Biases Helping Professionals MAKE SMARTER Decisions Mindset Essay

In the business enterprise world managers are presented with a variety of problems on a regular basis, some of which require quick at that moment decisions whilst others require more depth in knowledge. The decisions and judgements made by managers are based on beliefs relating to outcomes of incidents. These managers rely on heuristic ideas which is these heuristic rules which also determine the outcome of a bias in their decisions (Kahneman, et al. , 1982).

Heuristics are those guidelines, cognitive techniques, which people use to reduce the amount of information processioning that is required when making decisions. An workplace for example might use the heuristic of "untidily outfitted candidates are sloppy workers" while making their hiring decisions. The example given allows the workplace to filter down the list of potential candidates by only considering the ones that are neatly dressed up and eliminating applicants that are untidily outfitted before supplying each a fair chance. Sometimes these heuristics work and sometimes as is the case in the example they are quite misleading and therefore lead to biases and discrimination.

Biases arise when people have tendencies to make decisions based on cognitive factors alternatively than considering the actual facts as information. These biases generally appear because of using the info processing shortcuts; heuristics, as was the circumstance in the last mentioned example. The product of human thought are biases, which is these biases which may easily skew the reliability of the evidence which people present in their decisions or facts (Bazerman & Moore, 2009).

Biases emanate from heuristics and are classed appropriately. Heuristic driven biases contains behavioural ideas such as availability, representativeness, affect and confirmation. Common biases emanating from the supply heuristic include ease of recall and retrievability biases. Representativeness heuristics includes biases occurring therefore of insensitivity to sample size, foundation rate fallacy, myths as consequence of chance and regression to the mean as well as others. Biases that are classed with the verification heuristic are those worried about anchoring and modification and overconfidence as well as the hindsight bias. You will find a large variety of heuristics and biases that people might be at the mercy of though only a little set of these are discussed in this newspaper.

This paper can look into highlighting only a subset of the biases and heuristics, seeking to focus on the more prevalent ones which will affect managers in their decision making processes on a regular basis. Biases originating consequently of the supply heuristic such as ease of recall and retrievability; the representativeness heuristic biases of insensitivity to sample size, misunderstanding of chance, regression to the mean; the verification heuristic biases of anchoring and adjustment as well as overconfidence will be talked about in further fine detail. A look into how these cognitive concepts affect the decision making process and how managers benefit from understanding these ideas will be explored.


We use the availableness heuristic in our decision making processes whenever we make judgements based on how easily we can bring to the forefront of our own minds, information of varied instances that relates to a circumstance which is under consideration. According to Tversky & Kahneman (1973) it is our life experience which have trained us that cases of larger classes are recalled better than instances of less recurrent ones, occurrences which will happen are better to imagine than a lot more unlikely ones, and associative cable connections are strengthened when two happenings occur together over a frequent basis. It is consequently of people utilizing their cognition to get, construct and affiliate information that folks can make judgements as to whether events will probably occur or the amount of times classes could arise or the frequency of co-occurrences.

For example, when a manager had a need to assess the performance of a member of his team over the past year he'll recall from storage all the associates that have constantly delivered over the last couple of months of the year. Here the administrator employs the availableness heuristic as he estimates the consistency of number of that time period a member has sent by the decrease at which they can recall a meeting where the team member has performed well (Bazerman & Moore, 2009). So the supply heuristic can be said to be found in making a judgement more credible based on the vividness or ease of imagining a meeting even though the probability of the function occurring might vary. Our reliance on availableness though leads us to predictable biases inside our judgements.


The ease of recall bias is situated surrounding the factors of vividness and recency to occasions that have taken place inside our lives where people tend to overweigh the importance of recent happenings and their vividness inside our memory. Tversky and Kahneman (1974) conducted a study where individuals were read a set of names of stars consisting of both sexes. Some brands on the list were famous (Richard Nixon, Elizabeth Taylor) while others less famous (William Fulbright, Lana Turner). Lists comprising different types of men and women where presented to two communities. The main one group was offered a list that included more famous feminine labels than men's brands but overall experienced less females than men on the list. The other group was offered a list of more famous men and less famous women's brands but had more names of women listed. One group was asked to write down as many names as they could remember from the list whilst the other was asked to guage if the list covered more titles of women or men. In both organizations individuals judged incorrectly and selected the gender where the famous names occurred the lesser quantity of times in the list. Individuals in this study paid attention to the more stunning famous labels which resulted in inaccurate judgments being made.

The ease of recall bias thus features to us that the greater vivid or recent an experience we'd of something we are more likely to place a much bigger emphasis on these scenarios. In so doing we consider these scenarios to learn a much larger part in our decision making processes when evaluating an answer.


The retrievability bias presents itself in occasions where we're able to retrieve certain pieces of information much quicker than others consequently of posting something common. As some things are more easily retrieved they manifest themselves more inside our judgements than we might think. A report conducted by Tversky and Kahneman (1983) exhibited the impact of retrievability bias when they asked students to list seven letter words in 60 seconds stopping with "ing" or the ones that had the notice "n" as the 6th character. The students concluded that words concluding with "ing" where more prevalent than those that acquired "n" as the 6th notice even although latter was present in all the words concluding with "ing". The conclusion which was come to by this study suggested that words formulated with the "ing" suffix where easier retrievable in comparison to those where the seek out the 6th personality was more challenging to set-up words. This analysis showed that we are able to access information easily when they are well set up and thus we can use the easily retrievable information in the judgements we make.

The retrievability biases can lead to discrimination in organisations where people being appointed from within a person's communal network where there is a commonality of history, education, culture, experience and even sociable status. The purpose here is probably not to discriminate but this could lead to discrimination within the company of sexism, ageism and racism (Petersen, et al. , 2000).

Managers on a regular basis can be influenced by these biases (ease of recall and retrievability), which can lead them to making fundamental errors in their managerial decision making process. This was clearly illustrated in the event in which a hedge fund was going through a change and recruited a CTO to manage its technology team, wherein a fresh leading edge technology was to be developed. On arrival the CTO started a restructure of the team and initiated a recruitment drive where he was solely responsible of employing candidates. Through the first period of the recruitment process the CTO looked at candidates who were already known to him. With this period he recruited lots of former fellow workers. Through the second period of the recruitment process he appointed people whom he didn't know but were of similar personalities, mind-sets and backgrounds to prospects of his former colleagues. It could have been his motive to hire likeminded people however the new technology required diverse teams of people to create out of the box ideas. The CTO though were left with a team where individuals were very similar in behaviour and their means of thinking. This impacted the implementation of the technology as there have been not enough people who have diverse backgrounds in the team to understand and make use of the correct tools to attain the best possible final result. If the CTO could recognise his biases therefore of the availability heuristic he'd have probably had the opportunity to recruit a far more balanced team. Further to this with too many folks of the same qualifications and ideology the ethnic balance and diversity within the organisation was influenced which led to larger systematic failures. With no intent to discriminate the CTO created a problem which he would have averted if he was more aware of his biases.


When dealing with making decisions we are occasionally faced with problems where we have to compare items and deduce if indeed they contain similarities found in another group predicated on previously shaped stereotypes. To be able to do this we have to relate the way the probability of our items are related to the items we are checking (Tversky & Kahneman, 1974). "Representativeness is an assessment of the amount of correspondence between a sample and a populace, an instance and a category, an action and an professional or, more generally, between an outcome and a model" (Tversky & Kahneman, 1983). Representativeness heuristics helps us to evaluate the amount to which items match one another.

For example, an investment banker might be considering a choice to buy a newly listed stock. He first considers whether he has prevailed or unsuccessful with other stocks and shares in the same sector that are familiar to him. This way he'll determine whether it is a stock he'll need it into or not. This heuristic may also be helpful for managers. For instance whenever a manager is building a team for a job that requires a very high degree of accuracy with numbers he might associate people that are good at mathematics and money as these kinds of folks are recognized to work with numbers which requires some level of correctness in them.

Representativeness though can also work against us by allowing us to unconsciously discriminate matching to contest, gender or culture which we'd not normally do. For example if a supervisor believes that the best investors are usually white men, then he unconsciously discriminates against women and other races. With this we see that folks seem to rely on representativeness heuristics even though they may not have the right information at hand which leads these to make wrong judgements.


This type of bias suggests that we tend to make decisions by using statistical information that we think is accurate but generally includes too small an example size. Say for example a fair amount of men and women are frightened to take a flight in airplanes because most feel that it will be involved in an accident but these same people will probably travel in a car without any concern. Statistically though this assumption created by people is incorrect as the chances of the person dying in their life-time in a car in the UK is 1 in 240 but is even less likely in an aircraft at 1 in 55078 (Bandolier, 2007).

This shows us that our decisions are skewed as result of not considering all the factual statistical information available. If those fearful of plane accidents had considered the information all together and looked at the various settings of travel and in each case evaluated the risk that each mode of transport posed to them their decisions will most probably change if it was the statistic that that they had founded their decisions on.


Occasionally when making decisions we consider normal incidents that happen consistently, to be uncommon occasions. Bazerman and Moore (2009) illustrate this in an example where a texas holdem player has been retaining bad cards for ten hands. The player presumed that he was due a good palm sooner as he previously lots of bad hands previously. The fact that the texas holdem player assumed this is unlike the particular expected end result should be. He previously a fifty percent chance of obtaining a good hand, which was exactly the same chance as he do in virtually any other previous side. The misconception that the gambler had was that he assumed there was a balance and that the total amount would be restored. Tversky and Kahneman (1974) exhibited though that it's not the deviations in the balance that is corrected but it's the deviations that are diluted as the number of events increase.

Decisions made because of the misconception bias are therefore of folks having hope somewhat than acting on the true facts available to them. Hope can't be found in business as any practical strategy (Barrows, et al. , 2010). In the aforementioned example if the gamer had considered each and every hand on a person basis and evaluated the outcome of every hand he'd have had the opportunity to triumph over this bias. With that in mind it would likewise have been easier for him to take into account his loss and take the corrective action needed. Moreover it's the same for professionals. They need to be aware of this misunderstanding of there being a balance and ensure that their decisions are based on the true reality of each piece of data at their removal when coming up with their decisions. A administrator involved in an investment trading environment who succumbs to this bias may find himself facing serious deficits falling into the same trap as the poker player above.


The regression bias is where we make the error of considering a uncommon event as a standard one without considering the possibility that it might be an exceptional case that the function has occurred. This is actually the exact contrary of the misconception bias and thus regresses to the mean (Barrows, et al. , 2010).

Consider an example of a fund supervisor and his team who deliver a stellar performance in their fund over the last financial yr. The buyers in the finance might go through the performance and feel that this sort of performance is expected to continue in to the next financial yr. More than likely though this may have been a one off event and the finance manager will almost certainly struggle to continue to regularly beat expectations calendar year on time thus regressing to the mean. The outperformance of the account in the one year could have been consequently associated with an anomaly in market conditions. In case the fund manager though does not acknowledge that was an exceptional time and a one off then he'll set false prospects for the future and make inappropriate assumptions about the funds future performance. In the likelihood that the account struggles to produce the results the following season as it regresses to the suggest the fund director may get started to make excuses to the traders for not meeting expectations. It really is of the most importance for professionals to keep yourself updated and understand the situations wherein such rare events might occur to defeat the misinterpretation of results because of this of this bias.

On a daily basis managers are faced with decisions where they are required to call after representativeness heuristics to aid them in their decision making process. Professionals though have to be aware of the biases that are associated with representative heuristics to be able to maximize effective decisions. The biases of insensitivity to test sizes, myths of chance and regression to the mean can provide managers well when used properly. However, frequently when managers are faced with pressure situations and put under constraints they could rely too intensely upon this simplistic type of heuristic. This in turn may lead to systematic irrationalities in their decision making functions (Bazerman & Moore, 2009).


The anchoring heuristic is when people begin to base their decisions around an individual point of information using previous experience or information. Then they make adjustments to the original value, the "anchor", by using additional information to reach a final answer. Corresponding to Tversky and Kahneman (1974) anchoring and adjustment is "beginning with an initial value that is changed to yield your final answer. The original value, or starting place, may be advised by the formulation of the situation or it might be the consequence of a partial computation. In any case, adjustments are usually insufficient. That's different starting items yield different estimates, which can be biased toward the initial values".

In a demo to highlight the consequences of anchoring Tversky and Kahneman (1974) asked members to calculate the ratio of African countries in the US. In the occurrence of each participant a arbitrary number was obtained by spinning a wheel (consisted of numbers between 0 and 100) that your members used as a starting place. Participants where then asked to state whether the real volume was higher or less than the random amount and then to provide their estimation. The random quantity had a designated effect on the participants' estimates. For example in the event where participants believed the ratio of African countries in the US. The individuals that started with the random number ten predicted 25 percent and the ones that started with the random quantity sixty five believed 45 percent. This demonstration illustrates evidently that even though members were given a random amount to start with they still gravitated around that number to find their finest estimate.

People have a tendency to anchor around a physique even though it might not relate to the circumstance. In this case an arbitrary one is composed. Companies use anchoring in their marketing and costs strategies to attract consumers. Consumers anchor against something that is familiar to them when making decisions. When there is none they choose an arbitrary one. Once an anchor is established they use this as the base for everyone future decisions. For instance, Apple released the first iPhone in 2007. When the telephone premiered it was coming in at $599. In the first couple of months sales where slow. This was because at first most people got nothing to compare it to as it was initially to advertise. A couple of months later the purchase price was fallen by $200 dollars. This listed drop sparked a large amount of sales. Once the priced fallen people acquired an anchor ($599) around which to calculate whether the new price ($399) was of value or not. By using the anchor people where able to make comparisons and purchase the phone (Dalrymple, 2007).

Managers need to be aware of the effects of anchoring and modification as they can utilise it with their edge in marketing promotions and influencing the results of situations as demonstrated by the previous example. They also need to be aware never to be biased by the result as it could cause them to gross misjudgements in their decision making process. A director who is responsible for procurement for example will need to be familiar with not falling into the same capture as consumers did above.


For people to succeed in a variety of activities such as their job performance, activities and in business they must be self-assured in their capabilities to achieve success. Overconfidence though is born from an individual who rates their assurance in decisions subjectively rather than evaluating them from a more objective and balanced point of view. Overconfidence thus brings about unrealistic expectations and becomes a hurdle to effective decision making (Bazerman & Moore, 2009).

In a study conducted by Odean (1998), he discovered that overconfident traders traded more frequently that those less positive. These investors also believed these were much better than others in choosing securities as well when the perfect time was to enter in or leave a trade. Further to the he discovered that those stock traders who traded more often also produced lower returns than the marketplace. From this example we can easily see that being overconfident in your decision making does not allow one to have the ability to assess a situation objectively and influences the end result negatively. Managers have to be aware of this and also objectively examine their decisions by questioning their decisions and looking at choice perspectives to ensure they achieve the most best results (Bazerman & Moore, 2009).


For professionals to have the ability to effectively use the heuristics and biases in their favour or to stay away from the pitfalls which these bring into the decision making process they need to arm themselves with methods on how to struggle their decisions.

To accomplish this most managers will follow the simple plan of gathering all the facts and information required for the task at hand. They'll review these details to ascertain whether their source has distorted or masked this information in anyways and then they will use their own encounters, knowledge and reasoning to deduce a final answer. This technique though is flawed at every level as there may be a number of errors made in judgement as a result of biases. To get over the biases managers require the proper tools to recognise and neutralize them. Using the correct tools over an extended period will reduce the effect of biases on the decisions (Kahneman, et al. , 2011).

In the article "Before You Make That Big Decision", Kahneman, et al. (2011) go on to list a set of 12 steps which professionals can utilise to check that the decisions they make are up to date ones. Some of these steps include looking at for the availability biases, anchoring biases and the overconfidence biases which were discussed before. The twelve steps include questions a manager could present to himself to ensure that he is effectively looking at the information at hand from an impartial view to help make the most informed tactical decisions. The challenge though to use these kind of quality controls in your choice making process is to create awareness amongst professionals that even the best of them are fallible.


Throughout this newspaper we've discussed a number of heuristics and biases ranging from the simple recall and retrievability biases which arise therefore of the availability heuristic; representativeness heuristic biases such as regression to the mean and misconception of chance; overconfidence and anchoring biases, which effect the decisions professionals make. For professionals to be able to reap the benefits of these biases and heuristics it is essential for them to firstly be familiar with the existence of the cognitive biases and heuristics. Awareness of these will allow managers to be able to focus their attention onto biases when undertaking decisions. As has been shown throughout the newspaper evaluating all the facts accessible and ensuring that the sources of information havent been distorted, managers can produce results that are neutral and decisions which can be correctly reflective of the situation. Distortion of the reality through various options as well through their interpretation, as a result of biases and heuristics, brings with it the systematic failures in the managerial decision making process.

For those managers that know about the biases, appropriate tools and mechanisms need to be used to effectively underlying out these biases using their decision making operations. To do this they might need to test their biases you need to include tools like the one recommended in this paper. Ongoing use of such tools will lead to a far more disciplined process in coming to decisions with minimal bias and in the longer term taking away biases. These will help managers make smarter decisions. A first step in an activity of how heuristics and biases can help managers make smarter decisions was shown in this paper. To truly gain the good thing about this in business though organisations need to take the next phase and play a more active role in changing their culture when you are more aware of these heuristics and biases in any way management levels to help make the most reliable decisions.

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