that avoid extremes, which causes extreme anchors to drive judgments toward more central values in decision sets. In choice scenarios, this aversion manifests as conservative adjustments from anchors, reducing the bias's magnitude when options are presented alongside the anchor, thereby attributing part of anchoring to a motivational pull toward moderation rather than purely cognitive insufficient adjustment. An evolutionary perspective frames anchoring as an adaptive mechanism for expediting decisions in resource-scarce, uncertain environments, where reliance on initial cues would have conferred survival advantages by enabling fast approximations despite incomplete information. This bias is seen as arising from bounded implementations of Bayesian updating, in which cognitive limitations prevent full integration of evidence, resulting in systematic errors that were nonetheless efficient heuristics in ancestral contexts. From the viewpoint of numerical cognition, anchoring leverages the imprecise, spatially organized representation of magnitudes on the mental , where initial numerical cues subsequent magnitude estimations due to nonlinear scaling that amplifies the influence of salient digits. A prominent example is the left-digit in , where the leftmost digit serves as an , causing consumers to perceive $2.99 as significantly cheaper than $3.00 despite the minimal actual difference, exploiting the compressed encoding of small numbers in cognitive representations.

Characteristics of the Bias

Persistence and Durability

Empirical studies demonstrate that the exhibits considerable , maintaining its influence on judgments even after significant time delays. For instance, in experiments involving estimates of historical events and environmental conditions, high or low anchors continued to participants' responses to a comparable degree after a one-week interval as they did immediately, with no significant diminution in effect magnitude. Similarly, uninformative anchors on product valuations persisted for up to eight weeks, with biases stabilizing after an initial decay period. This persistence arises from mechanisms involving , where the selectively enhances the accessibility of consistent information, fostering deeper cognitive processing that resists natural decay. Self-generated anchor-related thoughts, as opposed to externally imposed ones, further promote this consolidation by integrating the more firmly into memory schemas, thereby prolonging its impact without requiring ongoing exposure. Factors such as repetition can amplify the anchoring effect's endurance, particularly in applied contexts like or . When anchors are presented multiple times—such as three exposures versus one in price estimation tasks—the bias strengthens significantly, as repeated anchors consolidate their influence on value perceptions and reduce variability in subsequent judgments. Quantitative models of anchoring decay indicate that while the may halve over extended periods due to cognitive interference or , it rarely dissipates entirely without deliberate interventions like explicit debiasing, maintaining a residual influence that underscores the heuristic's robustness over time.

Resistance to Debiasing

The anchoring effect demonstrates significant resistance to debiasing efforts, particularly those relying on or explicit instructions to participants. Empirical studies consistently show that forewarning individuals about the potential for can attenuate the effect, but it rarely eliminates it entirely. For instance, when participants are informed in advance that an initial value may bias their judgments, the magnitude of the anchoring influence decreases, yet substantial residual effects persist across various tasks, as evidenced by reduced but still significant adjustment patterns in experiments. This persistence arises primarily from unconscious cognitive processes, including priming mechanisms that subtly activate anchor-consistent information without deliberate awareness. The selective accessibility model posits that exposure to an anchor increases the of related thoughts in , making it difficult for conscious efforts to override this automatic retrieval. Additionally, overconfidence in the sufficiency of one's adjustments exacerbates the issue, as individuals often believe they have adequately compensated for the anchor, leading to insufficient corrections in judgments. A notable example of this resistance occurs in legal , where even experienced judges remain influenced by prosecutorial demands despite being informed of the anchor's irrelevance. In one study, German judges reviewed a case file and were told that the prosecutor's suggested sentence was determined by a dice roll rather than case merits; nonetheless, high-anchor conditions led to harsher (mean of 6.5 months) compared to low-anchor conditions (mean of 5.4 months), with the effect persisting regardless of the disclosure. This illustrates how domain expertise and explicit knowledge fail to fully counteract anchoring in high-stakes professional contexts. Recent research in AI-assisted decision-making further highlights these challenges, showing that interactive effects between AI-sourced anchors and human judgments result in biased outcomes, with high anchors from AI leading to significantly elevated scores compared to human sources; the study underscores the need for robust interventions such as the "consider-the-opposite" strategy.

Ubiquity Across Contexts

The anchoring effect demonstrates remarkable ubiquity, manifesting robustly across a wide array of non-experimental settings and judgment types, as evidenced by comprehensive meta-analyses encompassing thousands of effect sizes. These reviews reveal a large overall effect (Cohen's d = 0.824), persisting in both probabilistic and numerical tasks with minimal attenuation after corrections for publication bias, underscoring its pervasive influence on human cognition beyond controlled laboratory conditions. In legal domains, the effect prominently appears in sentencing decisions, where meta-analyses of 29 studies involving over 8,500 participants show that arbitrary numerical bias verdicts toward the anchor value, with moderate to large effect sizes (d ranging from 0.58 to 0.91) moderated by factors like legal expertise and anchor relevance. Similarly, in medical contexts, anchoring on initial patient information can delay critical diagnoses; for instance, when congestive is mentioned in emergency visit notes, physicians exhibit reduced testing for , increasing time to diagnosis by an average of 15.5 minutes and thereby extending the untreated duration of potentially life-threatening conditions. The bias extends to everyday scenarios, such as consumer shopping, where an initial high acts as an , making subsequent discounted options appear more valuable and influencing buying choices—for example, a reduced from $100 to $60 feels like a superior deal compared to the same item priced directly at $60. In business intelligence applications, dashboard metrics often introduce plausible anchors that skew managerial forecasts; experimental evidence indicates that an initial sales projection of $5,000 billion on a BI interface biases estimates toward $3,990 billion, even when underlying data supports $3,818 billion, highlighting how such tools inadvertently perpetuate the effect in professional forecasting. Initial investigations, primarily drawn from Western samples, consistently replicate the anchoring effect's presence in numerical and probabilistic judgments, though subsequent analyses across 10 countries reveal heterogeneity, with cultural values like intellectual autonomy inversely related to effect magnitude. This broad applicability underscores the anchoring effect's role in real-world across individual and professional spheres.

Manifestations in Groups

In group settings, the anchoring effect manifests through shared reference points that foster consensus , where collective judgments converge around an initial rather than objective evidence. For instance, in deliberations, the first member's proposed sentence length or award serves as a salient , pulling the group's final verdict toward that value even when subsequent arguments suggest otherwise. A of 29 studies involving over 8,500 participants found significant anchoring effects in legal , including decisions, with an average of d = 0.58 for comparisons including control groups, indicating that arbitrary numerical suggestions reliably bias group-awarded penalties and compensations. This group-level anchoring is often amplified by mechanisms such as informational cascades, in which the initial anchor propagates through social , prompting members to align their estimates with the emerging group norm to avoid . In centralized group structures, where one dominates influence, to the leader's anchored preference exacerbates the , as subordinates adjust insufficiently from the reference point. from experimental studies shows that such dynamics can make group judgments more susceptible to anchoring than ones; for example, in groups with hierarchical trust networks, the anchoring effect index was significantly higher than for solo decision-makers (Wilcoxon z = 1.782, p = 0.075), leading to estimates biased up to nearly twice as strongly in low-anchor conditions compared to independent assessments. Recent research also indicates that structure moderates group anchoring, with dense networks reducing compared to sparse or centralized ones. In contexts, forecasting exemplifies this manifestation, where a leader's preliminary numerical projection anchors subsequent discussions and final outputs, often resulting in overly conservative or optimistic forecasts detached from market realities. A study of 45 financial professionals in Indonesian firms revealed that anchoring on initial estimates explained 34% of the variance in budgeting errors (β = 0.58, p < 0.01), with hierarchical intensifying the through pooled preferences that reinforce the starting figure. These findings underscore how shared anchors in collaborative environments can entrench errors, particularly when group emphasizes over critical scrutiny.

Moderating Factors

Cognitive and Affective Influences

Cognitive busyness, or high mental load, significantly increases susceptibility to the anchoring effect by limiting the cognitive resources available for adjustment from the initial . When individuals are engaged in demanding tasks or multitasking, their ability to critically evaluate and sufficiently adjust away from the anchor diminishes, leading to stronger biases in judgments. For instance, experimental evidence demonstrates that imposing a secondary during estimation tasks results in larger anchoring effects, as participants make fewer and smaller adjustments due to constrained processing capacity. This aligns with the anchoring-and-adjustment , where effortful correction is hindered under load, exacerbating reliance on the anchor. Mood states exert a notable influence on anchoring susceptibility, with positive and negative emotions moderating the bias through distinct mechanisms. Positive mood, induced via recall of uplifting events or exposure to humorous stimuli, enhances cognitive flexibility and promotes broader information integration, thereby reducing anchoring by facilitating more thorough adjustments from the anchor. In contrast, negative moods, such as sadness, foster conservatism and narrowed attention, increasing reliance on the anchor and amplifying the bias, as evidenced in studies from the early 2000s showing greater anchoring in sad participants compared to those in neutral or happy states. These effects highlight how affective states alter the motivational drive for adjustment, with optimism in positive moods countering the bias more effectively than the caution induced by negative emotions. Overconfidence in one's or accuracy further moderates anchoring by reducing the extent of adjustment from the . Individuals with high overconfidence perceive their initial assessments as reliable, leading them to exert less effort in correcting toward the and resulting in persistent . Research testing the anchoring-and-adjustment model reveals that overconfident estimators produce wider intervals but still exhibit insufficient adjustment, as their inflated self-perceived accuracy discourages thorough reevaluation. This interaction underscores overconfidence as a cognitive factor that entrenches anchoring, particularly in domains requiring precise numerical or probabilistic judgments. In high-stress environments like prehospital care, affective influences such as acute stress amplify anchoring during triage decisions, where initial presentations serve as potent anchors. A 2025 scoping review of cognitive biases in prehospital critical care identifies anchoring as one of the most prevalent, noting that the intense pressure and time constraints heighten emotional , impairing adjustment and leading to diagnostic errors in prioritization. For example, paramedics under may overly fixate on early symptoms, delaying recognition of evolving conditions, thus illustrating how transient affective states in real-world settings intensify the bias beyond laboratory conditions.

Individual Traits and Experiences

Individual differences in traits significantly moderate susceptibility to the anchoring effect. Individuals high in , characterized by greater diligence and self-discipline, exhibit reduced anchoring bias compared to those low in this trait, as they tend to engage in more thorough information processing and adjustment from initial anchors. In contrast, extraversion is associated with heightened anchoring, particularly in social contexts where individuals may be more influenced by external cues or group-provided anchors due to their outgoing nature and sensitivity to . Cognitive ability also plays a key role in mitigating the anchoring effect. Higher levels of cognitive ability, such as measured by IQ or related tasks, correlate with better adjustment away from anchors, leading to smaller bias magnitudes, though the effect does not eliminate anchoring entirely. This relationship aligns with dual-process theories, where more reflective thinkers leverage analytical skills to counteract initial biases more effectively. Prior experience in a domain further diminishes anchoring proneness among experts. For instance, agents, with their extensive familiarity in property valuation, show less influence from arbitrary listing prices as anchors compared to novices, although they remain partially susceptible. This expertise effect stems from accumulated knowledge that facilitates more accurate adjustments and reliance on relevant cues over irrelevant anchors. The (NFC), reflecting an individual's intrinsic motivation to engage in effortful thinking, promotes more thorough adjustments from anchors. High-NFC individuals demonstrate greater resistance to anchoring by expending more cognitive resources to evaluate and revise initial estimates.

Cultural and Motivational Elements

The anchoring effect exhibits notable cultural variations, particularly in social contexts where collectivistic societies demonstrate stronger susceptibility compared to individualistic ones. Research comparing students from (a collectivistic, Eastern culture) and (an individualistic, ) found that Indian participants displayed a significantly higher anchoring indicator of 72.8%, indicating greater reliance on initial anchors, while Polish participants showed only 35.5%. This difference is attributed to holistic thinking patterns prevalent in collectivistic cultures, which may enhance the integration of contextual cues like anchors in social judgments. Motivational elements play a in modulating anchoring susceptibility, with directly influencing adjustment from . When individuals are motivated by high accuracy goals—such as through monetary incentives for precise estimates—they tend to exert more effort in adjusting away from the provided , thereby attenuating the . Extremeness aversion further explains anchoring in scenarios, where decision-makers avoid extreme options, resulting in insufficient adjustments from anchors to maintain moderate positions.

Real-World Applications

Negotiations and Persuasion

In negotiations, the first offer serves as a powerful that influences subsequent concessions and final agreements, often providing a substantial advantage to the party making the initial proposal. demonstrates that negotiators who make the first offer achieve better economic outcomes, with final settlement prices more closely aligned with their than with subsequent counteroffers, due to the anchoring effect's pull on . For instance, in experimental dyadic bargaining scenarios, the first mover's offer predicted final outcomes more strongly than later adjustments. This advantage arises because counterparts insufficiently adjust their counteroffers away from the initial , assimilating new selectively toward it rather than fully reevaluating based on independent standards. The tendency for insufficient adjustment is particularly evident in how negotiators respond to an opponent's first offer. When presented with an extreme initial proposal, individuals start from that and make concessions that fall short of what rational analysis might suggest, resulting in asymmetric outcomes favoring the anchor-setter. Studies show this holds across both face-to-face and written negotiations, where the 's influence persists even when negotiators are aware of it, as adjustments are psychologically constrained by the initial point. In group settings, this can amplify if multiple members converge on the same , though the core dyadic effect remains dominant. Beyond pure , the anchoring effect plays a key role in by shaping the magnitude of belief change through initial arguments. When exposed to an extreme starting position in debates or sales pitches, audiences anchor their evaluations to that point, leading to moderated shifts in opinion that are insufficiently distant from the original . For example, in persuasive communications, an initial high estimate of a problem's severity can anchor perceptions, making subsequent moderate proposals seem more reasonable and increasing acceptance rates compared to starting from a neutral baseline. This mechanism underlies why order of presentation matters in influence attempts, with early anchors biasing the interpretive frame for later information. A practical illustration occurs in salary negotiations, where an employer's posted range or initial offer anchors the applicant's expectations and counteroffers. If a job listing specifies a salary band of $80,000–$100,000, candidates often anchor to the lower end when proposing their desired pay, leading to final agreements closer to that floor despite the applicant's potentially higher . Experimental evidence confirms that such anchors influence the nature of counteroffers, underscoring the need for independent valuation to mitigate the .

Marketing and Pricing

In marketing, the anchoring effect is frequently exploited through pricing strategies that establish an initial reference point to shape perceptions of value. Retailers often display an original higher alongside a discounted , such as "Was $100, now $70," which serves as an making the lower appear as a superior deal compared to evaluating the discounted in isolation. Experimental research demonstrates that this reference anchoring significantly influences purchase intentions, with consumers perceiving greater savings and value when the original is presented as the , leading to a 38% increase in purchase likelihood in settings. Decoy pricing leverages anchoring by introducing an inferior option that shifts preferences toward a target product without directly competing on value. In a classic example, subscription bundles for included a web-only option at $59, a print-only option at $125, and a combined print-and-web option at $125; the print-only anchored the combined option as more attractive despite identical , increasing selections of the target by highlighting its relative superiority. This asymmetric dominance effect, where the decoy anchors the comparison, was first empirically demonstrated in experiments, showing it systematically alters preferences across product categories. Incidental anchors, such as nearby prices or formatting, further value perception without explicit intent. The left-digit effect exemplifies this, where prices like $99 are anchored by the "9" in the tens place, perceived as closer to $90 than $100, enhancing affordability illusions even for small differences. Studies confirm this anchoring occurs subconsciously during price , with consumers rating $2.99 as significantly lower than $3.00 due to the leftmost digit's disproportionate influence on overall . Sorting effects in price displays also utilize anchoring by presenting options from high to low, establishing the highest price as an extreme anchor that makes mid-range alternatives seem more reasonable and boosts their sales. For instance, restaurant menus sorted descendingly anchor diners to premium items first, increasing uptake of moderately priced entrees by contrasting them against the high end. Research on consumer price judgments supports this, indicating that high-to-low sorting amplifies anchoring, leading to adjusted perceptions that favor central options over absolute low-end choices.

Professional Decision-Making

In professional decision-making, the anchoring effect significantly influences judgments in high-stakes domains such as , where initial symptoms or presentations can bias clinicians toward overly pessimistic or optimistic and treatment durations. For instance, in prehospital critical care settings, anchoring on early or patient reports has been shown to skew decisions, leading to delayed interventions in conditions like or trauma. A 2025 scoping review of cognitive biases in identified anchoring as one of the most prevalent biases, occurring when paramedics fixate on initial findings, thereby affecting the perceived severity and urgency of care. Similarly, anchoring on initial diagnostic hypotheses can prolong stays or alter estimates, as evidenced by studies demonstrating that physicians who receive an initial low-severity anchor adjust subsequent assessments insufficiently, even with contradictory . In the legal field, anchoring manifests prominently during jury deliberations and sentencing, where the prosecutor's initial or suggested serves as a reference point that inflates final damage amounts or penalties. Empirical research on real litigation cases reveals that high-anchor demands from lead to significantly higher compared to low-anchor scenarios, with adjustments often insufficient to neutralize the bias. A of anchoring in legal confirms this effect across numerical judgments, including civil damages and criminal sentencing, where even expert judges and jurors exhibit partial reliance on arbitrary initial figures. For example, in damage trials, prosecutorial or anchors as high as 10 times the median outcome have been shown to elevate verdicts, underscoring the bias's robustness in adversarial contexts. Recent analyses further highlight how initial offers anchor jurors to extreme valuations, complicating fair assessments of liability and compensation. Emerging research in and technology demonstrates that large language models (LLMs) are susceptible to anchoring, mirroring human biases in their outputs for reasoning and prediction tasks. When prompted with an initial numerical or categorical anchor, LLMs tend to generate responses biased toward that value, even in unrelated subsequent queries, as shown in experimental studies where models like exhibited anchoring effects in estimation tasks. A 2025 investigation into anchoring in LLMs found this bias persists across domains, including financial forecasting and ethical dilemmas, but can be partially mitigated through advanced prompting techniques. These findings highlight the need for debiasing protocols in AI-assisted professional tools, such as decision-support systems in tech development. In and organizational contexts, anchoring affects performance appraisals, where first impressions or initial metrics heavily influence overall evaluations, often leading to skewed ratings and resource allocations. Studies involving managers using AI-driven appraisal tools reveal that an initial high or low performance anchor—such as early project feedback—biases final scores, with raters adjusting only marginally despite comprehensive data reviews. A 2025 experimental demonstrated that AI-generated initial ratings anchored human judgments, particularly in hybrid human-AI systems. This is exacerbated in annual reviews, where early interactions set a persistent reference, potentially perpetuating inequities in promotions and compensation. In behavioral finance, anchoring bias leads investors to rely excessively on initial or arbitrary reference points, such as high round target prices (e.g., $200,000 for an asset), which serve as psychological anchors. This fixation causes investors to focus on these anchors while ignoring relevant risks and current market conditions, often resulting in biased assessments of value and potential returns. For instance, investors may anchor to an analyst's target price or historical purchase price, leading them to hold overvalued investments in hopes of returning to that level, thereby underestimating actual risks.

Strategies for Mitigation

Debiasing Techniques

One effective approach to counteracting the anchoring effect involves , which educates individuals about the and its mechanisms to promote more deliberate . Studies have shown that such can significantly reduce the influence of anchoring, with -based interventions retaining debiasing effects over several weeks. For instance, combined slideshow and has been found to mitigate anchoring in tasks, with reductions in magnitude observed immediately and persisting for up to four weeks post-. However, alone often proves incomplete, as it may not fully eliminate the bias without additional strategies; one complementary method is the "consider-the-opposite" technique, which prompts individuals to actively generate and evaluate arguments against the initial anchor, leading to more balanced judgments. Experimental evidence demonstrates that this strategy compensates for selective information processing triggered by anchors, reducing in social and numerical contexts. Counter-anchoring represents another targeted debiasing method, particularly useful in interactive settings like negotiations, where introducing an alternative anchor can balance the initial reference point and moderate its pull. In negotiation scenarios, parties can employ this by presenting a counter-offer that serves as a new reference, such as averaging a high initial demand with a lower realistic proposal to shift perceptions toward a midpoint. Research on supplier evaluations illustrates that providing contrasting anchors helps mitigate asymmetric effects, with low anchors increasing perceived performance ratings when paired with high alternatives, thereby improving selection decisions. This technique leverages the same cognitive mechanism as anchoring but redirects it toward equilibrium, though its success depends on the credibility and timing of the counter-anchor. Process interventions focus on structuring to encourage thorough adjustment away from the , often through tools that promote extended deliberation or . Checklists, for example, guide users to verify assumptions, question the origin of numerical inputs, and consider multiple data sources before finalizing estimates, thereby interrupting insufficient adjustment. In clinical and managerial contexts, allocating deliberate time for reflection—such as pausing to override intuitive responses—activates analytical processing, reducing reliance on the anchor in controlled tasks involving probabilistic judgments. These interventions emphasize executive control, ensuring that decisions incorporate broader rather than anchoring prematurely. Technological aids, including AI-driven tools, offer promising support for flagging potential anchors in real-time, especially in research-intensive fields like . Recent investigations highlight how AI assistants, when used judiciously, can mitigate anchoring by prompting verification of outputs against primary sources and independent brainstorming prior to integration. A 2025 study on AI-assisted research found that structured protocols—such as cross-checking AI-generated literature reviews and statistical analyses—reduced anchoring bias in formulation and data interpretation, enhancing the reliability of findings in bias-prone processes. These tools function best as supplements to human oversight, alerting users to initial values that may unduly influence subsequent steps.

Challenges and Limitations

While debiasing techniques for the anchoring effect, such as considering , demonstrate efficacy in controlled individual settings, their application to presents significant challenges due to shared anchors and collective dynamics that amplify or alter bias propagation. In group contexts, external anchors influence judgments beyond individual preferences, often requiring tailored interventions that account for social interactions, which standard methods do not fully address. Furthermore, these techniques necessitate cultural adaptations, as anchoring susceptibility varies across cultural groups, with replication studies revealing heterogeneous effects that demand context-specific modifications to ensure generalizability. Key challenges in mitigation include the risk of overcompensation, where strategies like considering the opposite lead to reverse anchoring, particularly when initial anchors are close to true values, resulting in judgments that overshoot in the opposite direction. This context-dependency is exacerbated by the need for high motivation; unconscious biases persist without sufficient awareness or incentive to engage debiasing efforts, as overconfidence and resistance to change hinder adoption in dynamic environments. In real-world applications, such as managerial evaluations, debiasing shows asymmetrical results—effectively countering high anchors but failing against low ones—further complicated by practical barriers like reluctance to acknowledge prior errors. Research gaps remain prominent, particularly in non-Western cultures, where methodological limitations and low statistical power have yielded insufficient evidence of cultural variability, underscoring the need for more diverse, high-powered studies. In emerging domains like , anchoring biases in large language models (LLMs) persist despite mitigations; recent studies including a 2024 analysis on models like reveal that chain-of-thought prompting fails to reduce bias from "expert" anchors, highlighting incomplete fixes and the demand for novel approaches. Ethical considerations arise from the tension between intentional anchoring in public policy—such as nudges leveraging initial values to influence preferences, which risks manipulative elite control—and unintended anchoring in AI systems, where embedded biases can perpetuate inequities without transparency or accountability. In AI ethics, anchoring effects in decision aids raise concerns about overreliance on flawed recommendations, potentially amplifying discrimination unless proactively mitigated through rigorous bias audits.

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