Researching of Judgmental Forecasting

Subject: Management
Pages: 4
Words: 1167
Reading time:
5 min
Study level: Bachelor

Summary

The essay is devoted to the analytical method of evaluative prediction in business, economics, and other fields of application. Chapters from a textbook devoted to the basic principles of forecasting are used, as well as scientific articles on the basis of which the advantages and disadvantages of the given estimation method are demonstrated. The task of the work is to characterize this type of forecasting, describe its applied areas, and consider the advantages and disadvantages. In addition, the paper considers the Delphi prediction method, its features, and possible applications separately. It becomes possible to characterize the benefits and the most logical scope of use of evaluative forecasting by defining what additional practices it should be supplied with for improvement.

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What is Judgmental Forecasting

The evaluative prediction uses subjective judgment and intuitive judgment to produce results. The calculations derived from such a prediction are also subjective and based on the expert’s idea of probability. Such a forecast usually includes a group of experts whose opinions are consolidated into a forecast, which is then gradually brought to a consensus. Although judgment has a significant predictive role in economics and business modeling, the scientific status of this category underwent a change in the upheaval of the twentieth century. At this moment, the scientific attitude towards evaluative forecasting has changed for the better, and the industry’s current state is improving.

Where is Judgmental Forecasting used Most Effectively?

A fairly low percentage of existing companies and business enterprises use this type of forecasting. Judgmental forecasting can be useful in creating an idea of the future situation when there is a lack of information or delays in the arrival of data. This method can be used not only with a lack of data but also in order to strengthen its reliability by conducting additional subjective research (Hyndman & Athanasopoulos, 2018). Knowledge of the source in the perfect measure is the principle of a better value judgment, which calibrates the accuracy of the prediction. But to an even greater extent, the guarantor of the fidelity of a subjective prediction is the familiarity of the expert making a judgment with the data. The advantage of such forecasting appears in such situations when information is announced too often for analysis through computer processing of statistics.

In cases where the mechanism is not able to meaningfully process the incoming information, human judgment seems to be of great practical value. However, the fundamental condition for the success of the forecast is not only the knowledge of the predictor within the sphere, the information from which it is being interpreted. The very novelty, completeness, and relevance of the information determine to the greatest extent how accurate, detailed, and correct the forecast will be.

Examples of how Judgmental Forecasting is Used Most Effectively

Such forecasting is most effective when the situation requiring characterization is unstable or extremely new. In the case of a pandemic situation, such predictions seem especially necessary for the orientation and survival of certain businesses in the constantly changing economic space of imbalance. The subjective predictions of trained experts can help in the rapid readaptation of a manufacturing enterprise for quarantine measures or in the remodeling of logistics processes due to border closures and supply disruptions.

Advantages and Disadvantages of Judgmental Forecasting

The specificity of this type of judgment lies in its subjectivity, which has a number of limitations. Despite this, using a systematic initial approach to this practice is possible with improved structuring. Considering the advantages and disadvantages of this forecasting method, it turns out they are related and characterize the method as useful, but depending on the information and context.

Advantages

The main advantage is the prospects of this direction of forecasting and the increasing demand for it over time. The very socioeconomic situation at the moment is developing in such a way that more rapid and subjective forecasts are required in order to make quick and most effective decisions. It should be noted that the higher the domain knowledge of the topic under discussion by experts, the higher the probability of an accurate forecast, especially in combination with statistical calculations (Kim et al., 2020). In addition, a rather big plus is the ability to give a high variability of forecasts, which may not be taken into account in accurate statistical forecasting.

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Disadvantages

The main disadvantage is the impossibility of avoiding subjective perception when establishing such a forecast. There are assertions that there can always be enough reasons to suspect an expert of being biased. Excessive confidence by the group in the effectiveness of the forecast created can also cause an error, and therefore the expert groups should take into account the seriousness of this problem. In addition, there are practically no scientific studies that would deal with the issue of heuristics, that is, a set of scientific methods used to make a prediction (Sniezek, 1990). That is why complications of perception, such as overconfidence and prejudice, can actually affect the prognosis. Such shortcomings may not allow considering it sufficiently based on facts due to insufficient scientific evidence.

The Delphi Method of Judgmental Forecasting

The Delphi method was developed in the 1950s as a way to provide additional reliability for subjective prediction. The essence of this method of prediction is to search for a focus group of which conditions are proposed for building a value judgment. The summarized answers are offered to the experts themselves for the subsequent analysis of their own answers. Statistical reformulation of the forecast results makes it possible to improve it by providing an opportunity to additionally evaluate one’s own judgment against the background of the consensus summed up by statistics. It is important that a prediction cannot be based on the difference in subjective opinions; therefore, forecasts are revised and generalized in the search for a common opinion.

The complexity of the method is the observance of anonymity, as a result of which it is virtually impossible to influence the opinion of analysts from the outside, which protects against political bias. The anonymity of this method also provides other advantages, for example, the ability to gather a diverse pool of experts with different scientific and critical perspectives and backgrounds. Thus, such a method turns out to be more economically profitable and culturally diverse since it becomes possible to collect information from a specialist from any geographic location.

This method requires equality between each of the experts, the presence of a reasonable judgment, as well as not too long a time between rounds for which a consensus opinion is agreed upon. The Delphi method is actively used in industries related to scientific and technological progress. Obviously, this is due to the fact that Delphi is the most formal method of evaluative prediction; that is, it gives the most statistically corrected view of the prediction made by an expert and not by a machine. This method can be used in such industries as space exploration and futurological predictions, as well as in the production of technology, such as smartphones and devices.

References

Hyndman, R.J., & Athanasopoulos, G. (2018) Forecasting: principles and practice, 2nd edition. OTexts: Melbourne, Australia. OTexts.com/fpp2.

Kim, H. Y., Lee, Y. S., Jun, D. B. (2020). Individual and group advice taking in judgmental forecasting: Is group forecasting superior to individual forecasting? Journal of Behavioral Decision Making, 33(3), 287-303. Web.

Sniezek, J. (1990). A comparison of techniques for judgmental forecasting by groups with common information. Group & Organization Studies, 15(1), 5-19. Web.

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