Understanding Decision Making Bias For Several Theories

The Principle of Bounded Rationality

Discuss about the Understanding Decision Making Bias for Several Theories.

Decision-making is a vital part of any organization that contributes to the smooth functioning of every department of the organization. However, decision-making is not devoid of certain biases that hampers the organization (Craft, 2013). Mostly, biases in decision-making are cognitive, that is, psychological. Several theories have been proposed over the years to explain this bias. These biases prove as barriers to effective management. Some biases include action-oriented bias, self-interest bias, pattern recognition bias, anchoring bias and social harmony bias. Herbert Simon, a renowned political scientist and economist who proposed a detailed discussion concerning reasons that create bias.

The essay will provide explanation on three different concepts that explain bias in decision-making while deciphering a quote from Simon. The essay will first explain Simon’s quote from his seminal work Models of Man: Social and Rational and then proceed to explaining the concepts. These concepts include bounded awareness, framing and preference reversal and heuristics.

 “The capacity of the human mind for formulating and solving complex problems is very small compared with the size of the problems whose solution is required for objectively rational behavior in the real world – or even for a reasonable approximation to such objective rationality” (Simon, 1957).

It is evident from the above quote that Simon is talking about the human mind’s capability to understand and solve complex problems. The author states that the problems encountered by humans are way beyond their mind’s capability to resolve those. Further, Simon mentions that although the problem s are difficult to solve, the solution to these problems are necessary for a dispassionate coherent behavior in the real world. In fact, the solutions to these problems are necessary for finding a reasonable estimation of the rationality displayed by humans. He explains that the human mind seeks to uncover solutions that are good enough for any particular situation. The reason explains Simon, is that the human mind restricts itself from going beyond the realms of its capacity and finds an all-encompassing solution. The human mind is bounded by psychological limits.

Simon then proceeds to explain the consequences of this principle of bounded rationality. According to him, the first consequence of bonded rationality is that humans would try to build an alternative reality where they form simpler models of these problems that they can easily solve and thus sustain. However, the model they create is not even close to the actual model in the real world. This leads to further escalation of the problem. However, the author states that this behavior presents a chance to study the human behavior that could assist in understanding the psychological traits of the human. The second consequence, explains Simon is that the administrative behavior displayed by humans would be unproductive if human rationality had no limits. To put this in simple words, bounded rationality allows humans to choose from the best alternative available to them to satisfy their needs as opposed to the administrative theory, which states that humans choose the most satisfactory alternative.

Heuristics

In his book, Simon further elaborates the capabilities of the human mind in making decisions or choosing alternatives that are satisfactory enough for them to go further. He focuses on the means by which humans simplify the problems in order to bring the, within the power of their calculation. The major element in simplifying the problem is to replace the objective of ‘maximizing’ with the objective of ‘satisficing’. Satisficing is a term used by Simon to define the human behavior in decision-making. It combines the words ‘satisfy’ and ‘suffice’. In the functioning of the principle of bounded rationality, claims Simon, this process of satisficing is an important step. As per this hypothesis, decision-makers aim at sufficing with satisfactory results of their chosen alternative rather than looking for maximizing the values (Agafonow, 2014). When the one making the decision decides to choose the best option available amongst the given ones, he or she is maximizing the value. On the other hand, when the individual decides to choose an option that exceeds particular criteria of acceptability, an option that is not the best nor unique, he or she is satisficing. Maximization involves complex calculation of various alternatives and their orders of magnitude before selecting the best option. Commonly, search processes go together with satisficing hypothesis, for alternatives as well as for new learning. Satisficing is also attuned with imperfect orderings of options and with numerous principles of choice. The proposition put forward by Simon on human behavior regarding decision-making has received both popularity and criticism from different economists over the years.

After examining the quote thoroughly, three major concepts could be identified that help in explaining bias. The first concept is heuristics. It is defined as the way to simplify the process of decision-making. In heuristics, an easy question substitutes a difficult question thus making the process of decision-making easier (Vidal et al., 2013). Consequently, it leads to cognitive biases. However, it has been debated by many experts whether heuristics offers an opportunity for decision-makers to save time or whether it yields negative results due to bias. Busenitz and Barney (1997) explain that heuristics is useful to entrepreneurs than it is to the managers of organizations. The authors reason their claim by stating, “Under conditions of environmental uncertainty and complexity, heuristics can be an effective and efficient guide to decision-making”. They further state that using heuristics and biases in decision-making might provide an advantageous way to calculate the correct decisions.

Availability Heuristic

Heuristics can be categorized into Availability heuristic and Representative Heuristic (RH). The first that is Availability Heuristic is the psychological shortcut that gives rise to two biases – the ease of recall and retrievability (Kirkwood, Baucus & Dwyer, 2016). The ease of recall bias proposes that anything that is recalled easily in memory has the top priority and highest probability of being used in decision-making. Retrievability mentions that humans are biased in analysis of frequency partially because of their memory structure limitations and partly because of their search mechanisms. The important thing is the means by which humans store memories and retrieve those. Availability heuristic depends on the examples, which emerge instantaneously on human mind while conducting analysis before making any decision.   Houdek (2016) views availability heuristic as a quick method to make decisions has attracted several managers as well. Nonetheless, it has the potential to incur maximum damage to decision-making results due to its manipulative nature. In contrast to this, Representative Heuristic (RH) is crucial from the economic viability perspective. This heuristic type explains that people engage in decision-making with limited availability of information by comparing it to ‘prototype’ that preexists in their mind. While utilizing RH, individuals tend to involve in bias because the recognizable prototype might not be as efficient as others might. Marusich et al. (2016) have found that the existing prototype in the individual’s mind opted for by her or him for making a decision, is better than choosing an option that has no link to the mind and the memories. Mohan et al. (2014) in contrast to this view, mention that using RH increases the risk of making wrong and sometimes severely damaging decisions. To provide an instance, judging someone from his or her look or attire or his or her profession is a decision-making bias on part of the observer. This might give rise to racial discrimination and other social prejudices.

The second concept that explains bias in decision-making is bounded awareness. Similar to bounded rationality, bounded awareness refers to the limitations of humans to be aware of several things needed for decision-making. In revealing the ethical and unethical conduct of managers in the decision-making process especially, bounded awareness is quite helpful. The theory holds that specific situations and computational ability of humans bound them from making effective decisions. It brings out the general errors committed by individuals during the process of decision-making at frequent interval of time. As (Bazerman & Chugh, 2018) have observed from the 2004 case of Vioxx, the drug-manufacturing company, “decision-makers ignore certain critical information”. The authors explain that bounded awareness is nothing like information overload or lack of time, the absence of conscious awareness that limits their capacity to make decisions.

Representative Heuristic

The third and the final concept as evident from Simon’s concept is the framing and preference reversal. According to framing and preference reversal, individuals tend to opt for risk-aversion when they predict gain and risk-preference when they predict loss. Decision-making process involves maximum accounts of this preference-reversal bias because business is majorly about loss and gain. Drichoutis and Nayga (2013) have found in their study that individuals take longer time in choice reversals than they take in non-reversal. This study brings forth the idea that preference reversal is one of the most important concepts that explains bias.

To conclude, it must be stated that Simon’s contribution to the economics of making choices cannot be denied. His seminal work on human behavior explaining decision-making has been appreciated and is still used by many. The essay above provided an elaborate explanation of the quote used by Herbert Simon in his book and then associated it with the concepts of decision-making biases. Three concepts have been identified and explained in the essay regarding decision-making bias.

When one commits a blunder by falling prey to bias in decision-making, it not only costs the individual involved but also the larger community or organization. The work published by Herbert Simon in 1957 has provided a solid ground for future researchers to scrutinize the concepts that indicate bias in decision-making. However, few scholars did not comply with Simon’s views on human behavior in terms of decision-making. According to them, the principle of bounded rationality proposed by Herbert Simon cannot be applied to every situation because he has neglected other forms of rationality.

In this report, a real-world scenario will be discussed to explain the influence of the three concepts as mentioned in Simon’s quote in decision-making. The report will highlight the findings of the first part of this paper to analyze the decision-making bias of Uber, a multi-million dollar international car services company.

The revelation of UBER’s use of software known as ‘greyball’ in 2017 led to the company’s scrapping of services from one of the peak markets that us the United Kingdom (Time.com, 2018).  Founded in 2009, the company has its headquarters in San Francisco, California and operates in more than 600 cities. The company is involved in services like peer-to-peer ride sharing, transportation network, and food delivery (Uber.com, 2018). The ‘greyball’ software – that it used to dodge security regulators in regions where it had no permit – was in use since 2014, as revealed by The New York Times. However, back then, it functioned under a different name. The decision was taken by the higher authorities of the company to avoid investigators from locating illegal UBER riders. Travis Kalanick, the co-founder had to step down because of this controversial decision.

Bounded Awareness

UBER’s decision to place the ‘greyball’ software in its cars was made to boost the company’s profits and become the dominant player in this business.  Representative Heuristics (RH) is at work in this case. The company officials made the decision to continue with the controversial software from 2014 despite knowing that it was against law. According to RH, the bias in decision-making occurs when individuals rely on the better recognizable option from the past to make the decision. In this case, the officials at UBER decided to go with the past results using the software without scrutinizing other available options.

Apart from that, the decision to continue using the software to avoid investigators and specific riders demonstrated the bias of bounded awareness. Bounded awareness, as already discussed, refers to the limitations of individuals to use an array of data to make a decision. When UBER’s software controversy was revealed last year, the company in its defense stated that they were using the software to avoid illegal passengers who did not comply by the company’s terms and regulations. This demonstrates a clear instance of bounded awareness as the officials made wrong use of the information available to them and encouraged discriminatory practices.

The last concept of bias that is framing and preference reversal is visible in this case where UBER decided to take the risk of hiding the matter from the UK regulatory bodies in order to avoid the imminent loss. They took the risk to continue without disclosing the use of the software. The decision taken by the legal team of the company to pass the software as legal foreseeing the benefits makes it clear that they decided to take the risk in order to gain.

In order to proceed, it is important to understand that biases in decision-making are hard to avoid and are bound to occur in some cases. The case discussed in the previous section clearly shows that biases, especially cognitive biases are not possible to overcome but these are possible to avoid or avert. In this section, the various methods that measure bias shall be discussed.

Aczel et al. (2015) introduced the different methods of measuring bias probably for the first time. According to the authors, biases in decision-making can be qualitatively measured by observing certain factors. These factors are:

• Individualized scores – This method in heuristics and biases (HB) is utilized to measure the level of bias of the individuals within a larger organization. The individualized scores are achieved from measuring the performance of the individuals in different tasks. The tasks that measure biases at individual level are further divided into three categories

1. Type A: In such tasks, one question is asked to diagnose the individual’s susceptibility to violating the given normative rule. To measure bias, calculation is done from a collection of different versions of the question.
2. Type B: In this type of tasks, the bias is measured from the answer provided by the individual to a particular question that indicates his or her bias related only to another decision. For an instance, framing effect is at times measured at individual level by comparing the answers of differently framed questions in order measure bias.
3. Type C: here too, the measurement of bias is done by asking two questions to the individual.

• Constructing Validity – The next method to measure bias is validity construction. This method measures the influence of bias in decision-making by including questions with multiple questions that tests the ability of the individuals to construct the validity of the decision made (Schwager & Rothermund, 2013).
• Motivation – Biases in decision-making are also measured by checking the motivation of the employees to perform and the things that motivate them. The cognitive bias here is measured by measuring the motivation level of the participants. Therefore, managers make decisions keeping in mind the motivation factor of the employees where under-motivated employees might be motivated with the decision.
• Comparability – comparing the degree of susceptibility is another method to measure bias in decision-making. The measurement is done by observing the variations in vulnerability demonstrated by the participants during decision-making.

The case of the greyball software of UBER and the people linked to it should be examined using these above methods to measure the bias.

Preference Reversal and Framing

It is imperative to state that biases are not possible to avert and hence, one should find ways to overcome these biases using some strategies. Different types of biases require different strategies in order to reduce their impact (Johnson et al., 2013).. The following biases can be solved by following simple rules:

• Anchoring bias – It occurs when humans tend to ignore data and information that are not easily available. The humans make use of the initial portion of information to make the decision (Cen, Hilary & Wei, 2013). This bias is easily overcome by searching persistently for potentially applicable or new contradicting evidence. Humans must challenge the piece of information available to them and look for new ones every time they make decisions.
• Framing bias – This type of bias occurs when humans tend to prefer and avoid risks as per the gain or loss associated with the outcome. They make decisions to either take the risk for avoiding loss or avoid the risk for ensuring gain (Gamliel & Kreiner, 2013). To overcome this bias, humans must develop systematic review processes that present them with the option to leave out a possibility while attempting to cut losses.
• Availability heuristic – Here, humans use the most recent memory of an event to make the decision for the ongoing or future event ignoring the important events of the past. To avoid such biases, humans should go beyond the readily accessible zones of their mind and search for other crucial information evidences from the past.
• Overconfidence bias – Often humans tend to be confident about an option or idea and make the decision based on that idea without even attempting to listen to or consider other options (Erceg & Gali?, 2014). This gives rise to the overconfidence bias. To overcome this bias, humans should search for varied outside opinion.
• Confirmation bias – When humans look for evidences that confirm their initial decision but ignore those which go against it, they demonstrate the confirmation bias in decision-making (Kassin, Dror & Kukucka, 2013). This could be overcome by challenging one’s own thought process and see beyond it and looking for an array of sources.

Apart from the above mentioned biases, other biases are there too and all these have almost a simple and common strategy – challenging one’s own beliefs and exploring new, innovative options. UBER must also go beyond its belief that greyball software would ensure greater benefits and look for ethical options while making such decisions.

Conclusion

In the end, it is imperative to state that the biases in decision-making leads to the loss of not only the decision-makers but also those associated with it in any way. Overcoming these biases are very important to make sure that the hard work decision-making does not go futile. This paper discussed the case of the ride hiring services giant UBER, an American company and its decision to install the greyball software to ride illegally on banned locations. the software was being used by the company since 2014 but it was revealed in 2017. The software was installed so that UBER could dodge investigative personnel from locating the drivers. The decision did not prove successful for the company as it landed itself in controversies and cases and even got banned from the UK. The report provided some suggestions or strategies for the company to follow in order to overcome the biases in future.

References:

Aczel, B., Bago, B., Szollosi, A., Foldes, A., & Lukacs, B. (2015). Measuring individual differences in decision biases: Methodological considerations. Frontiers in psychology, 6, 1770.

Agafonow, A. (2014). Toward a positive theory of social entrepreneurship. On maximizing versus satisficing value capture. Journal of Business Ethics, 125(4), 709-713.

Bazerman, M., & Chugh, D. (2018). Decisions Without Blinders. Retrieved from https://hbr.org/2006/01/decisions-without-blinders

Busenitz, L. W., & Barney, J. B. (1997). Differences between entrepreneurs and managers in large organizations: Biases and heuristics in strategic decision-making. Journal of business venturing, 12(1), 9-30.

Cen, L., Hilary, G., & Wei, K. J. (2013). The role of anchoring bias in the equity market: Evidence from analysts’ earnings forecasts and stock returns. Journal of Financial and Quantitative Analysis, 48(1), 47-76.

Craft, J. L. (2013). A review of the empirical ethical decision-making literature: 2004–2011. Journal of business ethics, 117(2), 221-259.

Drichoutis, A. C., & Nayga Jr, R. M. (2013). Eliciting risk and time preferences under induced mood states. The Journal of Socio-Economics, 45, 18-27.

Erceg, N., & Gali?, Z. (2014). Overconfidence bias and conjunction fallacy in predicting outcomes of football matches. Journal of Economic Psychology, 42, 52-62.

Gamliel, E., & Kreiner, H. (2013). Is a picture worth a thousand words? The interaction of visual display and attribute representation in attenuating framing bias. Judgment and Decision Making, 8(4), 482.

Houdek, P. (2016). What comes to a manager’s mind: Theory of local thinking. Journal of Management Inquiry, 25(4), 359-366.

Johnson, D. D., Blumstein, D. T., Fowler, J. H., & Haselton, M. G. (2013). The evolution of error: Error management, cognitive constraints, and adaptive decision-making biases. Trends in ecology & evolution, 28(8), 474-481.

Kassin, S. M., Dror, I. E., & Kukucka, J. (2013). The forensic confirmation bias: Problems, perspectives, and proposed solutions. Journal of applied research in memory and cognition, 2(1), 42-52.

Kirkwood, J., Baucus, M., & Dwyer, K. (2016). Ethics in Entrepreneurship Education: The Case of a Student Start-Up Entrepreneur. Journal of Business Ethics Education, 13, 91-116.

Marusich, L. R., Bakdash, J. Z., Onal, E., Yu, M. S., Schaffer, J., O’Donovan, J., … & Gonzalez, C. (2016). Effects of information availability on command-and-control decision making: performance, trust, and situation awareness. Human factors, 58(2), 301-321.

Mohan, D., Angus, D. C., Ricketts, D., Farris, C., Fischhoff, B., Rosengart, M. R., … & Barnato, A. E. (2014). Assessing the validity of using serious game technology to analyze physician decision making. PLoS One, 9(8), e105445.

Schwager, S., & Rothermund, K. (2013). Motivation and affective processing biases in risky decision making: A counter-regulation account. Journal of Economic Psychology, 38, 111-126.

Simon, H. A. (1957). Models of man; social and rational.

Time.com. (2018). https://time.com. Retrieved from https://time.com/4696639/the-latest-victim-of-ubers-disruption-may-be-itself/

Uber.com (2018). About Uber – Our Story – Vision for Our Future | Uber. [online] Uber.com. Available at: https://www.uber.com/en-IN/about/ [Accessed 31 May 2018].

Vidal, T., Crainic, T. G., Gendreau, M., & Prins, C. (2013). Heuristics for multi-attribute vehicle routing problems: A survey and synthesis. European Journal of Operational Research, 231(1), 1-21.