Adapting to Change: Leveraging Decision Theory for Business Success - Essay Sample

Introduction

The business environment has significantly changed over the years. It has become increasingly volatile and unpredictable, requiring businesses to enhance their flexibility and ease of making critical decisions. In light of this, businesses are applying decision theory more and more. Decision theory can be described as an interdisciplinary approach used by businesses to arrive at the most favorable decisions in uncertain circumstances. The theory incorporates elements of different disciplines such as mathematics, statistics, philosophy, and psychology to analyze the process of decision-making critically. This paper seeks to discuss the applications of the decision theory in business and economics. A case study of Gerber will also be presented. The paper will be divided into four parts. First, the different elements and areas of the decision theory will be introduced. The second section will give a literature review of the development and application of the decision theory. The case study will then be presented, and the conclusions and recommendations will be the final part.

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Events, acts, outcomes, and payoffs are the main elements of the decision theory. Acts refer to the actions that agents consider. Events are the occurrences that take place but are beyond the agent's control. The results of the occurrence of the acts and events are known as the outcomes. Finally, payoffs refer to the values that the decision-maker places on the occurrence.

The four elements are represented in the figure above. Taking a raincoat or not, is the act. Presence or absence of rain is the event, and as discussed, it is beyond the control of the decision-maker. Staying dry or getting wet, as well as being burdened by the raincoat or not, are the outcomes of the occurrence of the event or lack of it. The value of bearing the burden of the raincoat and remaining dry or being free of the nuisance and getting wet is the payoff.

Descriptive, normative, and prescriptive aspects are the main areas of the decision theory. The descriptive decision theory focusses on the examination of how irrational human beings make decisions. According to theorists, people regularly act in ways that are considered to be irrational. This area, therefore, seeks to explain as well as predict how people make decisions. As such, this area is an empirical discipline with roots in experimental psychology (Peterson, 2011). Descriptive decision theory also takes into account external factors that affect the decisions of the actor towards less rational and optimal ends. The prescriptive decision theory seeks to offer guidelines that can be used to make the best decisions in an uncertain environment. Prescriptive decision-making focusses on what one should and can do. Finally, the normative decision theory gives guidance for making decisions following a specific set of values. It seeks to find out what decision-makers ought to or are required to do (Peterson, 2011). The actors are assumed to be rational, and hence makes the decision that is most ideal in the situation. The actors also arrive at the highest expected outcome with high levels of accuracy. As such, normative theory applications theory are ideal and are only applied in the creation of methodologies and software.

Besides, the decision theory framework consists of three class of decisions. First, there is the decisions under certainty class. There exists enough information in this class, and hence obvious and easy decisions are made. Decisions under uncertainty is the second class. This class calls for the analysis of the known and unknown variables. Ultimately, the best probabilistic decision is made. Finally, there is decisions under conflict class. This class usually takes a reactive approach and involves predicting the potential repercussions of the decision before making it.

Literature Review on the Development and Techniques of Decision Theory

To understand the development of decision theory, it is important to first look at the evolution of probability. The evolution of probability began in the 17th century during the Enlightenment period. It was championed by great minds such as Pascal and Fermat. Particularly, the two solved the Problem of Points (Goswami et al., 2019). In collaboration with other academics, they tackled the inherent vagaries of beliefs and ultimately established evidence-based inquiry. Decision theory was the next major milestone in the evolution. With the rise in the use of mathematical probabilities, it became increasingly easier to predict outcomes rationally. For instance, decision theory is closely related to the concept of expected value. In statistics, expected value refers to the sum of all the possible values of a variable when multiplied by the probability of its occurrence. Evolution of probability made finding the expected values of measured events easier. Decision theory, therefore, according to the scholars, was based on finding the highest possible expected value (Tarar, 2018).

However, on that basis, the theory did not apply to variables that were not numbers. Blaise Pascal sought to tackle this challenge when developing the decision theory in the famous wager experiment (Tarar, 2018). Though he sought to answer the unanswerable question of whether God exists, the experiment was not inherently religious. The experiment was critical in helping identify the highest expected value when using unknown probabilities. He held that one's belief in God is less relevant as compared to the expected value of one's decision to believe. By doing so, he helped see the belief in God from a cost and benefit perspective. He assumed that the belief in God carries better rewards and fewer risks than disbelief. As such, when one believes in God, the risks are reduced, and the expected value rises. Though the experiment lacks quantifiability, it explains how one can arrive at an optimal decision. Pascal's Wager did not only help develop decision theory but also helped distinguish between normative and descriptive decision theory (Tarar, 2018).

Decision theory would then undergo significant developments in the following centuries. In the 18th century, the axiomatic theory of utility which presents a way of choosing between actions with uncertain consequences. According to the theory, a rational decision-maker assigns numerical utilities to the consequences and then scores the actions according to their expected utility. This concept is seen in Bernoulli's St. Petersburg paradox (Bhattacharya & Podder, 2017). Ramsey and Savage further developed the axiomatic system in the 20th century. Savage's theory integrated the coherence and utility concepts to create a theory that leant more towards individual decision-making. It also heavily relies on the ability to distinguish judgment of utility from judgment of probability in the evaluation of an action's worthiness. Anscombe and Aumann identified more conditions for the separation (Hokari & Yao, 2016). It is also in the 19th century that Wald developed statistical decision theory (Manski, 2019). Today, decision theory is employed in different fields, including engineering, medicine, and business. In business, the theory is used to help the management make critical decisions in the face of uncertain times. Over the years, different techniques of decision theory have been developed.

Techniques of Decision Theory

As mentioned in the introduction, decision theory borrows from different disciplines, including psychology, mathematics, and statistics. As such, there exist different techniques of decision theory. The techniques can broadly be divided into quantitative and non-quantitative. Quantitative techniques involve the use of mathematical and statistical data and reasoning to make decisions. Non-quantitative decision making is simple and majorly depends on the decision maker's experience and intuition. Experimentation, brainstorming, and synectics also fall in this category. However, for the purposes of this paper, only the quantitative techniques will be discussed. Quantitative techniques largely depend on facts. The use of factual data makes quantitative techniques the most preferred mode of decision-making today. Various software programs have been developed over the years to help in the analysis of big data and facts. Some of the quantitative techniques of decision theory include operations research, marginal analysis, correlation, and linear programming.

Operations Research

This technique was developed and applied in military operations during World War II. It is a systematic method of evaluating the basic structure, functions, as well as the relationships of an organization or a business, looking at it as an open system. The aim of the technique is to develop optimal solutions using limited resources. The technique scientifically analyses the interaction between various components in an organization, hence helping managers make appropriate decisions (Hazen et al., 2018). Generally, the management identifies the problem to be solved and constructs a model around it. The model is then used to get solutions. Each of the solutions is also tested and analyzed before it is implemented.

Marginal Analysis

This technique compares additional costs with additional revenues. In other terms, this technique examines the additional benefits of a certain activity against the additional costs that the same activity is expected to incur (Anderson & Eidler, 2017). The extra cost is then compared with the expected benefit. Using the technique, therefore, managers can make decisions that maximize the potential profits.

Correlation

This technique studies the extent of functional relationship between multiple variables. It is useful in estimating one variable, if the value of the other is known (Garg, 2016). For instance, it can be used to determine the expected profit if the demand is known.

Linear Programming

This technique helps the management of an organization determine how to best use limited resources to achieve the set objectives. This goal can be achieved either through minimalization of a disutility or optimization of a utility. It is founded on the assumption that the relationship between the limits of variations and variables is linear and can be certain. As such, the technique can only be used under conditions that involve certainty (Aliyev, 2017). In uncertain or unpredictable circumstances, the management might be forced to employ another technique. Some of the fields in which the technique can be employed include warehousing, transportation, and production.

Simulation

Using this technique, the management of an organization observes different outcomes under artificial or hypothetical settings. The observed outcomes then help the managers understand the consequences of the intended decisions under different circumstances (Ryzhov & Chen, 2017). As such, they pick the decision that is observed to be most beneficial. The technique, therefore, drastically reduces risks.

Game Theory Technique

Managers majorly use this technique to develop decisions to overcome competition and rivalry. The business simulates the conflicts or rivalries between organizations as a game. Through it, managers find ways of overcoming rivalry or gaining at the rivals' expense (Fang et al., 2016).

Application of Decision Theory in Different Business Areas

For busin...