Decision Heuristics
Matt L. Bass
Argosy University Online
Dr. Paul Sam
January 4, 2013

Abstract

Managers and leaders make many business decisions on a daily basis and there are many factors that factors into how these decisions are made. From a psychological reference, heuristics are defined as a mental shortcut that describes the behavioral mechanics on how individuals solve problems and make quick and efficient decisions. Heuristics are considered proficient cognitive processes, conscious or unconscious, that ignores part of the information in making decision. This discussion examines the concept of heuristics in terms of making decisions and explaining the failure to ignore base rates, discuss the relevant factors of why associated with the decision and if the decision was framed properly to ascertain a better decision.

Literature Review

(Shanteau, 1989) suggested that (Kahneman and Tversky, 1982) exemplified their view of heuristics and biases as follows: "In making pre dictions and judgments under uncertainty, people do not appear to follow the calculus of chance or the statistical theory of prediction. Instead, they rely on a limited number of heuristics which sometimes yield reasonable judgments and sometimes lead to severe and systematic errors.” (Kahneman & Tversky, 1982) went on to identify three of several cognitive heuristics for risk judgments- representativeness, availability, and anchoring-and-adjustment. * Representativeness – (Shanteau, 1989) referenced (Kahneman and Tversky, 1984) as explaining the representativeness heuristic to describe making an uncertainty judgment on the basis of the degree to which the judgment is similar in essential properties to its parent population and also if the judgment reflects the prominent features of the process by which it is generated. Evidence to support these two degrees of biases was based on findings that people will ignore base rates, neglect sample size, overlook regression toward the mean, and misestimate conjunctive probabilities. The representative heuristic is a cognitive bias in which the categories of a situation or event are based on a pattern of previous experiences or beliefs about a scenario. This heuristic can be useful when trying to make a quick decision but can also be limiting because it has the potential to lead to close-mindedness such as stereotypes. * Availability- Reported by (Shanteau, 1989) from (Kahneman and Tversky, 1984) to refer to the heuristic that is utilized to estimate the “frequency or probability by the ease with which instances or associations come to mind. The availability heuristic is supported by behaviors that are influenced by imaginability, familiarity, and vividness in response to stereotypic and scenario thinking. In laymen’s terms the availability heuristic defines judgment biases on the how easily specific examples are recalled. The easier the example, the more frequent or more casually related would be the judgment. * Anchoring and adjustment – As described by (Shanteau, 1989) is a behavioral heuristic that involves “starting from an initial value that is adjusted to yield the final answer where the conditions of the initial value or starting point, my by suggested by the formulation of the problem or the result of a partial computation that leads to insufficient adjustments. Evidence that support this heuristic results from biases in the evaluation of conjunctive and disjunctive events, insufficient revision of probabilities relative to Bayes theorem and framing. Anchoring is a cognitive heuristic that involves decisions based on an initial anchor and thus reflects the degree in which the initial judgment about an event or situation prohibits the individual from deviating from the position regardless of new data or information to the contrary.

Summary

There are many complexities when mangers and leader make business decisions. What is paramount in today’s business environment on a macro-level is that business decisions are greater than or congruent with the businesses strategic goals and performance metrics to sustain a competitive advantage. Further complexities are inherent in business decisions as the concept of heuristics come into play. Heuristic are simple rules where decisions are typically automatic with easy answers to decision problems that are not sometimes the correct answer to the problem.
With the explosion of the Internet and all of it its collateral innovations, many business operations use business analytic programs to track and trend various business processes that aid management to make better informed and statistically viable based decisions. Despite the role of these business intelligence systems, decision biases in the forms of representative, availability and anchoring heuristics remains a prevalent reason behind business decisions. Case in point-our facility had an opportunity to start-up a new production line for a product that has projected sales of over $5 million dollars in sales. A sister facility in Canada, was already producing this product, but the potential sales increase warranted a second facility to produce the same product. The initial decision was to send a group of engineers to the sister plant in Canada to capture best practices in producing this product as well as the type of equipment that was required to simulate the product process. The best practices of the production process for this product was captured with the exception of one important piece of the equipment-the dough extruder. The decision not to purchase the same extruder to run the product in our facility was made because it was hard to get parts for the equipment so another extruder was purchased. After months of trying to duplicate the production process in our facility, even with the help of R & D and Canadian engineers from the sister facility, the process could not be run. Statistical data from the sister plant revealed that the product ran very well with the extruder that the sister plant was using, but ignoring base rate information and not waiting to make a better decision resulted in having Corporate pull the project for our facility.
Many business managers and leaders ignore the base rates when making decisions because they tend to use their preexisting beliefs about probabilistic category attributes too much rather than not enough. Cognitive psychologists term decision making as the base-rate fallacy that links a decision maker’s tendency to ignore base rates in favor of individual perceptions when such information is available. Explaining this judgment events is correlated to how managers and leaders relate information to its realized degree of relevance allowing high-relevance information to supersede low relevance information. Information is also reasoned to be more relevant when it relates more specifically to a judged target case. The base-rate fallacy is in consequence the result of pitting what seems to be merely coincidental, therefore low-relevance, and base rates are excluded against more specific or causal information in many decisions. Statistically relevant factors that are ignored in heuristic biases include expressions of probability, such as possible and probable, are a common source of ambiguity that makes it easier for judgments are made to support their own preconceptions. The probability of a scenario is often miscalculated. Data on "prior probabilities" are commonly ignored unless they illuminate causal relationships (https://www.cia.gov).
Management and leaders are constantly making decisions that will mitigate risk. At the forefront of the decision process is risk aversion that will persuade the decision maker to look for reasons to turn down a judgment call where a loss is minimized and a gain is possible. The rule of thumb is to support the framing of a situation as it is presented as opposed to waiting and restarting the problem. The initial framing scenario should be at least tentative and must be subject to change to investigate and provide an opportunity to see the problem from various perspectives, different sides, and envision several potential outcomes to substantiate a better solution for the problem (http://www.cdnbizwomen.com).

References
Kaheman, D., & Tversky, A. (1982). The psychology of preferences. Scientific American, na (na), 161-173. Retrieved January 4, 2013, from http://dx.doi.org

Kaheman, D., & Tversky, A. (1982). The psychology of preferences. Scientific American, na(na), 161-173. Retrieved January 4, 2013, from http://dx.doi.org
Shanteau, J. (1989). COGNITIVE HEURISTICS AND BIASES IN BEHAVIORAL AUDITING: REVIEW, COMMENTS AND OBSERVATIONS. Accounting, Organizations and Society, 14(1), 165-177. Retrieved January 3, 2013, from my.ksu.edu
Shanteau, J. (1989). COGNITIVE HEURISTICS AND BIASES IN BEHAVIORAL AUDITING: REVIEW, COMMENTS AND OBSERVATIONS. Accounting, Organizations and Society, 14(1), 165-177. Retrieved January 3, 2013, from my.ksu.edu
Kaheman, D., & Tversky, A. (1984). Choices, values, and frames. American Psychologist, 39(4), 341-350. Retrieved January 4, 2013, from http://dx.doi.org
Kaheman, D., & Tversky, A. (1984). Choices, values, and frames. American Psychologist, 39(4), 341-350. doi: http://dx.doi.org/10.1037/0003-066X.39.4.341
Shanteau, J. (1989). COGNITIVE HEURISTICS AND BIASES IN BEHAVIORAL AUDITING: REVIEW, COMMENTS AND OBSERVATIONS. Accounting, Organizations and Society, 14(1), 165-177. Retrieved January 3, 2012, from my.ksu.edu
Center for the Study of Intelligence. (2013, January 4). Psychology of Intelligence. Retrieved January 4, 2013, from https://www.cia.gov
Canadian Women's Business Network. (2013, January 5). Canadian Women's Business Network. Retrieved January 6, 2013, from http://www.cdnbizwomen.com