The sunk cost effect in pigeon behaviour.

The sunk cost effect in pigeon behaviour.

Eva and Jason had just finished a long morning trading in stocks in Canary Wharf. Every day at lunch time they liked to like to get out of the grey, poorly lit office and eat their lunch outside in Jubilee Park. They always sat on the same patch of grass and watched the pigeons and people pass by. As they ate Eva noticed that one of the pigeons tended to stick to the same area of the park, pecking around and looking for food rather than move to another area of the park where more people were sat around enjoying their lunches. Eva mentioned the pigeon to Jason and they both wondered why the pigeon would not move to the area of the park with more food.

In humans when an individual is given the choice between two or more investment decisions people often stick with the investment they are already involved in rather than moving to a new investment (Novemsky & Kahneman, 2005). The initial investment of resources (money, time, energy etc) make switching investments less likely even if the new investment could produce a better outcome than the original investment – psychologists call this tendency to stick with the original investment the sunk cost effect (or bias) (Avila et al., 2010). One explanation of the sunk cost effect is that people have strong misgivings about wasting resources, a disposition that economists call loss aversion (Novemsky & Kahneman, 2005).

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Like humans, other animals (other than humans) often show the same decision behaviours. Several studies have investigated the sunk cost effect in pigeons (for a review see White & Magalhaes, 2015). In one study by Pattison and colleagues (2012) at the University of Kentucky researchers examined decision behaviour in pigeons (Columba livia). The pigeons were trained to peck at coloured keys with a potential reward (food). The pigeons could make a choice between pecking at one key for 30 pecks with a potential reward (e.g., a red key) or switching to another key (e.g., a green key) with a potential reward after 10 or 20 pecks. The pigeons showed a bias towards continuing with the sequence of pecks that they had invested in rather than switching to peck on another key that would produce a reward after fewer pecks. These results show that pigeons, like humans, show a bias to stay with an initial investment.

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A second study investigating decision behaviour in pigeons used a slightly different method to Pattison and colleagues (Watanabe, 2009). The pigeons in this study still had the same choice of sticking with an initial investment (in pecks) or switching. After training the pigeons to peck 30 times at one colour for food and 10 times to another colour for food Watanabe gave the pigeons the choice of switching to the 10-peck option after they had already started the 30-peck option. The experimenters found that three of the four pigeons showed a preference for completing the initial investment, despite the fact that switching to the second option would need pecks overall. So, like humans the pigeons in this second study were also susceptible to the sunk cost effect. Although these results are interesting the resources conceded that there may be one alternative explanation for the behaviour pigeons – switching to the new alternative would require the pigeon to move to the new colour.

Therefore, although most people would like to think that we do not act in the same way as other animals some of our behaviour is not uniquely human. Next time you find yourself sat on a patch of grass like Eva and Jason enjoying your lunch whilst watching the local wildlife and wondering why that pigeon is acting so strangely simple think “what would I be doing if I were that pigeon?” Afterall, the pigeon stubbornly sticking to its little patch of the park is not too unlike the way that our investment bankers and stock brokers behave.

The framing effect on climate change communication and policy making.

The framing effect on climate change communication and policy making.

Hannah and Thomas had both recently become interested in the environment and the politics of climate change after seeing a few news articles on the topic. Last week Hannah watched a very convincing documentary by a leading politician about the dangers of climate change and how humans are contributing to it. Thomas’ father worked in manufacturing and was skeptical about the contributions of humans to the change in environment, his father had always said that humans could not change the environment. Both Hannah and Thomas agreed that although they had different views on climate change there are mixed messages in the press.

Many controversial topics in the media are framed in such a way that benefit the person or company that is responsible for placing the message. The framing effect is one of the major heuristics (short-cuts in decision-making) that is used in the media. Framing works by wording a message in a persuasive way to influence the thinking of the reader (Kuhn, 1997). Communicators can make a choice to present the possible outcomes of a medical interventions as either (a) 80% chance of surviving (i.e., positively framed) or (b) 20% chance of death (i.e., negatively framed). On the topic of climate change communication researchers have found that a negatively framed (highlighting losses) message decreases individual intentions to behave environmentally whilst positively framed messages (highlighting the possibility of losses not materializing) produce a stronger intention to act in a pro-environmental way (Morton et al., 2011).

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One important factor for policy makers is that in the United States the public do not think that climate change is a concern, compared to other policies. Every January the Pew Research Centre for The People and The Press conduct a large poll of the public’s belief in which of 20 policies are of most important.  Between 2007 and 2009 the policy of “dealing with global warming” was consistently ranked at the bottom of 20 priorities (see the table below).

% considering each as a “top priority” January 2007 January 2008 January 2009
Strengthening the nation’s economy 68 75 85
Securing social security 64 64 63
Securing Medicare 63 60 60
Reducing crime 62 54 46
Reducing health care costs 68 69 59
Strengthening the military 46 42 44
Dealing with illegal immigration 55 51 41
Reducing middle class taxes 48 46 43
Dealing with global trade 34 37 31
Dealing with global warming 38 35 30

To aid in breaking through the communication barrier policy makers can tailor their messages to specific audiences by framing their messages. News reporters (e.g., news about an event), policymakers (e.g., employment statistics), advertisers (e.g. vitamin advertisements), and public speakers (e.g., conference talks) already use framing effectively.

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The framing of climate change information has been used successfully by John Kerry in 2004 and Obama in 2008. In his 2004 election campaign the Democratic party presidential candidate Senator John Kerry made strategic use of the public accountability framing (i.e., we are responsible for our environment). Senator Kerry compared the use of different frames (i.e., denial or acceptance of climate change) to the administrations use of intelligence to invade Iraq. In Obama’s 2008 presidential candidate campaign he made use of a sound bite “creating green jobs and fuelling economic recovery.” Creating green jobs in industry with alternatives to gasoline (or petrol) was heralded as a way to benefit the economy by creating more jobs and as a way to reduce greenhouse emissions. We can see here that Kerry chose to use a public accountability frame whilst Obama used an economic frame – both frames were used towards the same aim but tailored differently. Famously, Gore in his 2008 WE campaign picked a moral frame for climate change communication.

We have seen here that the framing effect can be used effectively by communicators for any message (e.g., advertising, policies etc). On the topic of climate change communication, we can put the framing effect to work by carefully considering how we would like to frame a message to our intended audience. In the cases of Hannah and Thomas they may have both seen the same information presented in different ways (i.e., the frame), the frames that they have encountered helped them come to their own decisions about climate change.

The confirmation bias in the forensic sciences.

James and Nicki always wanted to work in the forensic sciences. Whilst reading towards their undergraduate degrees they would borrow as many books from the library as they could on forensics and watch the popular television programmes about ‘forensic experts.’ One day when looking though an interesting book about case studies in forensics Nicki came across an interesting case study.

In 1988, Barry Laughman confessed during interrogation to the charges of rape and murder of his neighbour. The following day tests revealed that the person who committed the crime had Type A blood whilst Laughman had Type B. Aware that Laughman had confessed to the crimes the state forensic chemists proposed four theories (none of which were scientific) to dismiss the mismatch. Laughman was in due course convicted and sentenced to 16 years in prison. He was eventually released in November 2003 after a re-examination of the DNA evidence.

The case of Barry Laughman gives us a clear example of the influence of confirmation bias in the forensic sciences. The confirmation bias is shown when an individual ignores evidence that goes against what they believe whilst trying to confirm the belief (Dror, 2006). In Barry’s case the Virginian state forensic chemist ignored contradictory evidence and persisted in dismissing the mismatch in DNA evidence.

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The confirmation bias causes problems in all areas of decision-making. In the forensic sciences errors in decision-making, as caused by the confirmation bias can have severe consequences innocent people can spend a lifetime in prison, and the actual criminal can go on to reoffend. In the forensic sciences, the confirmation bias has been reported by the National Academy of Sciences (2009) in firearms, hair and fibre analysis, blood splatter, hand-writing and fingerprints (Kossin et al., 2013; Garrett et al., 2011).

In a recent study investigators found an interesting example of how the confirmation bias can influence the outcome of a forensic analysis (Ulery et al., 2012). The investigators gave forensic fingerprint examiners the same evidence twice, at approximately 10% of the time the examiners reached different conclusions (Ulery et al., 2012). Three of the reasons as to why the examiners reached differing conclusions are (i) examiners often receive direct communication from the police (e.g., letters, phone calls etc), (ii) cross-communication between examiners, and (iii) examiners overstating the strength of evidence.

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There are measures that can be taken to prevent the confirmation bias. The FBI’s Latent Print Unit revised their Standard Operating Procedures (SOP) (Cole et al., 2005). They adopted a programme of masked verification whereby fingerprint comparisons that involve a single print are masked-verified (i.e., in isolation with no further information about the print). The change in SOP prevents the second examiner from inferring the first examiner’s conclusion when two examiners individually examine the evidence (Office of the Inspector General, 2011).

Other measures that can be undertaken to prevent the confirmation bias include training all forensic examiners so that they know about cognitive biases. Just two of the courses help to install knowledge of cognitive biases are the FBI’s week-long Facial Comparison and Identification Training and the Australian government’s 2-day long facial comparison course. The linear examination of evidence by multiple examiners (Heyer et al., 2013), cross-laboratory verification (Kossin et al., 2013) and peer verification (Heyer et al., 2011) can all help in reducing the impact of the confirmation bias in the forensic sciences.

So, like James and Nicki if you are interested in working in the forensic sciences it is important to learn about the influence of cognitive biases on decision-making. Some private forensic companies have begun to provide training for their employees, and some governments have started to provide training. With adequate training one day we may be able avoid false convictions.

The recognition heuristic in advertising

The recognition heuristic in advertising

Anton and Sarah were shopping for their weekly groceries in their local supermarket. They bought their regular fresh fruits, vegetables, meat and dairy. As they started down the cleaning aisle Anton and Sarah were trying to decide which furniture polish to buy. They looked at the choice of polishes that were stocked in the aisle. There was a supermarket own-brand choice, two little known choices and a well-known brand. Anton remembered the well-known brand of furniture polish from a television advertisement with a comical cartoon character. There was little difference between the prices so Anton and Sarah decided to opt for the well-known brand that they remembered from the television advertisement.

Like the furniture polish brand companies spend great fortunes on making their products well-known. Millions is spent on brand communication with the goal of achieving the aided and unaided awareness of products. In 2007 and 2008 two large brands, Proctor and Gamble and Unilever spent $5.2 billion and $7.8 billion respectively. The substantial resources that are committed to promoting brand awareness shows the importance of establishing and retaining the awareness of a brand.

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Because of the importance of brand awareness many companies and academics have sought to understand the decision processes that are involved when a consumer chooses one brand over another. In industry, a major US automobile brand invested substantial resources to study how they might persuade customers to choose their brand over their competitors. They found that although their brand had excellent new vehicles as judged by independent raters two thirds of US consumers did not consider their brand (Hauser et al., 2011). The automobile company lacked a memorable advertising campaign.

In academia, researchers at the Max Planck Institute have suggested a rule-of-thumb (i.e., heuristic) that attempts to explain why consumers choose one brand over another (Gigerenzer & Goldstein, 1999, 2011). In a now famous experiment where participants were given the names of two cities (e.g., Oxford or Lannion), and then asked to judge which of the two cities had the largest population, participants reliably choose the city they knew (i.e. Oxford) over the city that they did not (i.e. Lannion). The researchers recorded this rule-of-thumb as… If one of two objects is recognized and the other is not, then infer that the recognized object had the higher values with respect to the criterion. The researchers called this rule-of-thumb the recognition heuristic. In consumer psychology, the recognition heuristic works equally well when consumers are asked to choose one brand over another (Thoma et al., 2013; Oeusanthornwattana & Shanks, 2010).

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In purchases with higher stakes (compared to simply buying a product in a shop) such as stock market investments the recognition heuristic is also of use. Imagine that you wish to invest a particular amount of money in stocks. If one person recognises a stock name over another they are more likely to choose the known stock over the unknown stock. Having a recognised stock name can increase the number of investors in a certain stock (Erdfelder et al., 2011).

In the case of Anton and Sarah shopping for their furniture polish it is clear why they choose the well-known brand over the unknown polish. The advertising campaign of the branded furniture polish with the comical cartoon character aided in the recognition of this brand thereby resulting in one more sale. If you multiple Anton and Sarah’s purchase by thousands or millions then you can clearly see the huge amounts of money that are involved. Many of us, like Anton and Sarah stick to brands we know simply because we know them, we buy the same cleaning products and food stuffs because of successful brand awareness campaigns that act on the recognition heuristic. Perhaps if we wish to avoid making unconscious choices based on the recognition heuristic we could simply try a different product, afterall the new product is not as we expect we can change back to our regular shopping pattern next time.

The home advantage bias and attribution bias during football refereeing in the German Bundesliga and the English Premier League.

The home advantage bias and attribution bias during football refereeing in the German Bundesliga and the English Premier League.

Andrew had always dreamt of a career in professional sports, unlike most people that want to work in sports he wasn’t interested in the fame and money. Andrew had spent the last 10 years working up to where he is. He had refereed for many small clubs and matches in the lower leagues. Now Andrew had been selected to referee for the top leagues. During his first big match Andrew was the top referee, he found himself in a stadium with 60,000 cheering and yelling fans. One of the star strikers went down after being tackled, he was holding is leg in pain. Andrew had a decision to make, was this a foul? He felt the pressure as the crowds began to yell. His decision would influence the outcome of the whole match.

Referees, judges and umpires all have an important role to play when it comes to sporting events. In a lot of sports the decisions of these officials can have a big impact on the position of a team in the league tables. The position of a team in the league table can furthermore affect the finances of a team, it’s investors and in some cases, it’s supporters.

One well known phenomena in sporting events is the home team advantage (Garicano et al., 2005; Rickman and Witt, 2008). The home team advantage phenomena states that a team playing in their home stadium will have an advantage over the opposing team simply because they are in their home stadium. The home team advantage has been shown in almost all sports. In football, researchers have investigated the home team advantage in the Italian Serie A league (Scoppa, 2008), the US leagues (Garicano et al., 2005), German Bundesliga (Kocher et al., 2004) and the English Premier League (Rickman and Witt, 2008). The decisions that referees make play a major role in the home team advantage.


In 2007 Perrersson-Lidbom and colleagues at the Universities of Stockholm and Munich had an opportunity to study the home team advantage in 24 matches after a serious of problems in the Italian Serie A and Serie B leagues. On February the 2nd 2007 supporters of the Italian football clubs Calcio Catania and Palermo Calcio clashed with each other and police in a violent and serious act of hooliganism. Filippo Raciti, a police officer in Catonia was killed during the rioting and around a hundred people were injured. Following the rioting police forced some of the clubs to play their home games without spectators. The researchers found that the presence of the supporters had an influence on the decisions of referees. When matches were played in front of spectators referees would punish away team players more harshly and home team players more lightly for the same offences (e.g., fouls). The home team advantage disappeared when there were no spectators. This reveals that although referees are expected to be unbiased factors such as cheering spectators can influence their decisions.

Another study of the home team advantage phenomena was conducted at the California Polytechnic State University by Richard Pollard in 2002. Between 1987 and 2001 there was a building boom for new sporting stadiums in United States. Pollard gathered data from 37 teams as they moved to new stadiums. If the home team advantage was in part to do with the stadium itself then the advantage should temporarily disappear when teams move stadium. Pollard found that 24% of the advantage of playing at a team’s home stadium is lost in the months after moving stadium. A large bit of the home team advantage remains which may be in part due to the referee’s decision-making.


One influence on a referee’s decision-making that many readers may find surprising are seemingly unimportant perceptual cues such at the height of a footballer (Quaquebeke and Giessner, 2010). Perceptual cues such as height can influence a referee’s decisions through a cognitive bias called the attribution bias. The attribution bias states that an attribute that does not implicitly seem important to the referee can influence a decision, when prompted the referee would not acknowledge that the attribute was an important factor in their decision. Quaquebeke and Giessner (2010) investigated what factors can influence the decision of a referee to punish a foul. They found that since height can sometimes be associated with the concepts of strength and aggression height can have an influence on decision-making. When an ambiguous tackle is seen by a referee the referee is more likely to attribute the wrongdoing to the taller of two players. Field data of decision-making by referees in seven UEFA Champion Leagues, three FIFA World Cups and the German Bundesliga league all support the attribution bias in refereeing.

So, although many of us are unlikely to find ourselves in the position of Andrew presiding over important decisions in a football match it is important to remember that we cannot always be unbiased in our decision-making. When Andrew finds himself deciding which of two players were in the wrong when going into a tackle he should bear-in-mind that factors that he is not aware of (e.g., height and crowd noise) play a role in his decision-making.

The framing effect in bonobos, chimpanzees and capuchin monkeys.

The framing effect in bonobos, chimpanzees and capuchin monkeys.

Noam Chimpsky had had a long day playing with the other chimpanzees. Noam was starting to feel hungry so headed towards the eating area of his enclosure. He had previously stored some fruit on a branch of a large tree. On the way to collect his fruit Noam noticed that one of the other chimpanzees had a new fruit that he had never seen before. Noam was determined to try some of the new fruit. He collected some of his fruit from his stash of fruit and went towards the other chimpanzee who was still carrying the new fruit. Noam wanted to trade some of his fruit for the new fruit. He stopped in front of the other chimpanzee, placed his fruit on the ground and tried to trade. The chimpanzee with the new fruit was happy to trade but wanted more fruit then Noam was willing to give. Noam decided that trading was a bad idea if he had give away too much fruit so he went off to his tree, with his fruit in hand and settled down to eat – afterall, Noam could try some of the new fruit another day.

Noam’s choice was to trade a small amount of food for the new fruit or to trade a lot of his fruit for the new fruit. In the end, Noam decided that the new fruit was not worth a lot of his fruit. Like humans Noam is susceptible to the framing effect. For Noam, his dilemma was to accept a negatively framed trade were he would lose out by giving away too much of his fruit or to broker a positively framed trade were he would give away a small amount of fruit for the new fruit.


The framing effect states that the manner in which options are presented (or framed) can influence how we evaluate choices. We evaluate the options relative to a reference point (i.e., the amount of fruit the Noam had to start with). Changes that seem to worsen the status quo (i.e., Noam giving away a lot fruit for the new fruit) are treated differently to changes that improve the status quo (i.e., Noam gaining the new fruit after trading for a small amount of fruit). The way in which we (and Noam) perceive the choice is important because we are more willing to invest in a choice that is positively framed, rather than negatively framed. The framing effect has been documented extensively in human decision-making in areas such as financial trading (Seo et al., 2010) and medical decision-making (Bornstein et a., 2001). However, as we have seen in the case of Noam other animals, other than humans also exhibit the framing effect. According to molecular-clock estimates our genus split with other primates around 23 million years ago (Schneider et al., 2001), which means that we share a common ancestor with other primates. We share some of our decision-making processes (e.g., the framing effect) with the other animals.

One study the sought to investigate the framing effect in other primates used 40 bonobos (Pan paniscus) and chimpanzees (Pan trogladytes) (Krupenye et al., 2015). The apes were required to make choices between a positively framed option that provided a preferred food item (fruit) and a negatively framed option with a different food item (peanuts). The apes completed 5 sessions of 12 trials on separate days. Both the bonobos and the chimpanzees choose the positively framed option more than the negatively framed option demonstrating that they were susceptible to the framing effect. Furthermore, male apes were more susceptible then female apes to the framing effect.


Capuchin monkeys (Cebus appella) have also shown the framing effect in several different studies (Chen et al., 2017; Lakshminarayanan et al., 2011). In their natural environments capuchins live in complex environments, they are socially sophisticated primates whose native environment requires careful management of their scarce resources. The study by Lakshminarayanan et al., (2011) found that capuchins are able to learn how to trade tokens for food. When trading tokens they are susceptible to framing effect for positively and negatively framed choices.

The results of the capuchin, bonobo and chimpanzee studies suggest that the mechanisms that drive the framing effect is evolutionarily ancient. Some of our ‘human’ economic biases are shared by Noam and the other primates throughout the primate order. These studies highlight the importance of comparative research in understanding the origins of cognitive biases and individual differences in human decision-making. To understand the human brain and decision-making we should complement of research by looking towards our distant relatives.

The sunk cost effect in customer loyalty schemes.

The sunk cost effect in customer loyalty schemes.

Gary was bored at work sat at his desk. He was wasting time before a meeting by browsing some of the popular shopping websites looking for a new book to read for his commute to work. Like many of us, Gary has several membership subscriptions for websites that he shops on a lot. The subscriptions that he buys for with a small annual cost guarantees Gary quick delivery, priority ordering and discounts on a large range of products. Since he was bored at work Gary thought that he’d take advantage of his subscription and order another book – afterall he pays for the service so why not make the most of it?

Almost all large companies have subscription membership schemes that offer their customers exclusive benefits. Mobile phone (cell phone), internet, utility (e.g., gas and electricity), insurance, and travel providers all offer their main services alongside additional schemes with small membership charges for exclusive offers. These schemes are everywhere. Subscription-based membership schemes are very profitable for most companies, and rely on customers opting for easy, lazy decision-making over well thought through decision-making. The historical cost of purchasing the subscription for a small fee is an important aspect of this easy decision-making.

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Traditional economic theories explain that when making decisions historical costs should be irrelevant, they should not factor into present decisions (Kramer, 2017). One of the most prominent cognitive biases were historical costs are an important influencer of decision-making is known at the sunk cost fallacy. Kelly (2004) summarized the sunk cost fallacy into two claims: i) individuals give weight to sunk costs in their decision-making (i.e., subscription costs), and ii) it is irrational for them to do so.

Two popular examples of subscription-based membership schemes that benefit from the sunk cost fallacy are the Bahn Card 50 and Amazon Prime. The Bahn Card 50 is the original customer loyalty scheme for Deutsche Bahn, the main German railway operator. The Bahn Card 50 was introduced in 1992, as of 2014 it had 5 million subscribers. The fee for the card is EUR 255 per year which gives the owner of the card a 50% discount on train fares. Like all of the other subscription-based membership schemes the owner of the Bahn Card 50 regards the subscription cost as a sunk cost (historical cost), in doing so they try to make the most of the scheme (Tacke & Firmer, 1992).

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Amazon’s subscription-based membership scheme is called Amazon Prime. Prime was introduced 2004. For a fee of $99 (as of November 2014) subscribers get faster delivery and priority to access to products on Amazon’s shopping website. Prime has become an important part of Amazon’s business model. After the introduction of Prime Amazon recorded a 150% increase in purchases by Prime members in the year immediately after joining the scheme. Prime is responsible for 20% of Amazon’ overall sales in the United States. For a company that makes billion in profits every year exploiting the sunk cost fallacy has proven to be very profitable.

The sunk cost fallacy is just one of many cognitive biases that influence decision-making. As we have seen above for people like Gary, and of course most of us, the sunk cost bias can affect the way in which we make decisions through subscription-based membership schemes. It is easier to think that since we have already paid a membership fee that gives us a discount we should take advantage of this and use it. If you sit back and think about the situation not exploiting the discount actually saves us more money than the discount would because we are not spending anything more. Big companies such as Amazon have learnt to use these cognitive biases, thereby making huge profits from our quick, easy and effortless decision-making.

The gambler’s fallacy, framing, anchoring and hindsight bias in judicial decision-making.

The gambler’s fallacy, framing, anchoring and hindsight bias in judicial decision-making.

Months before William had moved into a new house with his wife, Sarah. They were excited about their move to their new home. William and Sarah unlocked the door to their new house and waited for the furniture removal staff to arrive at the end of their driveway. As the removal van drove up the driveway the van struck the side of William and Sarah’s car, writing the car off. Several months later William and Sarah headed to court to claim for the damages of their car against the removal company. Over many years their lawyer had learnt to be careful in the way that she words her cases in court. Their lawyer asked the judges “How much would you award the plaintiff in compensatory damages?” rather than “We are claiming ‘x’ amount from the defendant.” William and Sarah’s lawyer had learnt that framing a case in such away could exploit the framing bias.

The judicial system is one of the most important systems in all countries with a court of law. Judges play a significant role in society deciding on the outcome of many cases and setting precedencies for future cases. The public expect judges to decide on the outcome of each case fairly, without systematic errors or bias. However, like any instance where a person must make a judgement or decision judges are subject to systematic biases.

One of the most important aspects of law for many people is immigration law. Asylum judges must make decisions that can determine the fate of the individual in court. The United States offers asylum to foreign national who can (i) prove that they have a well-founded fear of persecution in their own countries, and (ii) that their race, religion, nationality, political opinions, or membership in a particular social group is one central reason for the threatened persecution. In 2016 a study by Harvard academics investigated the use of heuristics and biases in the asylum courts of the United States (Chen et al., 2016). Chen and colleagues accessed data through a Freedom of Information Act request of 699 decisions that were made by 357 judges in 45 courts from 1985 to 2013. All cases in the US are handled on a first-in-first-out basis with no quotas as to how many individuals are granted, or not granted asylum. On analysis of the data Chen et al found that judges were subject to the gambler’s fallacy. Judges were more likely to grant (or deny) asylum after denying (or granting) asylum to a previous applicant. The judges presiding over the fate of the asylum applications believes in a representation of randomness, as such, they were more likely to alternate between granting and denying asylum.

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There are many other heuristics and biases that play an important role in the judicial decisions of judges in the US these include framing (as demonstrated by William and Sarah’s lawyer), anchoring, and the hindsight bias. In a joint study with Cornell Law School researchers at a major conference issued questionnaires to 167 federal magistrates. The questionnaires contained examples of cases that a court judge would preside over. From the responses to the questionnaires Guthrie et al (2002) found that judges were subject to anchoring in personal injury claims, framing in copyright action cases, and the hindsight bias in cases of medical negligence.

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A retrospective study by Rachlinski et al (2000) examined the effect of the hindsight bias on decisions in real court cases. The hindsight bias is a mental shortcut that states that we have an inclination, after an event has occurred, to see that the event was predictable. The researchers found that judges failed to appreciate the problems associated with judging an event from hindsight. One example (Chase v. Pevear) from the Supreme Judicial Court of Massachusetts decided that the trustees of two high risk investments should have known the outcome of the investments with a nearly omniscient ability. In the case of First Alabama Bank v. Martin the Alabama Supreme Court held that another group of high risk, high yield equities were speculative, as shown by the fact that trustees lost money by selling ‘at the bottom of the market.’ The courts in both cases assumed that the investors should have known that the price of the equities were recovering, and hence the trustees should not have sold them. One court even held a trustee liable for failing to predict the stock market crash of 1929.

The public expects our judges to make well balanced judgements and decisions without falling for cognitive biases. As humans tasked with make important decisions the judicial system is not isolated from any of the shortcuts that we use to make decisions. For William and Sarah seeking their compensation for the damage to their car the experience that their lawyer had with the framing bias came in useful. In many of other cases, such as with the example of the gambler’s fallacy in the asylum courts these cognitive biases and mental shortcuts can become problematic. We have seen how the framing bias, anchoring heuristic and hindsight bias are all important when making decisions, even at the highest levels of decision-making in society.




How do we make decisions quickly when placing bets in a casino?

How do we make decisions quickly when placing bets in a casino?

Like many tourists, Rebecca and Catherine always wanted to visit Las Vegas. They took in the dazzling sights of the bright lights, enjoyed the stage magic and drifted into one of large casinos. When they walked into the casino they were amazed by the sounds and lights. Catherine had always enjoyed the game of roulette so headed straight to the roulette table. Catherine placed three consecutive bets, losing all three. Watching this Rebecca quietly said to Catherine “Maybe you should try another game, the odds are against you winning.” To this Catherine replied “No, I have lost three times I am overdue a win, I will win soon.”

When we make decisions, we like to think that we make all of our decisions rationally. Casino environments are designed to be one of the most complex environments that we ever encounter, there are colourful, flashing lights, loud machines playing music and people cheering. It is hard to deal with the environment, tune-out and make clear decisions. To deal with complex environments the brain has developed a number of judgement heuristics. Judgement heuristics are mental short cuts that enable us to make quick decisions whilst ignoring part of the decision-making environment. One classic example of a judgement heuristic was given by Catherine above when speaking to Rebecca at the roulette table. The aptly named gambler’s fallacy, as demonstrated above can also be seen in the following classic scenario. Imagine that someone is flipping a coin multiple times, the coin lands head side up three times in a row. Someone watching the coin flipping is asked to guess whether the next coin will land head side up or tail side up. If the person guesses tail side up they are using the gambler’s fallacy, a belief that randomness follows a pattern. Many people believe that for the coin flipping to be truly random the outcome of a sequence of five coin flips must be something like HEADS-TAILS-HEADS-TAILS-HEADS. If an outcome of five flips is HEADS-HEADS-HEADS-HEADS-HEADS then the coin is overdue to land with the tail side up otherwise this is not random. The belief in a pattern of randomness persists in most areas of life.

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In a field study by researchers at the University of Navada 18 hours worth of overhead security camera footage of a roulette table was obtained from a large Reno casino, consisting for 904 bets (Sundali & Croson, 2006). The researchers watched all of the bets and coded the patterns of betting for every gambler that sat down at the table. On an American-style roulette table there are 36 different sections of the roulette wheel, coloured as red or black (European-style roulette has 37 sections). Players can bet on more than one number and colour by placing their chips on the corners or side of each of the numbered squares. If a gambler bets randomly then after the researchers coded each bet there should be a 2.6% chance that a bet would fall on each number. The researchers found that after an outcome of RED-RED-RED gamblers responded with a gambler’s fallacy type logic by betting on BLACK. Like Catherine above and the coin flipping above, the gamblers believed in a representation, or pattern of randomness.

A study by Gal and Baron in 1996 investigated the gambler’s fallacy in a laboratory-based experiment. Gal and Baron examined whether a change in betting strategy was due to boredom by asking participants why they choose to bet in the way that they did. Most of the participants in the Gal and Baron study responded by saying that they were attempting to maximize their earnings by using the gambler’s fallacy-type logic. It would appear that the gambler’s fallacy is not simply caused by boredom.

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Besides gambling at the roulette table other casino games in which heuristics play a major role include the craps table and fruit machines. One prominent heuristic at the craps table is the belief in illusory correlations. When rolling the dice at the craps table gamblers often blow on the dice for luck or roll the dice softly in the belief that a low number will come up, and roll the dice harder when they want a higher number (Griffths. 1994). These gamblers believe that if a number comes up that they did not want then they did not through the dice hard or soft enough.

Cognitive short cuts as heuristics are used in every area of life where decisions need to be made. With respect to gambling, heuristics have been documented in fruit machine users, and at the poker, blackjack, roulette and craps tables. Even when betting on horse or dog races gamblers reliably use these mental short cuts (Terrell, 1998). I have highlighted two of uses of heuristics above, namely the gambler’s fallacy and illusory control. Like Catherine above many people fall for these heuristics and make sub-optimal decisions based on incorrect beliefs. When it comes to gambling, there are two ways to avoid these cognitive traps. If gambling alone take your time to think about the decisions that you want to make. When gambling in a group of with a friend you could listen to the advice of your friend as Catherine should have above.

Heuristics in food choice, how do we choose what to eat and how much to eat?

Heuristics in food choice, how do we choose what to eat and how much to eat?

Ian and Jill were on their lunch break from a long and busy day at work. They both worked in the same office and liked to get out of the office environment for an hour to eat their lunch. Just like on any other day Ian and Jill walked from their office down the street to the local food court. Neither Ian nor Jill were picky eaters they’d just eat whatever they felt like eating. As they got to the food court they both looked around at the large number of restaurants and cafes that they could choose from to purchase lunch. Jill looked at Ian and said “Well, where should we eat today? There are so many choices here.”

In many countries, we live in very food-rich environments. In supermarkets, we can choose from thousands of different items of food, and from this we can combine these food items into tens of thousands of different dishes, albeit with a little help from our recipe books. When we go out to eat like Ian and Jill we encounter vast numbers of dishes to choose from. Most consumers are not aware of it but on a day-to-day basis we make an estimated 200 food choices (Wansink & Sobal, 2007). Of course, we don’t like to agonise of what to eat for lunch so we like to use simple effortless strategies. The lack of any deliberation leaves us susceptible to ‘rules-of-thumb’ (mental shortcuts known as heuristics).

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So, what heuristics are involved in food choice?

When stood at the doors of the food court looking at the dozen or so restaurants ahead of us many us like to consider one attribute of food to be the most important (e.g., low in calories, inexpensive, convenient etc.). A heuristic known as the lexicographic decision rule, LEX for short, accounts for this by saying that we make our decision based on the food that satisfies an attribute the most (e.g., the food alternative with the lowest number of calories). If two foods are equal on the first attribute the second most important attribute of the food acts as the tie-breaker (e.g., convenience and inexpensive).

Academics at the Max Planck Institute in Berlin have investigated the lexicographic decision rule in food choice (Schiebehenne et al., 2007). In 2006 the researchers conducted a study at the Potsdamer Platz Arkaden, a large shopping centre in Berlin. They had students rate food along 38 different attributes (e.g., price etc.). The researchers then used the information from the students in a study were participants were shown 10 photographs of food dishes with the name of a restaurant, the price and type of food (e.g., Indian or Italian food). Participants choose a food and rated their choice on the 38 attributes. Example foods from this study include Big Mac burgers, Bockwurst with potato salad, chocolate muffins, lasagne, sushi and pizza. The results of the study found that participants did mainly consider only one attribute when making their decision about which food to eat. When tied on the single attribute with another food they simply considered the second most important attribute (is this low-calorie food as inexpensive as the second low-calorie food?). The lexicographic decision rule was able to predict the food choice that participants in this study made after considering what single attribute was of the most importance to a participant.

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Another strategy for food choice that many people use is evident in people who keep to strict diets for religious reasons or health reasons. Experienced dieters often practice rules that become habitual, for example, avoid all chocolate. When practiced frequently dietary rules become internalized and can be used as rules-of-thumb, however, this lack of conscious deliberation can leave us susceptible to other cognitive processes, even when trying to avoid sugary chocolate. The size of plates and serving utensils are just one of the things have been shown to influence how much we eat when serving our own food. People tend to serve themselves more, and eat more from larger bowls and plates than when given smaller plates, even when given the option to return for a second serving had they received a small plate (Gier et al., 2006). As for beverages, when given a tall drinking glass people pour more than when they have a short glass (Wansink & Van Ittersum, 2003).

For most of us who do not follow a particular diet and like to try different foods the lexicographic decision rule can be a great decision aid because when using this we don’t have to consider a lot different competing attributes of a food dish. In the case of Ian and Jill walking into the food court satisfying their joint desire to eat healthy the lexicographic decision rule works well to help them avoid the many fast food outlets. However, for those who are aiming to avoid chocolate (or any other single food) we should be aware of the other cognitive processes at work, after all a large plate does not mean that we all have to fill the plate completely.

Dan Edgcumbe