How important are cognitive

biases in determining our rationality in decision- making?

EXTENDED PROJECT QUALIFICATION

Jenny Guo | Subject Supervisor: Simon Harrison | 2017

Contents

Abstract Page 2

Introduction Page 2 -3

Cognitive Biases

Anchoring Bias Page 4 - 9

Framing Effect Page 9 - 12

Confirmation Bias Page 12 - 15

Neglect of Probability Page 16 - 19

Conclusions Page 20

References Page 21- 22

Bibliography Page 22– 23

Appendix Page 23 - 25

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Abstract

Background. Behavioural Economics applies psychological thinking to the economic decision-making of individuals and establishments. One aspect of behavioural economics is cognitive biases that are prevalent in many aspects of our lives without realisation. Advocates of Behavioural Economics argue more attention should be provided to cognitive biases that are often neglected and undermined under standard economic thinking. Aim. The aim of this piece is to challenge the standard economic thinking and highlight the importance of cognitive biases but also to examine the extent of their importance in making rational decisions.Method. Through academic research and social experiments on anchoring bias, framing effect, confirmation bias and neglect of probability bias this will help to stress the power and impact of cognitive biases in our lives.Results. The data suggests that humans are not as rational as they believe and highlights the ease for us to be blinded by systematic errors resulting in flawed decisions. The effects of the biases are demonstrated in the social experiments and challenge the standard economic assumption that humans have complete self-control and are rational. Conclusions. There needs to be more awareness of cognitive biases to enable us to make more effective and rational decisions in our lives.

Introduction

Humans tend to overestimate their rationality; a study run by ClearerThinking.org found over 81% survey takers believed they were more rational than 50% of the population and the median percentage of people believed they were more rational than 60% of the population. Likewise, in 1

driving people tend to believe they are above average on numerous skills, a study by Williams found 673 out of 909 motorists believed they were better than the average driver. Clearly, it is 2

not possible for such a high proportion of drivers to be seemingly better than the average driver. Consequently, suggesting that people are not as rational as they believe due to systematic biases resulting in their overconfidence. When people are asked to define a ‘rational person’ the most frequent, reoccurring responses include:

• Making decisions to optimize personal outcome

• Someone who makes good decisions

• Someone who uses logic

Clearer Thinking, how rational do people think they are, and do they care on way or another? (2015).1

Elanor Williams and Thomas Gilovich, Do people really believe that they are above average? Journal of 2

Experimental Social Psycholog y, (2008) 44, pg 1121–1128.

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• A sensible person

Figure 1: How rational do people think they are? (Clearer Thinking, 2015)

Kahneman states, “The confidence that individuals have in their beliefs depends mostly on the quality of the story they can tell about what they see, even if they see little.” This is a vital 3

reflection of how unaware we are of the ease with which decisions and judgments can be blinded by systematic errors known as, cognitive biases. It is not just the average human that neglects cognitive biases – even economists, politicians and bankers do so without realisation.

There is no doubt that Economics plays an important role in modern society with news reports constantly mentioning ‘Economics’, it is no surprise that a large proportion of the public would either have used the term or at least heard of the term. However, the term ‘Behavioural Economics’ often brings blank, confused responses from people who have never heard the term and are unable to differentiate the difference between Behavioural Economics and Standard Economics.

Behavioural Economics involves the use of psychological experimentation to examine theories in human decision-making. It has made numerous important discoveries regarding cognitive biases and the way people think and feel. It is a relatively new avenue with many questions yet to be explored but it is my purpose to bring greater awareness of these biases, thus enabling you to pursue more effective and rational decision-making in all aspects of life.

Daniel Kahneman, 2011: Thinking, fast and slow. 1st ed. New York: Farrar, Straus and Giroux. 3

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Cognitive BiasesFollowing intensive research by Kahneman, Tversky and others, there have been discoveries of over one hundred cognitive biases. Cognitive biases are deviations from the ‘norm’ or a ‘rational decision’ placing us in situations that may be inaccurate and distorted. The following study will focus on four main cognitive biases: anchoring bias, the framing effect, confirmation bias and the neglect of probability. In examining each cognitive bias, the plan follows a literature review, reproducing an existing experiment and creating a new experiment.

A) Anchoring Bias

Standard Economic Hypothesis: Initial values or numbers do not have influence in our decision-making.

My Hypothesis: When we are presented with an initial value or number there is a bias to lean towards this value in our decision-making.

Anchoring effects are prominent in our comparative nature arguing that we may rely particularly on one trait after being given a piece of information. The trait could be a value or number but it affects our judgments as we adjust it dependent to the given ‘anchor’. A study by Strack & Mussweiler examined a group of students who were given two different anchors; either Gandhi died before or after the age of 9, or before or after the age of 149. Undoubtedly 4

both these anchors were evidently incorrect answers, however, the group given the anchor of ‘age 9’ guessed an average of 50 years old compared to an average of 67 years old for the group given the anchor of ‘140’.

The implications of the anchoring effect results in it often being used within the marketing industry to help boost consumer sales through three predominant methods: multiple unit pricing, purchase quantity limits and initial price setting.

Multiple Unit Pricing: Multiple unit pricing is a technique used to stimulate sales by charging the same unit pricing for a ‘sale value’. For example, a regular item is priced at £1.25 individually but promoted as 4 for £5 – they single unit pricing remains the same in both cases. A study conducted by Brian Wansink, Robert Kent and Stephen Hoch compared the percentage

Fritz Strack and Thomas Mussweiler, Explaining the Enigmatic Anchoring Effect: Mechanisms of 4

Selective Accessibility. (1997)

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change in sales through the single unit or multiple unit pricing over 13 different products. 5

Their results found a 32% increase in sales through multiple unit pricing – by labeling products as ‘2 for $1.50’ instead ’75 cents’ draws more attraction for consumers to purchase more products. (See Table 1)

The use of multiple unit pricing is an example of anchoring bias in action, as when we purchase single units we tend to have an approximate idea of the pricing and have an initial anchor to help influence our purchases. However, when we are presented with multiple units our initial anchor becomes distorted leading us to purchase more than we expected. Although, it must also be noted that the results are not representative since not all consumers fully understood the process of multiple-unit pricing and may have been confused about the price deal. Therefore, this demonstrates that it was not solely the anchoring bias in action and the recall bias also led to systematic errors as people failed to properly understand the price deal.

Figure 2: The impact of multiple unit pricing on supermarket sales (Wansink, Brian, Robert J. Kent, and Stephen J. Hoch, 1998)

Purchase Quantity limits: Does a purchase quantity limit influence the quantity we purchase? For example, retailers may try to limit the number of purchases customers can make to create the illusion of ‘limited stock’ creating greater demand for their goods from an easy advertisement trick using the principles of the anchoring bias.

Wansink, Kent and Hoch conducted an experiment to test this theory out with purchase limits and a relatively small discount (10 cents off the original price of 89 cents) of Campbell’s soups across three different supermarkets. Their results found that with no limits shoppers purchased 6

Brian Wansink, Robert Kent, and Stephen Hoch, An Anchoring and Adjustment Model of Purchase 5

Quantity Decisions, (1998) Journal of Marketing Research, 35:1 (February), 71-8

Brian Wansink, Robert J. Kent, Stephen J. Hoch, 1998: An Anchoring and Adjustment Model 6

of Purchase Quantity Decisions, Journal of Marketing Research, 35:1 (February), 71-81

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3.3 cans of soup in contrast to purchases of 3.5 and 7.0 cans when limits of 4 and 12 were introduced. Higher quantity anchors encouraged more purchases compared to a lower anchor particularly prominent in the effect of a limit of 12 that increased sales by 112% for each buyer. This demonstrates the effect of anchoring bias as consumers deviate from the normal spending patterns, due to the influence of higher anchors encouraging them to spend more compared to when there are no limits.

Figure 3: The impact of purchase quantity limits on supermarket sales (Wansink, Brian, Robert J. Kent, and Stephen J. Hoch, 1998)

Initial Price Setting: Firms can establish an initial high price for their products and services often inspired by the competitor’s suggested price. This will allow more leeway to cut the price by a large proportion but still, make a profit at the lower price. Despite, the final price still being expensive consumers will be influenced by the anchor of the initial price making them more inclined to purchase.

This is demonstrated by the diagram on the right where it states prices have been cut by $300 but only the 64GB iPad has truly been reduced by this price. The emphasis on the price cuts encourages consumers to purchase the iPad. 7

Reproducing an existing experiment

I reproduced an existing experiment from Dan Ariely, Drazen Prelec and George Loewenstein that used social security numbers as an anchor in the value that MIT students placed on

Anthony Pensabene Harbored Revenue: How to Use Anchoring to Your Advantage (2013).7

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Figure 4: iPad pricing through marketing techniques (Anthony Pensabene, 2013)

different items. The experiment was conducted across 90 people and asked participants to 8

write the last two digits of their mobile number on a piece of paper. (For experiment method, see Appendix A) Participants were then presented with an image of a bottle of wine and asked to state the price they would pay for this bottle of wine.

Results

Confidence level test

To determine whether two populations means are different and examine how statistically significant my results are within intervals, a Z test is required to examine the significance levels.

Key = The Mean of X Values

= The Mean of Y Values

N = The Number of Values

= Standard Deviation of X Values

= Standard Deviation of Y Values

Following this equation, I calculated a Z score of 4.55 for the results to conclude there is a 5% significance level for the results which means the chances of a result like this happening randomly are less than 5%. (For calculations, see Appendix B)

The key findings are that people with higher digits between 51-99 were prepared to pay 1.73 times greater for the average price of the wine bottle. They paid £32.70 as opposed to people with lower digits of 00-50 that were willing to pay an average of £18.90. The anchoring bias was undoubtedly prevalent in the experiment as once people were presented with the initial digits of their number it influenced the price they were willing to pay. However, the results also found

Last two digits of phone number range

Average price they would pay for wine bottle (£)

1) 00-50 £18.90

2) 51-99 £32.70

Dan Ariely, George Loewenstein, Drazen Prelec and Tom Sawyer, The construction of value (2004)8

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that not everyone was influenced by the initial value as there were people with high digits that were willing to pay a low price, but the average of the results found the overall trend supported my hypothesis.

New Research

Following this experiment through validating the existing results I executed my own experiment examining life savings. 100 participants were split into two equal groups and asked the following question: (For experiment method, see Appendix C)

By the age of retirement, what level of lifetime savings do you hope to have?

Each group was provided with three control facts:

1. The average retirement age is 65 for men and 64 for women. 9

2. The new state pension is £159.55 per week.10

3. The average life expectancy at 65 years old is 84 for men and 86 for women.11

The independent factor was the fact on the average savings in the UK. Group A was told the average savings in the UK by age of retirement is £10,000 whilst Group B was told the average savings is £100,000.

Results

Confidence level test

I calculated a Z score of 3.47 for the above results to conclude that the chances for the results to happen by random chance are less than 0.1% or one in a thousand. (For calculations, see Appendix

Average life savings (£)

Group A 90,700

Group B 255,940

Retirement Age, Wikipedia. <https://en.wikipedia.org/wiki/Retirement_age>9

The new state pension, Gov. UK. <https://www.gov.uk/new-state-pension/what-youll-get >10

Office for National Statistics, Life Expectancy at Birth and at Age 65 by Local Areas in the United Kingdom: 11

2006-08 to 2010-12

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D) Therefore, the results suggest there is a real difference between Group A and Group B, and hence the anchoring bias does make a difference.

The difference in the average savings in Group A of £90,700 compared to Group B of £255,940 was rather stark. The anchoring bias was undoubtedly prevalent in the experiment as once participants were shown the false facts of average savings, the initial fact anchored their decision as participants were likely to adjust their savings based on the value they saw. The results found that participants presented with the fact of £10,000 on average had lower expectations for lifetime savings. Whereas participants presented with the fact of £100,000 had higher expectations and in turn gave higher values for their lifetime savings suggesting the anchoring bias does have an effect in our rationality in decision-making. Perhaps if the participants had not been shown the false fact, then their expectations of lifetime savings would be different but the subconscious influence of the initial value affected how rational they are.

The majority of people will fall prey to this bias making it virtually impossible to overcome however by being made more aware of the anchoring effect - the tricks used in a supposedly ‘good deal’ becomes more evident in your daily lifestyle. In policy making, Governments can use the principle of anchoring bias to encourage people to save more, especially in the UK with household savings being at an all-time low of 3.3%, by providing higher anchors to people 12

potentially saving rates could increase.

B) Framing Effect

Standard Economic Hypothesis: The way something is presented makes no difference to our final decision since humans are rational beings.

My Hypothesis: The way something is presented affects our response or chosen decision to it.

Positive or negative aspects of something can be emphasised through the way it is presented known as the Framing Effect. Shlomi Sher and McKenzie interpret the framing effect as “equivalent descriptions of a decision problem lead to systematically different decisions.” This 13

technique is often used within marketing by retailers to help generate sales and increase the desirability of their product from consumers. A famous experiment called the ‘Asian Disease Problem’ conducted by Tversky and Kahneman in 1981 demonstrated how people could be risk-averse or risk-seeking depending on the presentation of a decision. 14

Angela Monaghan, UK households’ savings fall to record low in warning sign for economy (2017)12

Shlomi Sher and Craig McKenzie, Framing Effects and Rationality In The Probabilistic Mind: Prospects 13

for Rational Models of Cognition (2008) ed. Chater and Oaksford. Oxford: Oxford University Press.

Amos Tversky, Daniel Kahneman, The framing of decisions and the psychology of choice, Science, New 14

Series, Vol. 211, No. 4481. (1981), pp. 453-458.

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The Asian Disease Problem:Imagine that the U.S. is preparing for the outbreak of an unusual Asian disease, which is expected to kill 600 people. One possible program to combat the disease has been proposed. Assume that the exact scientific estimate of the consequences of this program is as follows:

Some subjects are then presented with options A and B: A: If this program is adopted, 200 people will be saved. B: If this program is adopted, there is a one-third probability that 600 people will be saved and a two-thirds probability that no people will be saved.

Other subjects are presented with options C and D: C: If this program is adopted, 400 people will die. D: If this program is adopted, there is a one-third probability that nobody will die and a two-thirds probability that 600 people will die.

The results found that 72% of subjects presented with options A and B tended to prefer the ‘sure’ option of A and were risk-averse. Whereas 78% of subjects presented with Options C and D chose Policy D and were more risk-taking. Despite both the option sets having equal outcomes when compared people’s preferences for choices changed, due to the differences in the way the options were framed.

The framing effect can even be implemented within food industries to boost consumer purchases. A study conducted by Levin and Gaeth (1988) presented beef in two ways: “75% lean” or “25% fat”. The “75% lean” choice was received more positively by the public through higher ratings. This is known as attribute framing when a single aspect of an object is presented 15

either as a positively valenced proportion or negatively valenced proportion. By focusing on the positively valanced proportion of an object it will be received more favourably as opposed to the negatively valanced proportion.

A group of cognitive neuroscientists explored the link between the framing effect and the human brain through fMRI (functional magnetic resonance imaging) by asking 20 university students to participate in a financial decision-making task. Initially, participants received a message 16

indicating the quantity of money they would receive, then

Irwin Levin and Gary J. Gaeth. How consumers are affected by the framing of attribute information before and 15

after consuming the product (1988).

Benedetto De Martino, Dharshan Kumaran, Ben Seymour, Raymond J. Dolan, Frames, Biases and 16

Rational-Decision Making in the Human Brain (2006)

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they had to choose between a ‘sure’ or ‘gamble’ option presented in two different frames.

The data results showed that there is increased activation in the amygdala – the emotion centre in the brain for subjects that were risk-averse in the ‘Gain frame’ but risk-seeking in the ‘Loss frame’. This demonstrated that our affect heuristic in our 17

emotional system was a key driver in the framing effect.

Reproducing an existing experiment

I reproduced the study conducted by Levin and Gaeth to examine the effects of positively and negatively valanced proportion in decision-making. 140 participants were presented with two choices for purchasing a chicken fillet – a positive frame ‘25% lean’ or a negative frame ‘75% fat’. (For experiment method, see Appendix E)

Results

Although the two choices were same, results found that 84% of participants preferred the ‘25% lean’ choice due it being framed more positively desirable compared to the ‘75% fat’ choice. The results suggest that people are susceptible to the framing effect and the way choices are framed does make a difference in decision-making even if they are all the same choices.

New Research

To further examine how susceptible people are to the framing effect, I conducted an experiment on 140 people regarding charitable donations. (For experiment method, see Appendix F) All participants were posed the following question:

Imagine you want to make donations to Oxfam Charity. Choose from below the donation slip that you would like to apply for.

• A weekly donation of £1.20• A monthly donation of £5

Choice Number of participants Percentage (%)

25% Lean 117 84

75% Fat 23 16

Dharshan Kumaran , Ben Seymour, Raymond J. Dola, Frames, Biases, and Rational Decision-Making in 17

the Human Brain by Benedetto De Martino. (2006)

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Figure 5: Gain versus loss frame (Martino, Kumaran, Seymour, Dolan, 2006)

• An annual donation of £60

Results

The results were interesting as although the framing effect appeared to still exist to an extent, it was statistically more limited. The most popular choice was a monthly donation of £5 with 45% of participants picking this choice. However, the most popular choice was relatively narrow to the second most popular choice with 35% of participants choosing an annual donation of £60 and surprisingly only 20% of participants chose a weekly donation of £1.20. The theory of framing effect would expect the £1.20 choice to be most popular due to it being framed to be the ‘cheapest’ choice for participants, yet the results are not consistent with the theory. I have four potential explanations for the results.

1. People prefer values that don’t involve pence because the extra pence makes people think harder about the money aspect. The extra twenty pence may not be a lot but psychologically it does to the donor. Perhaps if the weekly donation is changed to £1 or even £2, then the percentage rate may increase. For example, research has found that consumers prefer selected round prices in self-pumped gasoline purchases 18

2. People didn’t choose the weekly donation because the donations are too often for their liking and the annual donation appears too expensive, therefore they end up choosing the monthly donation of £5. People have the tendency to select the middle option known as the centre stage effect. For example, a study in 2000 by Shaw, Bergen, Brown & Gallagher found that people preferred the middle options, as opposed to the extremes when choosing from similar options. 19

3. People are actually rational and they did not fall prey to the framing effect. Participants realise that the different choices are essentially the same, therefore it does not make a difference in which choice they pick. This could explain why 35% of participants chose

Choice Number of participants Percentage (%)

Weekly donation of £1.20 28 20

Monthly donation of £5 63 45

Annual donation of £60 49 35

Michael Lynn, Sean Masaki Flynn, Chelsea Helion, Do Consumers Prefer Round Prices? Evidence from 18

Pay-What-You-Want Decisions and Self-Pumped Gasoline Purchases, Journal of Economic Psychology, 22, vol. 36 (2013).

Jerry Shaw, Jon Bergen, Chad Brown, Maureen Gallagher, Centrality preferences in choices among 19

similar options. (2000)

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the annual donation of £60, despite it being framed to be more ‘expensive’ compared to the other choices.

4. People are altruistic in the context of charity donations. The framing effect does not make a substantial impact on the decisions they make. Instead of following their brain, people follow their hearts and don’t mind making a donation of £60. Research by Batson in 1989 presents evidence that suggest humans are capable of empathy and caring for others in needs supporting the empathy- altruism hypothesis. 20

For organisations and firms, by understanding the framing effect it could be used to their advantage by presenting choices in a desirable way to consumers. For consumers the next time we are given numerous choices we can also be smarter in realising they are the same choices simply disguised in different ways.

C) Confirmation Bias

Standard Economic Hypothesis: Humans will seek out all appropriate information in enabling them to make a rational decision to provide the optimal satisfaction.

My Hypothesis: People tend to evaluate information that coincides with their personal opinions and often will overlook information opposing to their beliefs.

‘‘A person suffers from confirmatory bias if he tends to misinterpret ambiguous evidence as confirming his current hypotheses about the world”, Rabin and Schrag (1999). The dominant 21

prevalence of the confirmation bias can lead to perfectly capable people in making poor decisions. Investors often suffer from the confirmation bias by being more likely to ask positively framed questions to confirm their beliefs. Therefore, by narrowing themselves to only confirmatory questions they neglect disconfirming data and can fall into the trap of analysis that provides them with a false sense of confirmation.

In the early 1990s, IBM employees neglected adverse signs due to being convinced their operating system would achieve industry status. In reality, by 1993 the stock slipped to $10 per share and IBM had to make numerous cuts to their labour market. They failed to 22

acknowledge the competition from Microsoft Windows and were assured that the positive developments confirmed they were formulating a comeback. Failure to recognise the confirmation bias can result in negative long-term consequences even for experienced

Daniel Batson, How social an animal? The human capacity for caring, American Psychologist, 45 (1990) 20

pg 336-346

Ben Lockwood, Confirmation Bias and Electoral Accountability. (2015)21

IMCA, The Investment Advisor Body of Knowledge + Test Bank: Readings for the CIMA. John Wiley & Sons. 22

(2015)

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professionals.

A prime example of confirmation bias is demonstrated in the retracted Rolling Stone article ‘A Rape on Campus’ covered by Sabrina Erdely. The article describes a group sexual assault at 23

the University of Virginia on a student identified by ‘Jackie’. The fatal mistake is in Erdely’s pre-existing assumptions that went against the fundamental principle of journalism: tell the truth. Erdely and her team were so fixated on their result of the story that they overlooked the flaws in Jackie’s evolving story, memory lapses and failed to corroborate their source. A failure that was avoidable is evidently overwhelmed by the confirmation bias that consumed Erdely and her team to ignore opposing evidence to their beliefs.

Reproducing an existing experiment

To examine the confirmation bias, I showed 140 participants the following popular logic puzzle. (For experiment method, see Appendix G)

Results

The results suggest the logic puzzle is a classic example of the confirmation bias because most participants failed to choose the correct cards (A, 7) – only 14% of participants did so. Instead,

Choice Number of Participants Percentage (%)

A, 4 88 63

A, 7 26 19

Q, 4 20 14

Q, 7 6 4

Edward Morrissey, Rolling Stone and the perils of confirmation bias. (2015)23

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the most popular cards were (A, 4) with 63% of participants choosing this choice. People chose ‘4’ to confirm the hypothesis if they found an accompanying vowel and they chose ‘A’ to confirm if it had an even number on the other side. Conversely, many failed to identify ‘A,7’ as the correct choice as by turning over the ‘7’ card if there is a vowel then the hypothesis would have been wrong but if it did not then the hypothesis was correct. The existence of the confirmation bias causes people to be more likely to confirm the hypothesis than to challenge it directly.

New Research

In another experiment I created a profile for an imaginary character, Ben, as shown below:

• Very good with technology and analysing patterns• Good at observing things• Able to communicate freely with anyone• Ability to initiate interest of others• Enjoys getting involved with things• A good listener• Likes having control

The characteristics for Ben was chosen to fit both the profiles of a good manager and a good computer designer. By having characteristics that overlapped both job profiles it would test to see if participants would observe the qualities that did not necessarily fit the provided job role or if they would focus more on positive qualities.

The ‘good manager’ skills consisted were adapted from an online management site :24

1. Ability to initiate interest of others2. Enjoys getting involved with things3. Likes having control 4. Able to communicate freely with anyone

The ‘good computer designer’ skills were adapted from an online creative site :25

1. Very good with technology and analysing patterns2. A good listener

PHD in Management, 25 Qualities and Characteristics of a Good Manager (2011) <25 Qualities and 24

Characteristics of a Good Manager, http://www.phdinmanagement.org/25-qualities-and-characteristics-of-a-good-manager.html>

Will Gibbons, 9 traits of a great designer (2016) <http://www.creativebloq.com/career/9-traits-great-25

designer-71613188 >

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3. Good at observing things

The same profile of Ben was presented to 140 people however the questions varied between two

equal groups of 70 people. (For experiment method, see Appendix H) Group A was asked ‘Would Ben be a good computer designer’, whereas the Group B were asked ‘Would Ben be a good manager’.

Results

The results convey that people are susceptible to the confirmation bias - 63% of participants in Group A agreed yes whilst even more strikingly 90% of participants in Group B agreed yes. For both groups, participants focused on seeking characteristics that confirmed the statement whilst failing to acknowledge contradicting characteristics.

An experiment conducted by Bruner and Potter in 1964 tested 89 different subjects divided into 9 groups. The subjects were shown blurred images that were gradually brought into clarity 26

and focus. The range of blur and time exposed to the image varied for different subjects, however, the exposed image being stopped was at the same point of focus - neglecting the starting point and pace of the focusing process. The results found that subjects exposed to viewing at a severe-blur stage less than a quarter correctly identified the images compared to those viewing at a light-blur stage with over half correctly identifying the image. Bruner and Potter concluded that ‘the longer the exposure and the worse the display, the greater the effect’. Those who form their initial hypotheses on weak evidence have greater difficulty interpreting better information that contradicts their initial hypotheses.

The confirmation bias is extremely important because by being more aware of the pitfalls like being more prone to seeking information that confirms our own opinions we can make more rational, better decisions by not neglecting contradictory information in our decision-making.

D)Neglect of Probability

Standard Economic Hypothesis: Humans are rational enough to consider the probabilities of certain events when making decisions.

Yes (%) No (%)

Group A 63 37

Group B 90 10

Matthew Rabin and Joel Schrag, First Impressions Matter: A Model of Confirmatory Bias. The Quarterly Journal 26

of Economics (1999) 114(1),37-82.

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My Hypothesis: People have a tendency to neglect the probability of an event taking place in reality and overestimate the risk when making a decision, especially under uncertainty.

The thought of a plane crash or a terrorist attack frequently sends anxiety and fear for many triggering strong emotions. The reality of this happening is actually an extremely small probability. In fact, Brits should be more afraid dying in a car crash as the odds are 1 in 36,512 versus dying in a plane crash of 1 in 3.5 million (The Economist, 2015). Yet we fear a plane 27

crash over a car crash more, why is this? Although the probability of a plane crash is relatively low the consequences are so grand and horrific that it triggers strong emotions causing us to overreact.

Following the aftermath of a terrorist attack, it is natural for us to become more cautious and demand governmental actions in response. The act of terrorism often creates numerous, unwanted ‘ripple effects’ by the demands for more security that may actually be counterproductive. As of 2017/18, the budget for the Office for Security and Counter Terrorism in Britain is estimated to be at £824 million (Home Office, 2017) yet the effectiveness of the 28

‘Prevent Strategy’ introduced in 2003 is questionable. David Anderson stated certain elements of Prevent were “ineffective or being applied in an insensitive or discriminatory manner”. The 29

strategy intended to help stop people supporting or converting to terrorism has been criticized as being counterproductive in preventing people from wanting to share information with the police. It is interesting to note the odds of dying in any terrorist attack globally is 1 in 9.3 million but the risk of dying from choking on food is 370,035. However, rarely do we see people 30

fearing death from choking on food. The problem is that although the probability of a terrorist attack is low; the psychological impact is long-lasting and leads people to make decisions out of vulnerability and panic.

A study conducted by Harvard examined the willingness of law students to pay a special premium to reduce the risk of arsenic in drinking water. Students were separated into four 31

groups each with a different condition. The four conditions are as follows:

1. cancer risk of one in 1,000,000

2. cancer risk of one in 100,000

The Economist, A crash course in probability (2015)27

Anthony Barej, Budget for Home Office anti-terrorism unit to drop by £32m. (2017)28

David Batty, prevent strategy 'sowing mistrust and fear in Muslim communities (2016)29

Zeeshan ul Hassan, A data scientist explains odds of dying in a terrorist attack. (2015)30

Cass R. Sunstein and Richard Zeckhauser, Dreadful possibilities, neglected probabilities (2010)31

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3. cancer risk of one in 1,000,000, but cancer described in descriptive terms like “very gruesome and intensely painful, as cancer eats away at the internal organs of the body.”

4. cancer risk of one in 100,000, but cancer described in descriptive terms like “very gruesome and intensely painful, as cancer eats away at the internal organs of the body.”

Each group was asked to state their willingness to pay to reduce the risk through the possibilities of $0, $25, $50, $100, $200, $400, and $800 or more. The results found that increasing the probability by a factor of ten had a high statistical significance in the unemotional description but was not significant in the emotional description. The study suggests people are likely to have a greater response to extremely low probability risks that trigger extreme fear compared to less fearful risks. In addition, people are more likely to focus on the severity of the outcome rather than the likelihood of emotionally gripping risks.

Figure 6: Willingness to

Pay to Eliminate Arsenic Risks (Harvard, 2009)

New Research

To study how rational people are in decision-making when considering probabilities, I asked 90 people the following question: (For experiment method, see Appendix I)

From the following options, which one do you think has the highest probability of killing you?• Shark Attack

• Terrorist Attack

• Stroke

• Lightning Strike

• Choking on food

Choice Number of Participants

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Results

The results were interesting as it showed that the majority of participants did correctly choose one of the top two killers – stroke and choking on food. The response to terrorist attack, shark attack and a lightning strike is smaller than expected, as the neglect of probability theory suggests that more participants would have overestimated the risk for these attacks. Yet the results show that in fact people are relatively rational when presented with different options and they do consider the probabilities of the different events happening.

To further examine the extent of the neglect of probability bias, 90 participants were posed with two questions, as shown below. (For experiment method, see Appendix J)

Question 1

All in all, how worried are you that you or someone in your family might become a victim of a terrorist attack? Would you say you are:

• Very worried

• Somewhat worried

• Not too worried

• Not at all worried

Results

Shark Attack 3

Terrorist Attack 10

Stroke 51

Lightning Strike 4

Choking on food 22

Choice Number of Participants

Very worried 5

Somewhat worried 19

Not too worried 40

Not at all worried 26

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Question 2

All in all, how worried are you that you or someone in your family might become a victim of a stroke? Would you say you are:

• Very worried

• Somewhat worried

• Not too worried

• Not at all worried

Results

The results found approximately 27% of participants were either very worried or somewhat worried about terrorism whilst 67% of participants were either very worried or somewhat worried about stroke. It appears that the neglect of probability bias is not prevalent in the decision-making, as participants were more concerned with stroke than terrorism. This suggests that in important decision-making people are rational and do consider the risks and probabilities.

The next time we begin to panic over something happening, pause for a moment and consider the probability of it truly happening to you. Often the reality is that the probability is far smaller than we believe it to be. By becoming more aware of the neglect of probability bias it will allow us to make more logical decisions, instead of choices out of fear and insecurity.

Choice Number of Participants

Very worried 11

Somewhat worried 49

Not too worried 21

Not at all worried 9

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Conclusions

After analysing and focusing on four different cognitive biases: anchoring, confirmation, framing and neglect of probability, it is evident that cognitive biases play an important role in our lives and our day-to-day decisions. Cognitive biases can have an impact on both small and large scale decisions from choosing food in the supermarket to lifetime savings. The social experiments on anchoring bias demonstrated the subconscious ease for initial values to affect the decisions we make and how we rational we are. It is interesting to observe how ‘anchors’ can influence our expectations for prices even in long-lasting, important decisions like life savings. Those that were presented with a lower anchor had a lower expectation and it can be difficult to overcome the anchoring bias. However, by understanding how anchors work it will enable people to be more rational in their decision-making and not be influenced by initial values.

Likewise, the framing effect is another important bias as simply by disguising the same choice in different ways it can affect how desirable one choice compares to others. For organisations and firms, the framing effect can be a powerful tool to boost profits by focusing on positive frames. For consumers, by understanding the framing effect it will enable them to not fall prey to the tricks of the marketing industry and save money at the same time.

One of the key findings of my research is the ease for humans to ignore opposing information to their beliefs and instead they only seek information that confirms it. The confirmation bias is extremely prevalent in our decision-making and can have long-lasting, devastating consequences as shown in the Rolling Stone ‘A Rape on Campus’ situation. With the ever-increasing knowledge available in today’s society it is essential that we can try to remain as impartial as possible when forming our opinions.

It is not that humans are not rational as some of my experiments demonstrated that in fact, cognitive biases did not have the expected impact, especially when testing the neglect of probability bias. Under certain conditions, people do consider the probabilities and risks enabling them to make calculated and rational decisions. There are many factors asides cognitive biases that can also determine how rational we are in decision-making. Time constraints, asymmetric information and knowledge, emotional responses are all other factors that can influence the outcome of our decisions.

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However, it would be fallacious to not highlight the importance of cognitive biases, as it is evident from the social experiments and research that humans are not as rational as standard economics assumes them to be. Often we believe ourselves to be more rational than we actually are and it is essential that we understand and are aware of cognitive biases to enable us to not make wrong, fatal decisions.

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Appendix

Appendix A: Experiment method for wine bottle experiment

90 participants chosen through the method of convenience sampling ranging from ages of 16 - 70 were presented with an image of a wine bottle. They were asked to write the last two digits of their phone

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number and the price they would pay for the wine bottle on a piece of paper. Results were recorded in an Excel spreadsheet.

Appendix B: Calculations for Z Score

= 4.55

Appendix C: Experiment method for life savings experiment

50 participants chosen through the method of convenience sampling ranging from ages of 16 - 70 were allocated to be Group A and sent an online survey via their emails where they were told the average life savings is £10,000. Another 50 participants chosen through the method of convenience sampling ranging from ages of 16 - 70 were allocated to be Group B and sent an online survey via their emails where they were told the average life savings is £100,000. In the surveys, participants were provided with an answer box where they stated their answer and results were recorded in an Excel spreadsheet.

Appendix D: Calculations for Z Score

= 3.47

Appendix E: Experiment method for chicken fillet experiment

140 participants chosen through the method of convenience sampling ranging from ages of 16 - 70 were sent an online survey via their emails and had two answer options for the question either ‘25% lean’ or ‘75% fat’. Participants chose one of the two answer options and results were recorded.

Appendix F: Experiment method for charitable donations experiment

140 participants chosen through the method of convenience sampling ranging from ages of 16 - 70 were sent an online survey via their emails that posed them the question and three answer options of:▪ A weekly donation of £1.20▪ A monthly donation of £5

▪ An annual donation of £60

Participants had to choose one of the following options and results were recorded in a table.

Appendix G: Experiment method for logic puzzle experiment

140 participants chosen through the method of convenience sampling ranging from ages of 16 - 70 were sent an online survey via their emails with an image of the logic puzzle and presented with four answer options of:

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▪ A, 4 ▪ A, 7 ▪ Q, 4

▪ Q, 7

Participants had to choose one of the following options and results were recorded in a table.

Appendix H: Experiment method for ‘Ben’ experiment

70 participants chosen through the method of convenience sampling ranging from ages of 16 - 70 (Group A) were sent an online survey via their emails that provided the description for an imaginary character, Ben. They were asked the question whether “Would Ben be a good computer designer?”

Another 70 participants chosen through the method of convenience sampling ranging from ages of 16 - 70 (Group B) were sent an online survey via their emails that provided the description for an imaginary character, Ben. They were asked the question whether “Would Ben be a good manager?’”

In both surveys, participants were provided with two options of ‘yes’ or ‘no’ and results were recorded in a table.

Appendix I: Experiment method for probability of death experiment

90 participants chosen through the method of convenience sampling ranging from ages of 16 - 70 were sent an online survey via their emails with the answer options of:

▪ Shark Attack▪ Terrorist Attack▪ Stroke▪ Lightning Strike▪ Choking on food

Participants had to choose one of the following options and results were recorded in a table.

Appendix J: Experiment method for probability of terrorist attack experiment

90 participants chosen through the method of convenience sampling ranging from ages of 16 - 70 were sent an online survey via their emails with two questions and in both questions, participants were provided with the answer options of:

▪ Very worried▪ Somewhat worried▪ Not too worried▪ Not at all worried

Participants had to choose one of the following options to answer each question and results were recorded in a table.

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