Decision and Risk Analysis

Abstract

Decision and risk analysis helps organizations make decisions to maximize their utility in the presence of risk and uncertainty. It helps them make a risk-informed decision. The entire approach to developing alternative scenarios and evaluating them using tools and methods of decision analysis has undergone highly practical improvements in the last few decades.

These improvements have largely to do with the need to engage senior decision-makers directly. For the larger organizations, the approach is to maximize expected value, balancing rewards, and costs with uncertainty. Medium and smaller organizations may be risk averse. This means that they have a non-linear utility function, usually emphasizing avoiding negative outcomes, and maximizing expected utility.

1 Introduction

People decide according to their knowledge of the world around them. This knowledge determines what they believe they can do to achieve their goals. Thus, to obtain a more accurate and comprehensive knowledge it is essential to have an investigative attitude, involvement with things in the real world, and in the environment that surrounds them. The greater the wealth of information, the greater the possibilities of quality knowledge that enhances intelligence in decision-making.

The same is true of organizations. Without information, they cannot guarantee their continuity, let alone organizational success. In the information resides the main repository of knowledge, representing the great competitive advantage for organizations, allowing them to make the best decision at all times.

2 Decision-Making

Decision-making—identifying and choosing a course of action to solve a specific problem or seize an opportunity—is an important part of every manager’s job. Needless to say, we all make decisions. What differentiates this management exercise is the systematic and specialized attention that managers pay to decision-making.

In an organization where decisions are made collectively or when they impact several people, managers need to share their knowledge with others. However, this requires the deliberate effort of work teams to produce collective knowledge options and share them intensively. Otherwise, the observations and information collected individually by a member of the organization will be isolated and will not be effective for the quality of the organizational decision-making process.

Thus, from an organizational perspective, good decisions depend mainly on sensitivity to perceive what is happening around, experience to perceive and understand what the facts mean, and competence to share acquired knowledge, as shown in ◘ Fig. 7.1.

Fig. 7.1

Organizational learning process

Source: Elaborated by author from Nonaka, I; Takeuchi, H. The knowledge-creating company. Harvard Business Review, v. 85, n^o. 7/8, 2007

2.1 Knowledge as the Most Valuable Currency

Organizational equity should not be measured solely on the basis of accounting and material assets—which undergo continuous degeneration and depreciation—nor by their current market position—which can change at any moment. What the organization knows is what it’s worth. Knowledge became the most valuable currency. And the most valuable knowledge is rare and difficult to build. Organizational memory is still too fragile to take advantage of it properly.

There is a long way to go to achieve knowledge and improve it in the organizational context. For Goldberg [1], there are five different levels to address information. Information is presented as simple data, as information itself, as knowledge, and as intelligence, whose highest expression is wisdom.

• 1. Data—They consist of facts, texts, graphics, images, sounds, and records not yet processed, correlated, evaluated, or interpreted.

• 2. Information—This is data that has already received some processing and can be presented in an intelligible way by users who depend on it to make their decisions. The process of transforming the data into information involves the categorization under some criteria, the application of some statistical calculation, or editing of texts highlighting the relevance of the data presented. Graphs and other presentation techniques are often used to facilitate users’ understanding of the reliability, relevance, and importance of the information. Thus, information is a set of meaningful data.

• 3. Knowledge—It is obtained by interpreting, combining, and integrating various information that leads to the understanding of the situation studied. Knowledge is the result of a continuous learning process and changes with each interaction with the environment, the result of the assimilation of new knowledge to pre-existing cognitive structures. Thus, knowledge is properly structured information.

• 4. Intelligence¹—It is knowledge contextualized and applied with a purpose. Intelligence is a synthesis between different cognitive structures developed with the experience and intuition of individuals, being represented by an elaborate visualization (insight) of a given situation (scenario). It is this visualization of a situation that allows for the smartest decision-making. Associated with the capacity for synthesis is inference, which is a purely human capacity to predict new events, from current knowledge and the evaluation of the consequences and effects of a decision, as an anticipated response to events that must happen.

• 5. Wisdom—The highest stage of the informational process is reached when wisdom underpins the decision-making process. At this stage, the syntheses and inferences are the result of a great previous experience, and decision-making constitutes an almost magical act, because in a period of time, a large volume of previous knowledge is rescued and classified and solutions are presented, and an observer who does not enjoy this wisdom simply cannot understand how the process took place.

Finally, organizational intelligence is a function of regular and systematic monitoring of the external environment through information about the market, technology, customers, competitors, and socioeconomic trends. It is an essential activity for decision-making because no decision can be better than the information from which it derives.

As we see more and more, the value of knowledge surpasses the value of previous experience. It is that the previous experience reflects the past. And the past has been and does not always serve as an example for the present or the future. The present is different from the past, and the future will be different from what we do now.

Thus, knowledge today has become the most valuable currency in the market. It turns out that knowledge also presents an increasingly short life cycle and undergoes constant transformations. The change in which we live—the only constant of our life—makes the obsolescence of knowledge faster and faster.

For Nonaka and Takeuchi [2], the word knowledge has several meanings today. It can mean cognition, awareness, knowledge, awareness, wisdom, perception, science, experience, qualification, discernment, competence, practical ability, ability, learning, wisdom, certainty, etc. The definition depends, of course, on the context in which the term is employed.

For Sveiby [3], in practice, knowledge has three characteristics:

• 1. Knowledge is tacit—It is knowledge that is in people’s heads, and it is derived from their personal experiences and experiences, being transmitted in a vague and unstructured way, often unconsciously. It represents the knowledge of what is known, but which cannot be verbalized or written in words, as understood by Polanyi [4].² The author uses the following sentence to define what tacit knowledge is: “We can know more than we can tell.”

  It is the most common knowledge within the organization, but it is not its property. The cost of sharing tacit knowledge is high because it rests on direct face-to-face communication. Its transfer is inefficient, as it is often an unconscious knowledge.

• 2. Knowledge is dynamic—The human being is always generating new knowledge through the analysis of the sensory impressions he or she receives from the real world and losing the old knowledge. To explain how we acquire and generate new knowledge by applying to our sensory perceptions the capacities and facts we already have, Polanyi—the creator of the theory of tacit knowledge—uses the expression “process of knowing,” which corresponds to the gathering of data and information fragmented into analogous categories, organized by theories, methods, feelings, values, and skills [4].

• 3. Knowledge tends toward standardization—Over time, the brain creates numerous patterns that act as unconscious rules of procedure for dealing with every conceivable type of situation. These rules save a lot of energy and allow you to act quickly and effectively without having to stop to think about what you are doing. The patterns are also tied to the outcome of the actions. What’s more, standards act as filters for new knowledge. The greatest difficulty is not in persuading people to accept new things, but in persuading them to abandon old ones, John Maynard Keynes said.³

2.2 Decision Definition

The word “decision” comes from the Latin word “decisio,” which means “a cutting off, a determination, a decision.” The Latin root word “decidere” means “to cut off” or “to decide.” This word is formed from the prefix “de,” which means “off” or “away,” and the verb “caedere,” which means “to cut,” or “to split.”

So, originally, the word “decision” referred to the act of cutting off or eliminating alternative options in order to arrive at a conclusion or determination. Today, the word is still used in a similar sense, to mean the act of making a choice or arriving at a resolution after considering multiple options or possibilities.

Deciding can also be defined as a process of gathering information, attaching importance to it, subsequently seeking possible alternative solutions, and then making the choice between alternatives (deliberating) offering the solution.

For Hammond et al. [6], objectives help determine what information should be obtained, allow you to justify decisions to others, establish the importance of a choice, and establish the time and effort required to accomplish a task.

Decision-making can be evidenced in the simplest daily attitudes, such as what to do for fun: watch television shows, listen to music, or read a book. A first decision may entail others, for example: when choosing in which educational institution to study, within the educational institution, the individual must decide which courses he will take and, as a consequence, where to buy books, etc.

2.3 Making Complex Decisions

Making complex decisions and, in general, one of the most difficult tasks faced individually or by groups of individuals, as such decisions must almost always meet multiple objectives and often their impacts cannot be correctly identified. According to [7], the challenge for groups involved in a decision is to transform a set of individual decisions into a joint one.

Decisions are made, sometimes using quantitative parameters, sometimes using qualitative measurement parameters, with subjective characteristics. Quantitative parameters are usually easier to measure than qualitative parameters. The decision-maker has to glimpse the consequences of decisions in a changing environment subject to conditions that the decision-maker cannot control and with uncertainty, vagueness, and/or ambiguity.

According to Burchell et al. [8], uncertainties have a direct effect on the way the decision-making process in the organization is carried out which leads to decision-making, using quantitative and qualitative parameters. How Miettinen and Salminen explain [9], in many real-world situations in which the decision-maker engages with multiple decision criteria, the values to be assigned to rank the alternatives in the criteria or even the importance of the criteria may be made with inaccurate numbers. The following situation exemplifies the above:

It is desired to solve the problem of hunger in a community, and the alternatives to solve the problem could be:

• (a) To subsidize the food so that everyone could buy it with their wages.

• (b) To institute an allowance for the needy.

These two proposals would solve the problem. The measurement of the costs in the subsidy and in the value of the salary is of a quantitative nature, but the social impact of the measures will have a different qualitative evaluation.

In the same way, another group could argue that there is no point in feeding sick people, and in that case, the money should be sent primarily to health; likewise, another expert(s) might argue that there is no point in treating a “starving patient.”

In the case of scarce resources, it could mean that only one alternative could be implemented, and the choice would have a technical and subjective character at the same time, revealing itself to be a paradox. How to decide?

When faced with a complex decision like this, there are several factors that should be taken into consideration. One approach could be to use a multi-criteria decision analysis (MCDA) framework that involves identifying, weighing, and evaluating various factors in a systematic way. Some factors that could be considered in this case are:

• Cost-effectiveness: Both proposals have a cost associated with them, and it is important to evaluate which option provides the most value for money in terms of the number of people that can be helped and the impact on hunger reduction.

• Equity: It is important to consider how each proposal would affect different groups within the community, such as the elderly, children, and people with disabilities. Would one proposal be more likely to benefit certain groups more than others?

• Social impact: Both proposals have social implications that should be considered. For example, would one proposal create dependency or disincentivize work? Would one proposal be more likely to promote social cohesion and community engagement?

• Health impact: It is important to consider how hunger affects people’s health, and how each proposal could impact the health outcomes of the community. For example, would an allowance for the needy lead to better nutrition and health outcomes than subsidizing food?

• Sustainability: It is important to consider the long-term sustainability of each proposal. Would one proposal be more sustainable in the long run, or would it create unintended consequences that could make the problem worse over time?

Ultimately, the decision will have both technical and subjective elements, and it is important to involve stakeholders in the decision-making process to ensure that their perspectives and concerns are considered. The decision should be based on a careful evaluation of the available evidence, and a thorough analysis of the trade-offs and benefits associated with each proposal.

According to Kahane [10], people are motivated and will prefer to decide for change, to do something, just if they find themselves in a situation that has the following three characteristics.

• First, these people see the situation they are in as unacceptable, unstable, or unsustainable. Their situation may have been this way for some time, or it may be becoming this way now, or it may possibly become this way in the future. They may feel frightened or excited or confused. In any event, these people cannot or are not willing to carry on as before, or to adapt to or flee from what is happening. They think that they have no choice but to try to transform their situation.

• Second, these people cannot transform their situation on their own or by working only with their friends and colleagues. Even if they want to, they are unable to impose or force through a transformation. The larger social-political-economic system (the sector or community or country) within which they and their situation are embedded is too complex—it has too many actors, too many interdependencies, and too much unpredictability—to be grasped or shifted by any one person or organization or sector, even one with lots of ideas and resources and authority. These people therefore need to find some way to work together with actors from across the whole system.

• Third, these people cannot transform their situation directly. The actors who need to work together to make the transformation are too polarized to be able to approach this work head-on. They agree neither on what the solution is nor even on what the problem is. At best, they agree that they face a situation they all find problematic, although in different respects and for different reasons. Any attempt to implement a solution directly would therefore only increase resistance and rigidity. So the transformation must be approached indirectly, through first building shared understandings, relationships, and intentions.

3 How to Identify Problems

Decision-making deals with problems. A problem is a situation in which a solution is being tried to achieve some result, and a means must be found to get there. Figuring out puzzles, solving algebra problems, deciding how to budget a limited amount of money, trying to control inflation, and reducing unemployment—these are all examples of problems that we as individuals and as a society encounter all the time.

Clearly, these problems cover an enormous range of difficulty and complexity, but they do have some things in common. They all have some initial state, whether it is a set of equations or the state of the economy, and they all have some goal. To solve the problems, it is necessary to perform some operations on the initial state to achieve that goal. Often, there are some rules that specify allowable operations, and these are generally called constraints.

A problem arises when the actual state of affairs does not conform to the desired state. In many cases, a problem can represent a disguised opportunity. The problem of customer complaints due to delays in order deliveries, for example, can also be considered an opportunity to redesign production processes and customer services.

For Malczewski and Rinner [11], a decision needs to be made whenever one is faced with a problem that has more than one alternative for its solution. One should, then, chooses an alternative of action among many, a solution to a problem or a goal that is expected to be achieved. Decisions are necessary when an opportunity or problem exists, or when something is not what it should be, or when there is an opportunity for improvement or optimization.

Even when, to solve a problem, there is a single action to take, there are alternatives to take or not to take that action. Basically, a decision-making process boils down to a decision-maker’s choice (it can be a person, a team, a committee, an organization, etc.) of the best alternative among the possible. According to Diaz-Balteiro and Romero [12], the analytical problem lies in defining the best and the possible in a decision process.

The decision process requires the existence of a set of feasible alternatives in which the choice of a feasible alternative (decision) has associated a gain and a loss. For Zeleny [13], decision-making and an effort to try to solve problems(s) of conflicting objectives, the presence of which prevents the existence of the optimal solution and leads to the search for the “best compromise.”

The human being is thus forced to glimpse the consequences of decisions in a changing environment and subject to conditions that the decision-maker cannot control, and with uncertainties, imprecision, and/or ambiguity. It is emphasized that subjectivity is not the opposite of objectivity, the opposite of subjectivity, and the lack of objectivity. A subjective decision can be objective.

3.1 Detecting Problems Process

William Pounds ([14]; see also [15]) said the process for detecting problems is often informal and intuitive. As a rule, there are four situations that alert managers when a problem may arise:

• 1. A deviation from past experience means that an existing pattern of organizational performance has been broken. For example: current year’s sales are lower than the previous year’s; expenses skyrocketed, employee turnover increased. Facts like these signal to the manager that there is a problem.

• 2. A deviation from the established plan means that managers’ projections or expectations are not being met. The number of profits is less than expected; one department exceeded its budget; a project does not conform to the schedule. These circumstances signal to the manager that something must be done to get the plan back on track.

• 3. Other people often present problems to the manager. Customers complain of delays in delivery; managers at senior levels set other standards for the results of the manager’s department; employees resigned. Many of the decisions that managers make, every day, involve problems that have been presented to them by third parties.

• 4. Competition performance can also produce situations that require problem-solving. When other companies develop new processes or improve their operating procedures, the manager may need to reevaluate their organization’s processes or procedures.

The first experimental work on solving human problems was done by Gestalt psychologists, notably Wolfgang Köhler, Otto Selz, Karl Duncker, Abraham S. Luchins, Michael Maier, and George Katona. They focused on multi-step tasks where only a few of the steps to be taken were crucial and difficult. Such problems are called insight problems because the solution follows quickly once the crucial steps have been taken.

Vanlehn [16] gives an example of such a task with the construction of a wall-mounted candlestick from a strange variety of materials, including a candle and a stud-box. The materials are chosen in such a way that the only solution involves using the box as a support for the candle, attaching it to the wall. To find this solution, subjects must change their belief that the box is just a container for the studs and instead see the box as a building material. This change of belief is a crucial and insightful step. Once done, the solution is soon reached.

In contrast, most troubleshooting research refers to multi-step tasks where no single step is the key. Instead, finding a solution depends on doing a series of correct steps. An example of such a task is to solve an algebra equation. The solution is a sequence of appropriate algebraic transformations, correctly applied.

Thus, responsibility for the solution is distributed throughout the solution process, rather than falling into the discovery of one or two main steps. This choice of tasks has made the research focus on how people organize the solution process, how they decide what steps to take under what circumstances, and how their knowledge of the task domain determines their view of the problem and their discovery of its solution.

Most research today concerns tasks that do not require special training or background knowledge. Everything the subject needs to know to perform the tasks is presented in the instructions. A classic example of such a task is the Tower of Hanoi.

The Tower of Hanoi is a classic mathematical puzzle that involves moving a stack of disks from one peg to another, with the constraint that you can only move one disk at a time, and you can’t place a larger disk on top of a smaller one.

Here’s an example of how you might solve the Tower of Hanoi problem for a stack of three disks:

• 1. Start with all three disks on the left peg (which we’ll call Peg A).

• 2. Move the top disk from Peg A to the middle peg (which we’ll call Peg B).

• 3. Move the bottom disk from Peg A to the right peg (which we’ll call Peg C).

• 4. Move the top disk from Peg B to Peg C.

• 5. Move the only remaining disk (the larger one) from Peg A to Peg B.

• 6. Move the top disk from Peg C to Peg A.

• 7. Move the only remaining disk (the larger one) from Peg B to Peg C.

• 8. Move the top disk from Peg A to Peg B.

• 9. Move the only remaining disk (the larger one) from Peg C to Peg A.

• 10. Move the top disk from Peg B to Peg C.

At this point, all three disks have been moved from Peg A to Peg C. The key to solving the Tower of Hanoi problem is to realize that you need to move the largest disk to the third peg first, and then work backward from there, moving the remaining disks one at a time.

Simon and Newell’s landmark theory of problem-solving [17] is a cognitive model that seeks to explain how people solve problems by breaking them down into subgoals, or intermediate steps. According to this theory, problem-solving involves a process of searching through a space of possible solutions, where each solution is defined by a set of intermediate states, or landmarks, that must be achieved in order to reach the final goal.

The landmark theory posits that problem-solving is guided by heuristics, or rules of thumb, that help individuals to navigate the search space more efficiently. These heuristics are based on a person’s knowledge of the problem domain and may include strategies like working backward from the goal state, generating and testing hypotheses, and breaking the problem down into smaller subproblems.

The authors illustrate the landmark theory with several examples, including the Tower of Hanoi problem that is explained and discussed above. They argue that when solving the Tower of Hanoi, people use a set of heuristics to identify the intermediate states that must be achieved in order to move the disks from the starting peg to the goal peg. These heuristics might include rules like “move the smallest disk first,” or “never place a larger disk on top of a smaller one.”

The landmark theory has been influential in the field of cognitive psychology and has been used to explain a wide range of problem-solving phenomena. However, some critics have argued that the theory oversimplifies the problem-solving process and that it doesn’t take into account the role of creativity or insight in solving problems. Nonetheless, the landmark theory remains an important model for understanding how people solve problems and has inspired numerous studies on problem-solving and decision-making.

The overall problem-solving process can be analyzed as two cooperating subprocesses, called understanding and search:

• (a) The understanding process is responsible for assimilating the stimulus that poses the problem and for producing mental information structures that constitute the person’s understanding of the problem.

• (b) The search process is driven by these products of the understanding process, rather than the problem stimulus itself. The search process is responsible for finding or calculating the solution to the problem.

To put it differently, the understanding process generates the person’s internal representation of the problem, while the search process generates the person’s solution.

It is tempting to think that the understanding process runs first, produces its product, and then the search process begins according to explanation of Hayes and Simon [18]. However, for Chi et al. [19], the two processes often alternate or even blend together. If the problem is presented as text, then one may see the solver read the problem (the understanding process), make a few moves toward the solution (the search process), and then reread the problem (understanding again). Although some understanding is logically necessary before a search can begin, and indeed most understanding does seem to occur toward the beginning of the problem-solving session, it is not safe to assume that understanding always runs to completion before the search begins.

4 How to Spot Opportunities

There is a lot of research that addresses how to solve problems, but very little in terms of how to detect problems and even less about how to detect opportunities. Opportunity is a situation that arises when circumstances offer the organization the possibility of exceeding the goals and objectives established.

For Graham [20], problems are but opportunities in disguise. It is not always clear whether a situation represents a problem or an opportunity, because many times they can be intertwined. Passing up opportunities can cause problems for organizations, while studying problems can often find opportunities.

According to David B. Gleicher,⁴ problem is that which jeopardizes the ability of the organization to achieve its objectives and opportunity is that which offers the possibility of exceeding objectives.

For Schweiger and Finger [21], the method of dialectical research, sometimes called the devil’s advocate method, is very useful for solving problems and detecting opportunities. With this method, the person who makes the decision determines the possible solutions and the assumptions that support them, then raises the opposite.

The method of dialectical research is a method of analysis in which the decision-maker determines and rejects his or her assumptions and then creates “counter-solutions” based on the opposing assumptions of all hypotheses and from there develops contrary solutions from the opposing assumptions. This process can generate more alternatives of useful solutions, as well as detect opportunities that have gone unnoticed.

Livingston [22] clearly states that opportunities—not problems—are the key to the success of organization and management. For the author, solving a problem simply restores normalcy, but that progress necessarily comes from exploiting opportunities.

When decision-making is linked to detecting opportunities, this clearly implies choosing actions that can contribute to creating the future of the organization.

5 Problem Types by Their Complexity

The OLC—Organizational Learning Center of MIT—Massachusetts Institute of Technology research focused on helping leaders to cope with problems that are high on both dynamic complexities, according to Ackoff’s work [23], and behavioral complexity, according to Mitroff and Kilmann’s work [24]. Senge and Scharmer [25] believed that a third dimension needs to be added: generative complexity, as explained below.

Dynamic complexity characterizes the extent to which cause and effect are distant in space and time. In situations of high dynamic complexity, the causes of problems cannot be readily determined by first-hand experience. Few, if any, of the actors in a system are pursuing high-leverage strategies, and most managerial actions are, at best, ameliorating problem symptoms in the short run, often leaving underlying problems worse than if nothing at all was done. Such problems can only be understood in a systemic way, taking into account the interrelations of its parts and the functioning of the system as a whole.

Behavioral complexity describes the diversity of mental models, values, aims, and political interests of the players in a given situation. Situations of high behavioral complexity are characterized by deep conflicts in assumptions, beliefs, world views, political interests, and objectives.

A problem has a low social complexity if the people who are part of it have common assumptions, values, logics, and goals. A problem has a high social complexity if the people involved see things very differently from each other. Problems of high social complexity cannot be peacefully solved by the authorities at the top; the people involved need to participate in the creation and implementation of solutions.

Generative complexity—A problem has a low generative complexity if its future is known and predictable. A problem has a high generative complexity if the future is unknown and unpredictable. Solutions to problems of high generative complexity cannot be calculated beforehand, on paper, based on what has worked in the past, but they need to be developed as the situation unfolds.

In the new economy, generative complexity arises from the tension between “current reality” and “emerging futures.” In situations of low generative complexity, it is dealing with problems and alternatives that are largely familiar and known—wage negotiations between employers and unions are an example of high dynamic and behavioral complexity but low generative complexity (non-obvious causality, different interests, given alternatives).

In situations of high generative complexity, it is dealing with possible futures which are still emerging, largely unknown, non-determined, and not yet enacted (non-obvious causality, different views, not yet-defined alternatives).

In retrospect, throughout the 1990s, the Senge and Scharmer research focus had steadily shifted from traditional “wicked messes” of medium or low generative complexity to wicked messes that are also high in generative complexity. The challenge in this kind of environment is how leaders can cope with problems that (a) have causes difficult to determine, (b) involve numerous players with different world views, and (c) are related to bringing forth emerging futures?

5.1 Paradoxical Problem

A paradoxical problem is one that, based on false reasoning, leads to incorrect results.

When an idea is given the form of a problem, certain tacit, implicit presuppositions must be satisfied when the activity is to make sense. Among these is, of course, the assumption that the questions raised are rational and free from contradictions. Sometimes, without realizing it, we accept absurd problems with false and self-contradictory assumptions. In the practical and technical realm, however, in general, sooner or later we will find that our question is unreasonable, and then we dismiss the problem as “meaningless.”

However, it will not always be possible to rule out a contradictory problem. Describing such a situation as a “problem” only creates confusion. Instead, it would be better to say that we are facing a paradox.

In this case, what is required is not a procedure that will “solve this problem.” Faced with a paradox, there will be no way to put things in place. As long as a paradox is treated as a problem, it can never dissolve. On the contrary, the “problem” will do nothing more than increase, in an ever-increasing confusion. For an essential feature of thought is the fact that when the mind accepts a problem, it is considered appropriate for the mind to remain active until a solution is found.

Thus, if a person were faced with a real problem (e.g., the need to obtain food) and discarded it before it was solved, the result could be disastrous. In any case, such a way of acting would indicate an unhealthy levity or lack of seriousness. On the other hand, if the mind treated paradoxes as if they were real problems, it would always be a prisoner of them, for paradoxes have no solution. Every solution that came up would be considered inadequate and would only lead to new, even more confusing issues.

5.2 Polarity as Paradox

Barry Johnson’s book, Polarity Management [26], presents a framework based on the understanding that many of the challenges we face in life are not problems to be solved once and for all, but rather polarities to be managed over time. A polarity, as paradox, is a pair of interdependent and seemingly opposing forces or values, such as stability and change, or individualism and collectivism.

For managing polarities, it is required ongoing management and balancing of seemingly opposing forces.

Johnson suggests that polarities can be managed effectively by identifying the positive aspects of both poles, understanding how they are interconnected, and developing a comprehensive plan to manage them over time. This involves recognizing that both poles are necessary and interdependent and that they need to be managed in a way that maximizes the benefits of each pole while minimizing the negative effects of either pole.

Johnson also emphasizes the importance of developing a mindset of polarity thinking, which involves reframing problems as polarities and focusing on the interdependence of the poles. This mindset can help individuals and organizations to manage complex and recurring challenges more effectively and to avoid getting stuck in either/or thinking.

By recognizing the value of both poles and developing a comprehensive plan for managing them over time, individuals and organizations can more effectively navigate these challenges and achieve their goals. Johnson’s framework for managing polarities includes several key concepts:

• Polarity: A polarity is a pair of interdependent and opposing forces or values that are both necessary and can’t be fully resolved by choosing one over the other. Examples of polarities include stability and change, autonomy and collaboration, and short-term and long-term focus.

• Positive pole: Each polarity has a positive pole, which represents the benefits and advantages of that pole. For example, the positive pole of stability might be security, while the positive pole of change might be innovation.

• Negative pole: Each polarity also has a negative pole, which represents the disadvantages and drawbacks of that pole. For example, the negative pole of stability might be stagnation, while the negative pole of change might be chaos.

• Interdependence: The two poles of a polarity are interdependent and cannot exist without each other. Trying to eliminate or suppress one pole will inevitably lead to negative consequences.

• Polarity Map: A polarity map is a visual representation of a polarity that helps to identify and understand the interdependence of the two poles, as well as the positive and negative aspects of each pole.

• Polarity thinking: Polarity thinking is a mindset that involves recognizing and managing polarities as interdependent and necessary forces, rather than trying to solve them as problems. This approach helps to avoid getting stuck in either/or thinking and allows for more creative and effective solutions.

Overall, Johnson’s concept of polarity management provides a useful framework for understanding and managing the interdependent and recurring challenges that we face in life. By recognizing the interdependence of polarities and developing a comprehensive plan for managing them over time, individuals and organizations can more effectively navigate these challenges and achieve their goals.

6 Solutions Strategies Approaches

Representation—The representation of a problem consists essentially of the solver’s interpretation or understanding of the problem. Researchers have found that the representation is very important in determining how easy a problem is to solve. In the two-string problem, for instance, insight is really representation. The initial representation of the screwdriver must be broadened to include the fact that it is a heavy object, which can therefore be used as a pendulum weight.

Two strings are hung from the ceiling. The object is to tie them together, but they are too far apart for a person to reach both of them at the same time. On the table nearby are a box of matches, a few pieces of cotton, and a screwdriver. In solving this problem, the insight comes in recognizing that the screwdriver can be used differently than in its usual function. In this case, it may be tied to one string, to create a pendulum that can be swung to the other string.

This early research on problem-solving focused on the conditions that impeded or facilitated problem-solving and how intuitively improbable responses could become more probable. For example, would embedding the screwdriver among a different set of items on the table make it easier for the solver to recognize (and break the functional set) that a screwdriver can be used in an unfamiliar way, as a weight.

Searching for a solution—The process of finding a solution to a problem can be visualized as a search through the paths in the problem space until one that leads to the goal is found.

There are a variety of strategies for carrying out this search. One strategy is to try paths randomly, hoping to stumble upon the goal. Random search is adequate if the search space is small. However, since the search space expands exponentially for most problems, the chance of random search being successful is quite small.

Another obvious strategy is systematically searching the entire tree. In a depth-first search, for instance, a particular path is searched all the way to the bottom. If this state is not the goal, the search backs up one level and then starts down again via an untried path. When all paths from a particular state have been tried, the search backs up one more level and starts down again, etc.

This method (and any such exhaustive method) requires quite a bit of record-keeping track of which paths have been tried and which state should be backed up to when all links have been tried. Except for very simple problems, the memory required for this record keeping is too great for human beings. Exhaustive methods are often not even feasible for computers to use.

The key to the effective strategies used by humans is to significantly reduce the search space by considering only one or a very few branches at each point. For instance, de Groot [27] found that chess players use a strategy that can be called depth-first because they initially follow one path straight down for a few moves. They make no attempt to be exhaustive or to backtrack systematically, however. Instead, they tend to jump back to the beginning position or to some important intermediate point and gradually explore just a few alternative branches.

Obviously, if only a few alternatives at each point are going to be explored, they had better be good ones, so good strategies are those that guide the selection of promising moves or the elimination of unpromising ones.

Means/ends analysis—A powerful strategy for finding good alternatives, which takes the goal state into account, is means/ends analysis. This strategy was used as an important search strategy in one of the earliest attempts to create a computer program to solve problems, the GPS—General Problem Solver developed by Ernst and Newell [28].

The general idea is to determine what differences exist between the current state and the goal state and then to find operations that will reduce them. If there is more than one such operation, the one that reduces the largest difference is applied first. In other words, find the best means to achieve the desired end. General methods like this one which reduce the number of moves that must be considered but which are not guaranteed to succeed in all cases, are called heuristics.

Sub-goaling—A very useful strategy, which can be used in conjunction with means/end analysis, is sub-goaling. Sub-goaling is simply picking out an intermediate state on the solution path to reach as the temporary goal. In effect, sub-goaling divides a problem into two or more subproblems, thus transforming the entire search space into two or more spaces of smaller depth.

Generate-and-test—A heuristic that is useful in many cases, is to generate a set of possible solutions to a given problem directly, and then to test each one to see if it is the correct solution.

Consider the task of opening a small combination lock. Possible solutions are all short series of numbers (usually three) that fall between the smallest and largest numbers on the dial. These are easy to generate.

Fortunately (or unfortunately, depending on your situation), the total number of possible solutions is quite large, and the possibility of success in this case is quite small. Generate-and-test is useful only when it is reasonably easy to generate the set of potential solutions and test them; the set must be fairly small.

Symmetry—It is a powerful approach to solving problems in many different fields, including mathematics, physics, and chemistry. The basic idea behind using symmetry as an approach to problem-solving is that if a problem has a certain type of symmetry, then you can often simplify the problem by taking advantage of that symmetry.

One common example of using symmetry in problem-solving is in geometry. If a geometric figure has a high degree of symmetry, such as a regular polygon or a circle, then you can often use that symmetry to simplify calculations or prove geometric theorems. For example, if you want to find the area of a regular polygon, you can use the fact that the polygon can be divided into congruent triangles, and then use trigonometry to find the area of one of those triangles.

Symmetry can also be used in physics to simplify calculations or make predictions about physical systems. For example, in the study of electromagnetism, the laws of physics have a high degree of symmetry under rotations and translations. This symmetry can be used to derive equations that describe the behavior of electromagnetic waves and particles.

Symmetry is also a useful approach to solving problems in the organizational context. For example, in organizational design, symmetry can be used to create efficient and effective structures that align with the organization’s goals and values. By identifying the symmetries that exist within an organization, such as functional areas or hierarchical levels, designers can create organizational structures that are more streamlined and easier to manage.

Similarly, in project management, symmetry can be used to create a more balanced workload and minimize bottlenecks. By dividing tasks among team members in a symmetrical way, for example, by assigning similar tasks to each team member, you can reduce the likelihood of any one team member becoming overloaded or underutilized.

In marketing and branding, symmetry can be used to create visually appealing and memorable designs. Symmetry is a fundamental principle of design and is often used to create balance and harmony in logos, packaging, and other marketing materials. By using symmetrical elements, designers can create a sense of stability and reliability, which can help to build trust and credibility with customers.

Overall, symmetry can be a useful approach to problem-solving in the organizational context by helping to simplify complex systems, balance workloads, and create visually appealing designs. By understanding and leveraging the symmetries that exist within an organization, managers and designers can create more effective and efficient structures that support the organization’s goals and objectives.

How to use symmetry to solve a magic cube—a particular case of problem-solving applying symmetry is the approach to solving a Rubik’s cube or magic cube, by taking advantage of the symmetries that exist in the cube’s structure. One way to use symmetry is to identify pairs of cubies (individual cube pieces) that are mirror images of each other or related by rotational symmetry, and then use those symmetries to solve multiple cubies at once.

For example, one common approach to solving a Rubik’s cube is to start by solving one face of the cube and then using symmetrical moves to solve the opposite face. This approach takes advantage of the fact that the opposite faces of the cube are symmetrical and that solving one face will automatically solve the opposite face as well.

Another approach to using symmetry in solving a magic cube is to identify pairs of cubies that are symmetrically located in the cube, such as the two center cubies on opposite faces. By solving these symmetrically located cubies together, you can simplify the problem and reduce the number of moves required to solve the cube.

Finally, some advanced solving techniques involve identifying more complex symmetries in the cube, such as permutation symmetries or group symmetries, and using those symmetries to solve multiple cubies at once. These techniques require a deep understanding of group theory and abstract algebra and are typically used by expert cubers.

In summary, symmetry can be a powerful approach to solving a Rubik’s cube or magic cube, by taking advantage of the symmetries that exist in the cube’s structure. By identifying and exploiting these symmetries, you can simplify the problem and reduce the number of moves required to solve the cube.

In all of these examples, symmetry is used as a tool for simplifying complex problems by taking advantage of the underlying structure and organization of the problem. By identifying and exploiting symmetries in a problem, you can often reduce the amount of work required to find a solution or make a prediction.

Although research flourished well and provided valuable insights into the kinds of general problem-solving strategies people use, it became apparent that in more complicated kinds of problems, strategies alone did not sufficiently describe problem-solving performance.

Knowledge of the problem domain is also important and can influence the use of general problem-solving heuristics. This knowledge must be well structured so that relevant knowledge can be accessed at the proper time. Research is beginning to uncover just what well-structured means, but considerable work is left to be done on the problem of how we are able to retrieve information in a rapid and effective manner from the wealth of knowledge we all possess.

6.1 Problem Structuring

According to Schwenk and Thomas [29], problem structuring is the process by which a set of relevant aspects is sufficiently well presented as a problem or group of problems such that the risk of using analytical procedures to solve the wrong problem is minimized.

According to Phillips et al. [30], the structuring work aims at the construction of a more or less formalized model, capable of being accepted by the actors of the decision process as a scheme of representation and organization of the primary elements of evaluation, which can serve as a basis for learning, investigation and interactive discussion with and among the actors of the decision process.

By structure type, problems can be classified, basically, in three ways: Structured, semi-structured, and non-structured problems. They are three types commonly used in problem-solving and decision-making contexts that differ in terms of the degree of clarity of their goals, the nature of the available information, and the level of complexity involved. Here’s a brief differentiation among them:

• 1. Structured problems—Structured problems are well-defined and familiar and have a clear goal, a well-defined set of procedures, a set of rules to follow, a well-structured set of inputs and outputs, and a clear-cut solution. In other words, the solution process is pre-determined, and the required information is readily available. These problems are usually routine and easy to solve because they have a straightforward path to a solution. Examples of structured problems include mathematical equations, accounting balance sheets, and inventory management systems.

  They are those whose solutions can be achieved by following logical and very well-defined processes. Traditional information systems seek to solve these types of problems, which are routine and repetitive and therefore computer programmable. In this situation, the action is known, and the decision is subject to known results, that is, the consequences are known.

  These problems are classified as decisions under conditions of certainty. It is possible for the decision-maker to choose the alternative that has the best gain/loss ratio. Certainty is deterministic. To this type of problem is also associated the concept of the right universe because it is the hypothesis of perfect information; each line of action has a definite consequence (only one) and known. The appropriate method of solution is calculus [8].

• 2. Semi-structured problems—Semi-structured problems are those that have a clear goal but lack a well-defined set of procedures or a well-structured set of inputs and outputs. They are not fully defined and have an ambiguous or unclear solution. These problems have a partial goal, a set of rules that are only partially applicable, and are not fully defined. These problems require some level of creativity, analysis, or interpretation to solve. They are more complex than structured problems and require some level of judgment, interpretation, and decision-making skills. The required information is usually available but may need to be searched for or interpreted. Examples of semi-structured problems include designing a marketing campaign, developing a new product, or planning a research project, strategic planning, hiring decisions, or project management.

  Semi-structured problems use certain mathematical models in the structured parts of the problem being analyzed. Final decisions should be made on the basis of subjective criteria that are difficult to quantify. Structured parts of the problem can be solved with a computer program, and others can be solved by the judgment of the decision-maker. Artificial intelligence systems are being used to help solve the unstructured parts of the problem. In this type of problem, the probability of the states of nature is assumed as if they were known; the consequences are known and probabilistic.

  According to Yager [31], this type of decision is called a decision under risk conditions. Associated with this type of problem is the concept of the random universe. The consequences of decisions depend on a series of random successes, according to laws of probability [8].

• 3. Non-structured problems—Non-structured problems are ill-defined, unfamiliar, and have no clear solution. These problems do not have a clear goal or set of rules to follow, no well-defined set of procedures, and no well-structured set of inputs and outputs. These problems are complex and require extensive problem-solving skills, a high degree of creativity and innovation, analysis, and interpretation to solve. The required information may be incomplete, ambiguous, or contradictory and may need to be collected from various sources. Examples of non-structured problems include creating or developing a new business model, resolving a conflict between team members, designing a new product, or dealing with and responding to a global crisis.

  Unstructured problems are problems for which there are no logical, well-defined processes for resolution. Due to its non-quantifiable character, its resolution is the fruit of human intuition, being subject to unknown probabilities, or to subjective possibilities. They are unknown and/or complex problems for the organization and are also resistant to computational deployment. This is the decision made under “ignorance” or “conditions of uncertainty.”

The decision-maker, faced with an unstructured problem [32]:

• (a) May assume pessimistic or optimistic attitudes

• (b) Use decision-support algorithms, considering the subjectivity of the decision-maker’s values

• (c) Use utility theory to verify which alternative, in his view, adds the most value

In summary, structured problems have a clear solution and are straightforward and easy to solve; semi-structured problems have an unclear solution that requires some level of analysis, judgment, interpretation, and decision-making skills; and non-structured problems have no clear solution, are complex, and require extensive problem-solving skills and a high degree of creativity, analysis, and innovation.

6.2 How the Problem Structure Impacts Decision-Taking?

The structure of a problem can have a significant impact on the decision-making process by influencing the complexity, time, effort, flexibility, and creativity required to arrive at a decision.

Structured problems have a clear solution, and the decision-making process is relatively straightforward, involving the application of established rules or procedures. In contrast, non-structured problems have no clear solution, and the decision-making process requires creativity, exploration, and innovation. Semi-structured problems fall in between, requiring some level of judgment and interpretation in addition to established rules or procedures.

Structured problems are easier to solve and typically require less time and effort to arrive at a decision. However, the downside is that they may not allow for much flexibility or creativity in decision-making since the solution is predetermined. In contrast, non-structured problems require more time, effort, and creativity to arrive at a decision, but they allow for more flexibility and innovation.

The decision-making process for semi-structured problems is more complex, as it involves a combination of established rules or procedures and judgment, interpretation, and decision-making skills. It can be challenging to strike the right balance between established rules and flexibility in decision-making, making the decision-making process more nuanced.

In summary, solving a non-structured problem can be a challenging and complex process, but there are several steps you can take to help you arrive at a solution:

• 1. Define the problem—Begin by identifying the problem and defining it as clearly and specifically as possible. It’s important to break down the problem into smaller parts and understand the underlying causes and factors contributing to it.

• 2. Gather information—Collect relevant information and data to help you gain a deeper understanding of the problem. This may involve conducting research, analyzing existing data, and seeking input from experts or stakeholders.

• 3. Generate options—Brainstorm potential solutions or options to address the problem. It’s important to consider a wide range of options and approaches, even if they seem unconventional or outside the box.

• 4. Evaluate options—Once you have a list of potential solutions, evaluate each option based on its feasibility, potential impact, and other relevant criteria. This will help you narrow down the list of options and identify the most promising solutions.

• 5. Choose a solution—Based on your evaluation, select the solution that is most likely to be effective and feasible to implement. It’s important to consider any potential risks or drawbacks associated with the chosen solution.

• 6. Implement and monitor—Once you have chosen a solution, implement it and monitor its effectiveness. It may be necessary to make adjustments or revisions to the solution over time as you gather more information and data.

It’s important to note that solving a non-structured problem often requires a degree of flexibility and openness to new ideas and approaches. It may also involve working collaboratively with others and seeking input from a range of perspectives.

Examples of decisions that may involve non-structured problems for leaders are as follows:

• 1. Developing a new business strategy: when a company is facing challenges such as declining sales or increased competition, the leader may need to develop a new business strategy to address the issue. This may involve analyzing market trends and consumer behavior, exploring new product or service offerings, and identifying new revenue streams.

• 2. Responding to a crisis: when a crisis such as a natural disaster, cybersecurity breach, or public relations issue occurs, the leader may need to make decisions in real-time without a clear solution. This may involve assessing the situation, determining priorities, and taking action to minimize the impact of the crisis.

• 3. Designing a new product or service: when a company wants to introduce a new product or service, the leader may need to work with a team to develop a solution that meets the needs of customers while also being financially feasible to produce. This may involve conducting market research, testing prototypes, and identifying the optimal pricing and distribution strategies.

• 4. Addressing organizational culture issues: when there are cultural issues within an organization, such as a lack of diversity, poor communication, or low morale, the leader may need to make decisions to address these issues. This may involve implementing new policies, training programs, or other initiatives to improve the culture and create a more inclusive and supportive work environment.

In each of these scenarios, the leader is faced with a non-structured problem that requires creativity, flexibility, and the ability to make decisions based on incomplete information. Effective leaders in these situations will often draw on a combination of analytical skills, emotional intelligence, and strategic thinking to arrive at a solution.

7 Conditions for Decision-Making

For Thierauf and Klekamp [33], a decision may be taken under the following conditions:

• Decision in conditions of certainty—Occurs when the decision is made with full knowledge of all states of nature. There is certainty of what will occur during the period in which the decision is made. It is possible to assign 100% probability to a specific state of nature. Probability indicates the degree of certainty in which 0% will be complete uncertainty and 100% or one indicates complete certainty.

• Decision under risk conditions—Occurs when the probabilities associated with each of the states of nature are known. The total number of states of nature is known. Unlike the previous item, which had 100% certainty in the result, here this certainty will vary from 0 to 100%.

• Decision under conditions of uncertainty or decision under conditions of ignorance—Occurs when the total of states of nature has not been obtained, or even the portion of the known states of nature has data obtained with uncertain probability, or the probability associated with the events is unknown.

• Decision in conditions of competition or conflict—Occurs when strategies and states of nature are determined by the action of competitors with conflicting interests. There are necessarily two or more decision-makers involved; the outcome depends on the choice of each of the decision-makers.

Certainty in decision-making refers to the degree of confidence or assurance that a decision-maker has about the outcome of a particular decision. It is the level of belief or conviction that a decision-maker has about the accuracy, correctness, and effectiveness of a decision.

In other words, certainty is the level of confidence a decision-maker has that a particular course of action will lead to the desired outcome. When a decision-maker is certain about a decision, they have a high level of confidence that the decision will lead to the expected results.

However, it is important to note that certainty is not always achievable in decision-making, especially in complex and uncertain situations. Many decisions are taken by nondeterministic conditions and involve some level of risk, ambiguity, or uncertainty, which can affect the level of certainty that a decision-maker has about the outcome. In such cases, decision-makers may need to weigh the risks and benefits of different options and make the best decision based on the available information and their own judgment.

Nondeterministic conditions refer to situations where the outcome of a decision cannot be predicted with certainty, even with complete information about the situation. These conditions arise in situations that are complex, uncertain, and dynamic, where there are many variables at play and the outcomes are influenced by multiple factors.

In such situations, decision-making becomes more challenging, as it is difficult to determine the best course of action with certainty. The decision-maker may need to consider multiple scenarios and outcomes and weigh the risks and benefits of each option based on incomplete or imperfect information.

In many cases, decision-makers may need to rely on their judgment, experience, and intuition to make decisions in nondeterministic conditions. They may also need to collaborate with others and seek input from multiple sources to gather as much information as possible before making a decision.

It is important to note that even in nondeterministic conditions, decision-making is still possible, and decisions can still be made effectively. However, decision-makers need to be aware of the limitations of their knowledge and information and be prepared to adjust their decisions based on new information or changing circumstances.

Overall, decision-making in nondeterministic conditions requires a flexible and adaptable approach, where decision-makers are willing to consider multiple options, take calculated risks, and adjust their decisions based on feedback and new information.

The nondeterministic can originate from six basic sources:

• (a) Vagueness caused by the difficulty of assessing actions under the influence of a particular criterion (or more).

• (b) Indeterminacy of the methods of evaluation of the results, since these can be based on an “arbitrary definition” (according to Hapke and Slowinski [34]).

• (c) Doubt regarding the values of the data obtained.

• (d) Doubt about whether the values involved or obtained will vary over time and/or space.

• (e) Ambiguous classification and/or opinions.

• (f) Probabilistic events.

The nondeterministic can be defined as the junction of the terms: imprecise (also called vague or inaccurate), ambiguous (also called dubio), and uncertain (or probabilistic).

The concept of imprecise arises from the impossibility of accurately performing a measurement or graduation of an object and/or situation; it may also be associated with the inconsistency of natural phenomena.

The concept of ambiguous is characterized by the difficulty of obtaining an accurate classification of the element under study, although this is perfectly known to experts. It comes from the existence of two classifications for the same object and/or the existence of two different alternatives that have the same classification.

The concept of uncertain is related to the fact that some events are probabilistic and that the probabilities of these events are not known.

The uncertain and “treated” by means of the identification of:

• (a) Non-existence of the deterministic.

• (b) Comparison by nebulous relationships.

• (c) Use of information from subjectivity.

• (d) Need for the use of probabilistic models.

According to Marshall and Oliver [35], the decision under uncertainty requires that one or more decisions be made before, and one or more “uncertainties” are observed and/or structured. Associated with this type of problem is the concept of the indeterminate universe, in which it is not possible to obtain all the necessary information, since experimentation cannot be resorted to. The decision-maker may use “a priori probabilities” and/or possibilities. The decision-maker executes a choice (inspiration) based on their experience [8].

The probabilistic model is possible when there is a degree of regularity in an observed phenomenon and a mathematical model can be applied to the qualitative variability of that phenomenon.

Therefore, certainty is an important consideration in decision-making, but it is not always achievable or necessary. It is important to balance the need for certainty with the need for flexibility and adaptability in a dynamic and changing environment.

7.1 Analytic Tools Based on the Level of Uncertainty

Are you using traditional analytic tools—market research, value chain analysis, assessments of rivals—to inform your strategy?

Courtney et al. [36] answer that those tools work in stable business environments. But today we’re operating amid unprecedented uncertainty. Apply the old tools, and you risk formulating strategies that neither defend your company against threats nor leverage the opportunities uncertainty can provide.

According to the authors, instead, use analytic tools based on the level of uncertainty facing your company: are you facing only two or three alternative futures? Then use tools such as decision analysis. A wide range of possible scenarios? Consider scenario planning. Armed with the right kind of information, select an appropriate strategy—and execute it through savvy moves.

Courtney et al. [36] suggest three ideas in practice to confront uncertainty:

• 1. Apply appropriate analytic tools to identify strategic options.

  If only a few future scenarios are perceived, it is recommended to opt for valuation models and game theory to establish relative probabilities of each outcome and gauge alternative strategies’ risks and returns.

  On the contrary, if there is a wide range of futures, the idea is to apply Technology Forecasting and to develop 4–5 possible scenarios.

• 2. Select a strategic posture. Strategic postures clarify your strategic intent. They can take three forms:

  • (a) Shaping—driving your industry toward a new structure of your devising and creating new opportunities.

  • (b) Adapting—choosing where and how to compete within the current industry structure. Many telecommunications service resellers pursue competitive advantage through pricing and effective execution rather than product innovation.

  • (c) Reserving the right to play—making incremental investments to stay in the game without committing to a new strategy prematurely. Some pharmaceutical companies reserve the right to play in “genetherapy” applications by buying small biotech firms with relevant expertise.

• 3. Build a portfolio of strategic moves. Use one or more of these moves depending on your strategic posture.

  • (a) Big bets—major commitments (capital investments, mergers, or acquisitions) that will generate large payoffs in some scenarios and large losses in others.

  • (b) Options—modest initial investments (pilot trials, limited joint ventures, technology licensing) that enable you to ramp up or scale back your investment later as the market evolves.

  • (c) No-regrets moves—actions that pay off no matter what happens, such as cost-cutting initiatives and competitor intelligence.

Developing the article, Courtney et al. [36] describe four levels of uncertainty.

According to the authors, even the most uncertain business environments contain a lot of strategically relevant information. First, it is often possible to identify clear trends, such as market demographics, that can help define potential demand for future products or services. Second, there is usually a host of factors that are currently unknown but that are in fact knowable—that could be known if the right analysis were done.

Performance attributes for current technologies, elasticities of demand for certain stable categories of products, and competitors’ capacity-expansion plans are variables that are often unknown, but not entirely unknowable. The uncertainty that remains after the best possible analysis has been done is what the authors call residual uncertainty—for example, the outcome of an ongoing regulatory debate or the performance attributes of a technology still in development. But often, quite a bit can be known about even those residual uncertainties. In practice, they have found that the residual uncertainty facing most strategic decision-makers falls into one of four broad levels:

• Level 1: A Clear-Enough Future. At level 1, managers can develop a single forecast of the future that is precise enough for strategy development. Although it will be inexact to the degree that all business environments are inherently uncertain, the forecast will be sufficiently narrow to point to a single strategic direction. In other words, at level 1, the residual uncertainty is irrelevant to making strategic decisions.

• Level 2: Alternate Futures. At level 2, the future can be described as one of a few alternate outcomes, or discrete scenarios. Analysis cannot identify which outcome will occur, although it may help establish probabilities. Most important, some, if not all, elements of the strategy would change if the outcome were predictable. Many businesses facing major regulatory or legislative changes confront level 2 uncertainty. In a common level 2 situation, the value of a strategy depends mainly on competitors’ strategies, and those cannot yet be observed or predicted. For example, in oligopoly markets, such as those for pulp and paper, chemicals, and basic raw materials, the primary uncertainty is often competitors’ plans for expanding capacity.

• Level 3: A Range of Futures. At level 3, a range of potential futures can be identified. That range is defined by a limited number of key variables, but the actual outcome may lie anywhere along a continuum bounded by that range. There are no natural discrete scenarios. As in level 2, some, and possibly all, elements of the strategy would change if the outcome were predictable. Companies in emerging industries or entering new geographic markets often face level 3 uncertainty.

• Level 4: True Ambiguity. At level 4, multiple dimensions of uncertainty interact to create an environment that is virtually impossible to predict. Unlike in level 3 situations, the range of potential outcomes cannot be identified, let alone scenarios within that range. It might not even be possible to identify, much less predict, all the relevant variables that will define the future. Level 4 situations are quite rare, and they tend to migrate toward one of the other levels over time. Nevertheless, they do exist. Consider a telecommunications company deciding where and how to compete in the emerging consumer-multimedia market. It is confronting multiple uncertainties concerning technology, demand, and relationships between hardware and content providers, all of which may interact in ways so unpredictable that no plausible range of scenarios can be identified.

8 Approaches in Decision-Making

Most of these decisions impact the interests of people and organizations that will be called upon to decide whether to accept, support, and abide by or not. But how to make such complex decisions? Professional experience, intuition, and careful analysis are the basis for good decision-making. But they are not enough to guarantee their suitability and quality. It will be necessary to choose some method of decision-making, especially when decisions involve new, unknown, and complex situations.

Certainly, some methods are more effective than others, depending on the urgency or nature of the decision to be made, but they are not mutually exclusive, and often several techniques are combined. Davenport and Harris [37] address these methods.

Procedure-Based Decision-Making (checklist)

It may be the best choice when applied to problems that appear continuously, repeatedly, and systematically. Over time, standardized responses can be defined based on a set of explicit instructions (checklist).

The best example of applying this method corresponds to the decisions made by an airplane pilot in his cockpit, through a listing, when facing certain types of problems and reacting in a predictable way, even under the most exceptional conditions.

Your biggest challenge is to recognize the type of problem you’re dealing with and your biggest limitation is dealing with new or complex problems.

8.1 Experience-Based Decision-Making (Intuition)

When the problem faced is different or complex, it will be easier to rely on one’s own experience to recognize similarities between the problem faced with others previously experienced. This approach is most likely to succeed when the decision-makers have a wide range of relevant experiences and there is not much time to decide.

In general, there is a strong correlation between experience and skill. A pilot’s ability, for example, is measured in flight hours. The reliability deposited in a surgeon in a surgical process is directly related to the number of similar procedures already performed.

Experience will not always be the best advisor when making a decision, because:

• Perhaps past experiences are not applicable to the current situation. What at first seems like a habitual problem may be quite different in the sequence.

• Experience can be a source of self-esteem and authority. In order not to cede this authority, the decision-maker may not recognize that a decision is completely new in a given situation.

• The business world is changing rapidly, and past experience doesn’t always have anything to do with future situations. It is hard to accept that certain experiences lose their validity due to today’s constant changes.

• Finally, memories of past experiences may not be accurate. Many facts that are remembered more clearly did not happen in the way they are recalled, causing the conclusions drawn based on those experiences to be mistaken.

The great advantage of this method is the reduced time to decide. However, as explained above, its limitations are related to the validity period of the experience, and errors in the evaluation of the experience and also by the difficulty of explaining the procedures assumed by the decision-maker.

8.2 Analytics-Based Decision-Making

How is it done to make decisions about unknown and complex problems without a defined pattern to follow? Especially when dealing with huge volume of data (big data).

In this case, it is necessary to make an analysis of the problem and the options for action, going through the following steps:

• 1. Definition of the problem.

• 2. Determination of the causes of the problem.

• 3. Selection of the most important aspects of the problem.

• 4. Identify solution options and evaluate them.

• 5. Identify the consequences and risks of each option and the available resources.

• 6. Assessment of the risks and uncertainties that these consequences entail.

• 7. Identification of the most appropriate alternatives within available resources.

Predictive analytics-based decision-making is used when you already have enough information to generate alternatives for action and requires criteria to define what is true or false or what is relevant or not. It does not correspond to a quantitative analysis, although the numbers are very convenient, but they are not the most appropriate tools [37]. ◘ Graphic 7.1 presents a predictive analysis sequence for decision-making.

Graphic 7.1

Decision-making based on predictive analytics

Source: Adapted by the author of Davenport and Harris, op cit., p. 7, 2007

The disadvantages to the application of this method are the required time of analysis, the lack of availability of key information, the minimization of the importance of experience, and the reality of the facts in decision-making. Finally, the method makes an analysis but is unable to propose actions.

8.2.1 Information Systems

Peter Drucker was a management consultant, educator, and author who is often referred to as the “father of modern management.” He had a great deal to say about information systems and their role in business. In his article, The Rise of the Knowledge Society [38], Drucker discusses the role of information and communication technologies (ICTs) in shaping the modern world, and how they are transforming the nature of work, education, and society as a whole. He also explores the challenges and opportunities presented by the information age, including issues such as privacy, security, and the digital divide.

In one of his books, Management Challenges for the twenty-first Century [39], Drucker examines the increasing importance of knowledge work and the role of information technology in enabling and supporting it.

Here are some key insights from Drucker’s teachings on information systems:

• 1. Information systems must serve business objectives. According to Drucker, information systems are only effective when they serve the objectives of the business. Businesses should not implement information systems just because they are available but should consider whether they help achieve their goals.

• 2. The purpose of information systems is to provide information. Information systems should be designed to provide relevant and accurate information to managers and other decision-makers. They should help managers make better decisions by providing timely, relevant, and reliable information.

• 3. Information systems should be integrated into the business. Information systems should be integrated into the overall business strategy and operations. They should not be considered a separate, standalone component of the business.

• 4. Information systems should be simple and flexible. Drucker believed that information systems should be simple and flexible to be effective. They should be designed to be easy to use, with minimal training required. They should also be adaptable to changing business needs and processes.

• 5. Information systems should be used to improve communication. Drucker saw information systems as a way to improve communication within a business. They should be used to facilitate communication between different departments and levels of management and to improve coordination and collaboration.

Overall, Drucker emphasized the importance of information systems in achieving business objectives and improving decision-making. He saw information systems as a tool to be integrated into the overall business strategy and operations, and designed to be simple, flexible, and effective in improving communication and providing relevant information to decision-makers.

For Drucker, informational systems should reduce uncertainty and increase information about the environment that surrounds them, particularly in the inputs they receive. Such inputs, whether of a financial, material, or demand nature, must be known, calculated, and anticipated.

In informational systems, as in open organizations in general, the decision-making process originates in the identification of problems or opportunities, in the collection and analysis of data and information about these problems/opportunities, and in the conversion of this information into action.

Its objectives include avoiding surprises, identifying threats and opportunities, obtaining and maintaining competitive advantage, reducing reaction time, minimizing resources, monitoring changes in values, habits, needs, and especially technology, and continuously improving and reviewing long- and short-term planning.

8.2.2 Informational Systems Validation Steps

There should be a cross between what managers think they need, what they really need, and what is economically viable. In this sense, it is necessary to pay attention to some validation steps:

Defining the problem means delimiting the object under study by evaluating:

• (a) What is the real need for information.

• (b) What decisions will be made with the information.

• (c) What is the importance of the planned action.

Properly defining problem means considering:

• (a) The background.

• (b) The “iceberg” principle.

• (c) The search for causes, not the symptoms.

Properly defining problem means defining its components:

• (a) The unit of analysis.

• (b) The dependent and independent variables.

• (c) The timing for the definition of established hypotheses.

Raise needs, access information:

• (a) Decisions usually made.

• (b) Special studies required.

• (c) Identify information not received.

• (d) Evaluate the required frequency of information (“online,” daily, weekly, monthly, annual, random).

• (e) Define sources (news, blogs, articles, etc.).

• (f) Define themes (topics).

8.2.3 Methods of Searching for Information

• (a) Brainstorm or brainstorming—It is the technique used to help a group imagine/create as many ideas as possible around a subject or problem, in a creative way.

• (b) Priority matrix—It is a technique that prioritizes alternatives based on certain criteria and should be used when one wants to establish one among several alternatives, through more careful analysis.

• (c) Decision trees or tree diagram—Its role is to discipline sequential decisions, in the sense that the alternatives that are offered to choose at a given moment are due to a previous decision or will have a decisive influence on the determination of a set of future alternatives.

• (d) Cognitive maps—Technique that allows portraying ideas, feelings, values and attitudes and their interrelationships, so that it makes possible a study and a later analysis, using for such a graphic representation.

• (e) Delphi method—Can be considered as a structured method of indirect interaction and anonymity between specialists, through questions, statistical disposition of data, and control of feedback of information generated in the group.

• (f) ZOPP—German term “Zielorientierte Projektplanung,” translates in English to “Objectives-Oriented Project Planning.” ZOPP is a project planning and management method that encourages participatory planning and analysis throughout the project cycle with a series of stakeholder workshops. The general objective of the ZOPP method is to arrive at a detailed planning through several stages of work, from relatively vague information, determining the instruments of implementation and evaluation of the project.

• (g) Serendipity—Even considering the techniques of seeking information for decision-making, one cannot rule out the role of serendipity as a means of helping us make decisions. Serendipity is a term coined by the psychologist and creativity expert Mihaly Csikszentmihalyi [40]. It refers to the unexpected and fortuitous discoveries that occur when we are pursuing one goal or objective, but unexpectedly stumble upon something else that is equally or even more valuable.

  In other words, serendipity occurs when we are actively seeking one thing, but discover something else that is unexpected and beneficial. It is often described as a happy accident or a stroke of luck.

  One example of serendipity in action is the discovery of penicillin. Alexander Fleming was studying bacteria in a Petri dish when he accidentally left the dish uncovered, allowing mold to grow on it. He noticed that the mold seemed to inhibit the growth of bacteria around it, leading to the discovery of penicillin and the development of the first antibiotic.

  Another example of serendipity is the invention of the microwave oven. Percy Spencer, an engineer at the Raytheon Corporation, was working on a radar system when he noticed that a candy bar in his pocket had melted. He realized that the microwaves from the radar system were responsible, leading to the development of the first microwave oven.

  In terms of decision-making, serendipity can be a valuable tool for generating new and unexpected ideas and solutions. By remaining open and curious and by exploring new areas and ideas outside of our usual comfort zone, we can increase the likelihood of serendipitous discoveries.

  However, it’s important to note that serendipity is not purely random—it often occurs as a result of deliberate effort and focused attention. As Csikszentmihalyi notes, “Serendipity favors the prepared mind.” By actively seeking out new ideas and experiences and by cultivating a mindset of openness and curiosity, we can increase the chances of stumbling upon unexpected and valuable discoveries.

• (h) The Black Swan—Term coined by Nassim Nicholas Taleb [41], the expression Black Swan Events refers to an unexpected event or occurrence, which is extremely difficult to predict. Taleb argued that these disparate events are almost impossible to predict, but that they carry catastrophic consequences.

  Unlike White Swan Events, which are certain, Black Swan Events are very rare, difficult to predict (not probabilistic) and have outdated consequences. They are called black swans in reference to the fact that such swans were presumably non-existent until Dutch explorers discovered them in Western Australia in the late seventeenth century.

  According to Schwab and Malleret [42], it will always be difficult, if not impossible, to predict what might happen at the end of the chain when multi-order effects and their cascades of consequences have occurred after rising unemployment, business failures, or the collapse of some countries. None of this is unpredictable in itself, but it’s your propensity to create perfect storms when they get confused with other risks that will take us by surprise.

• (i) Weak Signals—The decision-making literature identifies many of the human weaknesses that impair our sense-making skills. According to Russo and Schoemaker [43], there are various individual biases that may cause managers to be taken unaware. In addition, there are organizational biases—such as groupthink or polarization—that may keep much of the periphery-dwelling enemy in the shadows, even in organizations with an active scanning process.

  In addition to our personal biases, according to Janis [44], in his original and classic work on groupthink, we also work in organizations and we can end up suffering from groupthink. In principle, groups should be better than individuals at detecting changes and responding to them. But often a group can fall victim to narrow-minded analysis, tunnel vision, a false sense of consensus, and poor information gathering, resulting in groupthink. The true relevance of various snippets of information often can be fully appreciated only when they are debated with others and merged into a larger mosaic.

  Organizational sense-making occurs in a complex social environment in which people are not just sensitive to what is being said, but also to who is speaking. We judge both the signal and the source when we assess the meaning of information. Source credibility is influenced by many factors, including status, past experience, politics, and the like. Since most managers receive information from multiple sources, they need to be aware of such biases. These social biases will be especially strong when the information is weak or incomplete.

  The individual and organizational biases discussed above underscore why it is important to bring together different perspectives on the same issue. But how these different perspectives are cultivated and connected will greatly affect the ability of the organization to make sense of the weak information it receives, according to Schoemaker and Day [45].

8.2.4 Human Inference and Judgment Traps

Although complete objectivity is elusive, managers need to be aware of well-established traps that underlie human inference and judgment. The major ones are described below in terms of how information is filtered, interpreted, and often bolstered by seeking additional information aimed at confirming prior leanings. These traps or biases described by Schoemaker and Day [45] reflect multiple classic references research such as Festinger [46], Janis [44], and Kelley and Michela [47].

• Filtering—What we actually pay attention to is very much determined by what we expect to see. Psychologists call this selective perception. If something doesn’t fit our mental model, we often distort reality to make it fit rather than challenge our fundamental assumptions. A related phenomenon is suppression or the refusal to acknowledge an unpleasant reality because it is too discordant.

• Distorted Inference—Whatever information passes through our cognitive and emotional filters may be subject to further distortion. One well-known bias is rationalization: interpreting evidence in a way that sustains a desired belief. We fall victim to this when trying to shift blame for a mistake we made to someone else or to external circumstances. Wishful thinking leads us to see the world only in a pleasing way, denying subtle evidence that a child is abusing drugs, or a spouse is being unfaithful. Another common interpretation bias is egocentrism, according to which we overemphasize our own role in the events we seek to explain. This self-serving tendency is related to the fundamental attribution bias, which causes us to ascribe more importance to our own actions than to those of others or the environment. We often view our organization as a more central actor than it really is.

• Bolstering—Not only do we heavily filter the limited information that we pay attention to, but also, we may seek to bolster our case by searching for additional evidence that confirms our view. We might disproportionately talk to people who already agree with us. Or we may actively look for new evidence that confirms our perspective, rather than pursuing a more balanced search strategy.

  Over time, our opinions may become frozen, and our attitudes hardened as we immunize ourselves from contradictory evidence. Indeed, we may even engage in selective memory and forget those inconvenient facts that don’t fit the overall picture. The hindsight bias similarly distorts our memories such that our original doubts are erased. A vicious circle is created in which we exacerbate the earlier biases and get trapped in a self-sealing echo chamber.

Schoemaker and Day [45] offer several ideas for organizations to identify and interpret weak signals in their environment:

• Recognize the importance of weak signals—Weak signals may be the first indication of emerging trends or threats in the environment. Organizations that are able to detect and interpret these signals can gain a competitive advantage.

• Develop a scanning system—Organizations should establish a formal process for scanning the environment to identify weak signals. This process should involve collecting data from a wide range of sources and analyzing it to identify patterns and trends.

• Look for patterns—Rather than focusing on individual weak signals, organizations should look for patterns and connections between signals. This can help to identify emerging trends and potential threats.

• Encourage diverse perspectives—Organizations should seek input from a diverse range of stakeholders when interpreting weak signals. This can help to avoid groupthink and increase the likelihood of identifying important signals.

• Experiment and learn—Once weak signals have been identified and interpreted, organizations should experiment with different strategies to respond to them. This can help to build organizational learning and improve the ability to respond to future weak signals.

Overall, Schoemaker and Day argue that organizations that are able to identify and interpret weak signals can gain a competitive advantage by anticipating and responding to emerging trends and threats in their environment.

8.3 Rational Model for Making Decisions

Decision is a choice whenever alternatives or options lie ahead. Most decisions at a crossroads require considering several interrelated factors simultaneously forcing a choice of what is critical and involving enormous uncertainty and risk about the consequences of their outcomes. Most decisions impact the interests of people and organizations that will also have to decide whether to accept, support, or abide by them.

But how to make such complex decisions as those involving organizational strategy? Professional experience, intuition, and careful analysis are the basis for good decision-making. But they are not enough to guarantee their suitability and quality. Decision-makers, especially when decisions involve new, unknown, and complex situations, will have to seek more rational methodologies depending on the urgency or nature of the decision to be made.

Managers who weigh their options and calculate optimal levels of risk are using the rational model to make decisions. It is the four-step process that helps administrators weigh alternatives and choose the alternative that is most likely to succeed.

None of the decision-making approaches can guarantee that a manager will always make the right decision. However, managers who resort to a rational, intelligent, and systematic approach are more likely to find high-quality solutions than other managers.

The following explanation of the basic process of rational decisions is based on Stoner et al. [48] and Daft [49].

The basic process of rational decisions involves four stages:

Stage 1 Investigate the situation—A good investigation covers three aspects: the definition of the problem, the diagnosis, and the identification of objectives:

Definition of the problem. The confusion in defining a problem arises, in part, because the facts or aspects that capture the administrator’s attention could be a symptom of another, more fundamental or widespread difficulty.

Diagnosis of the causes. This underscores the importance of correctly diagnosing the causes of the problem. Managers can ask a number of diagnostic questions. Each involves, in some way, human relationships: What changes taking place, inside or outside the organization, may have contributed to the problem? Which people have more involvement in the problem situation? Do they have knowledge or perspectives that could clarify the problem? Do their actions contribute to the problem? The causes, unlike the symptoms, are almost never apparent and managers sometimes have to resort to intuition to identify them. Different people, whose view of the situation is inevitably influenced by their experience and responsibility, may perceive different causes for the same problem. It is up to the manager to put all the pieces together and find a picture as clear as possible.

Identification of the objectives of the decision. When the problem has been identified and its causes diagnosed, the next step is to decide what an effective solution would be. Most problems consist of several elements, and the manager is unlikely to find a solution that works for all of them. If a solution allows managers to achieve organizational goals, it will succeed. However, more ambitious objectives could come to mind. The immediate problem could be indicating future difficulties that the manager could avoid if he took action early on. In addition, the problem may offer an opportunity to improve organizational performance, rather than just restore it.

Stage 2 Develop alternatives—This stage may be reasonably simple for most scheduled decisions, but not so simple for complex, unscheduled decisions, especially if there are time constraints. Too often the temptation to accept the first viable alternative prevents managers from finding the best solution to their problems. To avoid this, no important decision should be made until several alternatives have been found.

Stage 3 Evaluate alternatives and choose the best available one—When managers have a series of alternatives, they will have to evaluate each of them based on three key questions.

• 1. Is this alternative viable? Does the organization have the money and resources to carry out the alternative? Changing all the old equipment may be the ideal solution, but it is not feasible if the company is about to go bankrupt. Does the alternative satisfy all the legal and ethical obligations of the organization? For example, closing a factory to cut costs entails a complex tangle of legal and ethical obligations for laid-off workers. Is the alternative reasonable, given the organization’s internal strategy and policies? Any solution will only be as effective as the support it gets within the organization. Therefore, to evaluate an alternative, managers should try to anticipate what would happen if employees did not give their support and implement it fully.

• 2. Does the alternative represent a satisfactory solution? To answer, managers have to think of two other questions. First, does the alternative meet the objectives of the decision? Second, does the alternative have an acceptable chance of succeeding? (It is assumed that probability can be calculated; of course, under conditions of uncertainty this would be very difficult or impossible.) Managers should also be aware that the definition of “acceptable” can vary from organization to organization and person to person, depending on the culture of the organization and how much risk the parties involved in the decision tolerate.

• 3. What are the potential consequences for the rest of the organization? As an organization is a system of interrelated parties and operates among other systems, managers should try to anticipate how change in one area will affect other areas, both in the present and in the future. Competitors may also be affected by the decision and their reactions have to be taken into account. Are competitors likely to respond to a go-to-market strategy or a new product?

Step 4 Implement the decision and monitor it—When the best of the existing alternatives has been chosen, managers can make plans to address the requirements and problems that might be encountered in putting it into practice. The implementation of the decision is not only limited to issuing the appropriate orders. Resources must be secured and allocated as needed. Managers set budgets and timelines for the actions they have decided to implement, which allow them to measure progress in concrete terms. They then assign responsibility for the specific tasks involved. They also establish a procedure for progress reports and prepare to apply corrections should other problems arise.

Humans tend to forget the risks and possible uncertainties after they have made a decision. Managers can counter this failure by consciously taking extra time to re-analyze their decisions at this point, as well as to develop detailed plans to deal with such risks and uncertainties.

A common mistake of managers is to assume that when they have made a decision, action will be taken automatically. Even if a decision is a good one, if others are unwilling or unable to implement it, then the decision will be of no use. Actions taken to implement the decision should be subject to monitoring. Are things going according to plan? What is happening in the external and internal environment as a result of the decision? Do the results people are having fit expectations? How is the competition responding?

For managers, decision-making is an ongoing process, as well as the ongoing challenge of dealing with other human beings, over time.

However, it is known that managers must make decisions within narrow time limits and with less information than they would like to have. Over the years, three concepts have emerged that help managers put decision-making in their perspective: limited rationality, conformism, and heuristics.

Limited rationality and conformism—In trying to describe the factors that affect decision-making, Herbert Simon [50], among others, has proposed a theory of bounded rationality. This theory points out that decision-makers must be faced with inadequate information regarding the nature of a problem and its possible solutions, lack of time and money to gather more complete information, inability to recall large amounts of information, and limits to their own intelligence. Instead of looking for the ideal or perfect decision, managers often settle for the one that will serve their purposes properly. In Simon’s terms, they are satisfied, that is, they accept the first satisfactory decision they find, instead of extending to the maximum or searching until they find the best possible decision.

Heuristics—Research by Tversy and Kahneman [51] has expanded Simon’s concepts of bounded rationality. They have shown that people rely on heuristic principles, or rules of thumb, to simplify decision-making. For example, loan executives may select mortgage applications on the assumption that people can only afford to spend only 1% of their income on housing.

Tversy and Kahneman [52] explain that when humans make decisions, three heuristic elements are presented as general cognitive guides used intuitively.

Availability—Sometimes people judge the probability of an event by comparing it to their memories. In principle, it is easier to remember facts that occur frequently. Therefore, events that are more “available” in memory will presumably be more likely to occur in the future.

Representativeness—People also tend to determine the probability of an event by trying to equate it with an existing category. For example, product managers might forecast the performance of a new product by relating it to other products that have proven track records.

Anchors and adjustments—People don’t take their decisions out of thin air. As a rule, they start from an initial value or “anchor” and then adjust that value to reach a final decision.

8.4 Multi-Criteria Decision-Making

Sometimes it’s easy to make a good decision; there are problems and decisions whose best option is so evident and which one need not even think about. However, most decisions in a business require simultaneously considering several interrelated factors, forcing a choice of what is critical and involving enormous uncertainty and risks about the consequences of the results of the decision.

Some choices, when made, follow a single parameter; choosing to buy, for example, a car under the single cost parameter, to check which car is the least expensive, by means of a monetary measurement, and it will be bought; this is not a decision, it is a simple measurement. Therefore, to decide is to choose an alternative from a set of possible alternatives under the influence of at least two conflicting parameters.

Some decisions will be made through parameters not quantitatively measurable, but measured qualitatively, as is the case of the beauty parameter. However, this measurement can be done on a verbal or ordinal scale. Subsequently, it can be transformed into a numerical scale.

The following keys were used to define the theoretical procedure or approach in order to provide a brief review of the main MCDM (multiple criteria decision-making) approaches:

Multiple criteria decision-making (MCDM) is a decision-making process that involves considering multiple criteria or objectives that are often conflicting and arriving at a decision that best balances all these criteria. It is a widely used approach in many fields, such as business, engineering, and public policy, where decisions are complex and involve multiple stakeholders with different priorities.

MCDM models are mathematical or analytical tools that help in making such complex decisions by incorporating multiple criteria into the decision-making process. Here are some of the main MCDM models:

Analytic hierarchy process (AHP): AHP is a decision-making framework that uses pairwise comparisons of criteria to derive a priority ranking of the criteria. AHP is often used in business and engineering applications where decisions involve a hierarchy of criteria. For example, AHP can be used in selecting a supplier for a company where price, quality, delivery time, and location are the criteria. Main references: Wind and Saaty [53]; Forman and Gass [54].

Multi-objective programming (MOP) addresses the issue of optimizing several objectives subject to a set of constraints. Given any level of conflict among the objectives, which is a commonplace occurrence, not all of them can be simultaneously optimized. Instead of searching for a non-existent optimum, the MOP approach seeks to find a set of efficient solutions (i.e., the pareto-optimal set). Efficient solutions are those in which no other feasible solution can improve one objective without degrading at least one other. Main reference: Steuer [55].

Goal programming (GP) is an MCDM model that is used to solve decision problems where the decision-maker has multiple goals or objectives that need to be satisfied. The solution to the GP model provides the decision-maker with a set of decision variables that satisfy the set of goals or objectives as closely as possible. The GP model can also be used to evaluate trade-offs between different goals or objectives and to identify the best possible solution that balances these trade-offs. Main references: Tamiz et al. [56]; Romero [57].

In GP, the objectives are typically expressed in terms of both the desired level of achievement and the acceptable range of deviation. The acceptable range of deviation reflects the decision-maker’s tolerance for not achieving the desired level of achievement in the objective. The GP model then minimizes the deviations or discrepancies between the actual solution and the desired levels of achievement for each goal.

GP has a wide range of applications, such as in production planning, resource allocation, project management, and portfolio optimization. For example, in production planning, GP can be used to find the optimal production plan that satisfies multiple goals, such as minimizing production costs, maximizing production capacity, and meeting customer demand. In project management, GP can be used to identify the optimal project schedule that satisfies multiple goals, such as minimizing project duration, maximizing resource utilization, and meeting project deadlines.

Compromise programming (CP) is used to solve decision problems where the decision-maker has multiple conflicting goals or objectives that need to be satisfied. CP is a mathematical programming model that allows the decision-maker to find a solution that best compromises between the conflicting goals. Main reference: Zeleny [58].

In CP, the objectives are typically expressed in terms of both the desired level of achievement and the degree of importance of each objective. The degree of importance reflects the relative priority that the decision-maker assigns to each objective. The CP model then minimizes the weighted sum of the deviations or discrepancies between the actual solution and the desired levels of achievement for each goal.

The solution to the CP model provides the decision-maker with a compromise solution that balances the trade-offs between the different objectives. The CP model can also be used to evaluate the sensitivity of the compromise solution to changes in the relative priorities assigned to each objective.

CP has a wide range of applications, such as in environmental planning, resource allocation, and portfolio optimization. For example, in environmental planning, CP can be used to find a compromise solution that balances the trade-offs between different environmental objectives, such as reducing air pollution, conserving water resources, and preserving natural habitats. In resource allocation, CP can be used to identify the optimal allocation of resources that satisfies multiple objectives, such as minimizing costs, maximizing efficiency, and meeting demand constraints.

Multi-attribute utility theory (MAUT) is a decision theory that allows the decision-maker to compare and evaluate different alternatives based on their overall utility or value. Main reference: Keeney and Raiffa [59].

The MAUT model involves identifying a set of decision alternatives and a set of attributes or criteria that are relevant to the decision problem. Each attribute is then assigned a weight or importance factor that reflects its relative importance to the decision-maker. The decision-maker also specifies the value or utility function for each attribute, which describes how the decision-maker values different levels of the attribute.

The MAUT model then aggregates the values or utilities for each attribute to obtain an overall utility or value for each alternative. The alternative with the highest overall utility or value is selected as the best alternative.

MAUT has a wide range of applications, such as in investment decision-making, risk assessment, and product development. For example, in investment decision-making, MAUT can be used to evaluate different investment opportunities based on their expected return, risk, liquidity, and other relevant attributes. In risk assessment, MAUT can be used to evaluate different risk mitigation strategies based on their effectiveness, cost, and other relevant attributes. In product development, MAUT can be used to evaluate different design options based on their performance, cost, and other relevant attributes.

Fuzzy multi-criteria programming (FMCP) is a mathematical programming model that allows the decision-maker to find a solution that is robust to the uncertainty in the criteria values. Main reference: Zimmermann [60].

In FMCP, the fuzzy criteria are typically expressed in terms of fuzzy sets that represent the degree of membership of the actual criterion value to each linguistic term, such as “low,” “medium,” and “high.” The FMCP model then minimizes or maximizes a fuzzy objective function that aggregates the fuzzy criteria values.

The solution to the FMCP model provides the decision-maker with a set of decision variables that satisfy the fuzzy criteria as closely as possible, while also being robust to the uncertainty in the criteria values. The FMCP model can also be used to evaluate trade-offs between different fuzzy criteria or objectives and to identify the best possible solution that balances these trade-offs.

FMCP has a wide range of applications, such as in engineering design, project management, and financial decision-making. For example, in engineering design, FMCP can be used to find the optimal design that satisfies multiple fuzzy criteria, such as minimizing weight, maximizing strength, and optimizing cost. In project management, FMCP can be used to identify the optimal project schedule that satisfies multiple fuzzy objectives, such as minimizing project duration, maximizing resource utilization, and meeting project deadlines. In financial decision-making, FMCP can be used to evaluate different investment opportunities based on their expected return, risk, and other fuzzy criteria.

Data envelopment analysis (DEA) is a non-parametric method of multiple criteria decision-making (MCDM) that is used to evaluate the relative efficiency of a set of decision-making units (DMUs) that have multiple inputs and outputs. DEA compares the efficiency of each DMU with the others by measuring their ability to use their inputs to produce their outputs. Main reference: Stewart [61].

The DEA model involves defining a set of DMUs that have similar inputs and outputs. The DMUs are then evaluated based on their ability to convert inputs into outputs using the most efficient methods possible. The DEA model assumes that each DMU uses a particular technology to convert inputs into outputs and that this technology is fixed and cannot be changed.

DEA is particularly useful in situations where the decision-maker has limited knowledge of the technology or processes used by the DMUs and where there is no clear definition of what constitutes the “best practice.” DEA is widely used in healthcare, finance, and public sector organizations to evaluate the performance of hospitals, banks, and government agencies, among others.

For example, in healthcare, DEA can be used to evaluate the efficiency of hospitals based on their inputs, such as staff, equipment, and resources, and their outputs, such as patient satisfaction, quality of care, and health outcomes. In finance, DEA can be used to evaluate the efficiency of banks based on their inputs, such as capital, labor, and assets, and their outputs, such as profitability, customer satisfaction, and loan quality. In the public sector, DEA can be used to evaluate the efficiency of government agencies based on their inputs, such as budgets, personnel, and resources, and their outputs, such as public services, citizen satisfaction, and policy outcomes.

Group decision-making (GDM) techniques are a type of multiple criteria decision-making (MCDM) model that involves a group of individuals working together to make a decision. GDM techniques are used when there are multiple criteria or objectives to consider and when the decision-making process requires the input and expertise of multiple individuals. Main references: Janis [44], (Irving Janis a social psychologist who developed the concept of groupthink, which describes the tendency of groups to make poor decisions due to pressure to conform and maintain group harmony); Paulus et al. [62], (Paul B. Paulus—a social psychologist who has conducted extensive research on group creativity and brainstorming); Klein [63], (Gary A. Klein—a cognitive psychologist who has studied decision-making in high-stress environments, such as emergency response and military operations); Pomerol and Adam [64], (Jean-Charles Pomerol—a computer scientist who has developed many computational models and tools for group decision-making).

GDM techniques typically involve a structured process for gathering information, discussing options, and reaching a consensus on a decision. The process may involve various stages, such as problem identification, goal setting, criteria development, alternative generation, evaluation and ranking, and implementation planning.

There are many different GDM techniques, each with its own strengths and weaknesses. Some common GDM techniques include:

• 1. Brainstorming—a technique where group members generate ideas and alternatives in a free-form, non-judgmental way.

• 2. Nominal group technique (NGT)—a structured approach that involves generating ideas individually and then sharing them with the group, followed by group discussion and ranking.

• 3. Delphi method—a technique that involves a series of rounds of questionnaires sent to the group members, where the responses are aggregated and fed back to the group for further discussion.

• 4. Analytic hierarchy process (AHP)—a technique that involves decomposing a decision problem into a hierarchy of criteria and alternatives, and then using pairwise comparisons to determine the relative importance of each criterion and alternative.

• 5. Multi-criteria decision analysis (MCDA)—a technique that involves using mathematical models to evaluate and rank alternatives based on their performance on multiple criteria.

GDM techniques can be applied in many different contexts, such as business, government, education, and healthcare. For example, GDM can be used in a business setting to make decisions on product development, marketing strategies, or resource allocation. In government, GDM can be used to make policy decisions, budget allocation, or project planning. In healthcare, GDM can be used to make decisions on patient care, resource allocation, or quality improvement initiatives.

It’s important to note that the field of GDM is interdisciplinary, and draws on research from many different fields, including psychology, sociology, management, computer science, and mathematics.

Technique for order of preference by similarity to ideal solution (TOPSIS)—TOPSIS is a method used to rank alternatives by measuring the distance between each alternative and the ideal solution. It is often used in engineering and environmental applications where decisions involve multiple criteria with both positive and negative attributes. For example, TOPSIS can be used in selecting the best location for a wind farm where factors like wind speed, distance from residential areas, and environmental impact are the criteria. Main reference: Hwang et al. [65].

ELECTRE: ELECTRE is a method used to evaluate alternatives based on multiple criteria and to rank them into categories or classes. It is often used in public policy and environmental applications where decisions involve a set of criteria with qualitative or fuzzy attributes. For example, ELECTRE can be used in evaluating the environmental impact of a new highway project where noise pollution, air pollution, and habitat destruction are the criteria. Main reference: Roy [66].

Overall, MCDM models are useful for decision-makers in situations where there are multiple, often conflicting, criteria that need to be taken into account. By using these models, decision-makers can make more informed decisions and consider a wider range of factors that impact the decision-making process.

9 Risk Management

Risk is the effect of uncertainty on established goals. It is the possibility of occurrence of events that affect the achievement or achievement of the objectives, combined with the impact of this occurrence on the intended results. Risks exist regardless of the attention we give them. Whether in our daily life or in the corporate world, we are immersed in an environment full of risks, opportunities, and threats that, if not managed, can compromise the achievement of desired goals.

With each decision-making, with each movement we perform, or fail to perform, we change the probability of future events occurring and, therefore, we increase or reduce the level of risks to which we are exposed. In life, there are people with a greater appetite for risk, who are willing to accept higher levels of risk because they assess that the positive impacts outweigh the negative ones. At the opposite extreme, there are people who are not comfortable with the possible effects of uncertainty on their goals.

Thus, in the face of the same risk, people may have different reactions, depending on their maturity and previous experiences, their ability to avoid, mitigate or enhance its occurrence, as well as to reduce or tolerate its impact.

Risk management consists of a set of coordinated activities to identify, analyze, assess, treat, and monitor risks. It is the process that aims to provide reasonable certainty as to the achievement of the objectives. To deal with risks and increase the chance of achieving objectives, organizations adopt from informal approaches to highly structured and systematized approaches to risk management, depending on their size and the complexity of their operations.

Risk management is a crucial process in organizations that aims to identify, assess, and mitigate risks that may hinder the achievement of the organization’s objectives. Risk management encompasses all the activities that an organization undertakes to reduce the likelihood of an unfavorable event occurring, mitigate the impact of any adverse event, and ensure business continuity. Effective risk management allows an organization to identify and manage risks, thereby improving decision-making, enhancing operational efficiency, and reducing losses.

The risk management process involves identifying, analyzing and assessing risks, selecting and implementing responses to assessed risks, monitoring risks and controls, and communicating about risks with stakeholders, both internal and external. This process is applied to a wide range of the organization’s activities, at all levels, including strategies, decisions, operations, processes, functions, projects, products, services, and assets, and is supported by the entity’s culture and risk management structure.

It should be noted that the documentation of the activities carried out during the risk management process is an important tool for accountability,⁵ as well as facilitating communication with stakeholders.

The first step in risk management is to identify and assess risks. This involves identifying potential risks and evaluating their likelihood and potential impact on the organization’s objectives. Risks can arise from various sources, including internal factors such as operational processes, human error, and technology systems, and external factors such as economic, political, and environmental factors. Risk identification can be done through various methods such as brainstorming, scenario analysis, and historical data analysis.

Once risks have been identified and assessed, the next step is to develop a risk management plan. The plan should outline the strategies and actions that the organization will take to mitigate, transfer, or accept risks. The plan should also include a risk monitoring and reporting mechanism to track the effectiveness of the risk management strategies and identify any emerging risks. The risk management plan should be regularly reviewed and updated to reflect changes in the organization’s risk profile and objectives.

One of the essential elements of risk management is risk communication. Effective communication ensures that stakeholders are aware of the risks and the organization’s risk management strategies. It also promotes transparency, accountability, and trust. Communication can be done through various channels such as reports, presentations, meetings, and training sessions. The organization should tailor the communication to the needs of the stakeholders, including the board of directors, employees, customers, suppliers, and regulators.

Risk management also involves the implementation of risk controls to mitigate the impact of adverse events. Risk controls can be preventive, detective, or corrective. Preventive controls aim to reduce the likelihood of an adverse event occurring, such as implementing policies and procedures, conducting background checks on employees, and securing information technology (IT) systems. Detective controls aim to identify adverse events that have occurred or are in progress, such as audits, monitoring, and reporting systems. Corrective controls aim to reduce the impact of adverse events, such as contingency planning, insurance, and disaster recovery plans.

Risk management is not a one-time event but rather a continuous process that requires ongoing monitoring and evaluation. The organization should regularly review its risk management plan and make necessary adjustments to ensure that it remains effective. This includes evaluating the effectiveness of the risk controls, identifying emerging risks, and adapting to changes in the internal and external environment.

Effective risk management has numerous benefits for organizations. It helps to reduce losses, improve decision-making, and enhance operational efficiency. It also enhances stakeholder confidence and trust, improves the organization’s reputation, and ensures compliance with legal and regulatory requirements. However, failure to manage risks effectively can lead to significant losses, reputational damage, and even bankruptcy.

In conclusion, risk management is a critical process in organizations that aims to identify, assess, and mitigate risks that may hinder the achievement of the organization’s objectives. Effective risk management involves identifying and assessing risks, developing a risk management plan, implementing risk controls, communicating risks and risk management strategies, and ongoing monitoring and evaluation. Organizations that manage risks effectively can reap numerous benefits, including reducing losses, improving decision-making, and enhancing stakeholder confidence and trust. Therefore, it is essential for organizations to prioritize risk management and develop a risk-aware culture that promotes continuous improvement and innovation.

9.1 Risk Analysis

Risk analysis is the process of understanding the nature and determining the level of risk, in order to support the assessment and treatment of risks. Risk is a function of both probability and the measure of consequences. Thus, the level of risk is expressed by combining the probability of occurrence of the event and the resulting consequences in the event of materialization, that is, the impact on the objectives.

The result of this process will be to assign to each identified risk a rating, both for the probability and for the impact of the event, the combination of which will determine the level of risk. The identification of factors that affect the likelihood and consequences is also part of risk analysis, including the assessment of the causes, sources, and positive or negative consequences of the risk, expressed in tangible or intangible terms. Depending on the circumstances, risk analysis can be qualitative, semi-quantitative, or quantitative, or a combination of these.

Risk analysis is only completed when the actions that management adopts to respond to them are also evaluated, reaching the level of residual risk, the risk that still remains after considering the effect of the responses adopted by management to reduce the probability and impact of risks, including internal controls and other actions. Forms of risk response can range from accepting, reducing, avoiding, or sharing risk, including establishing control activities to ensure that defined responses are effectively applied.

9.2 Risk Assessment

The purpose of risk assessment is to assist in decision-making, based on the results of risk analysis, about which risks require treatment and the priority for the implementation of treatment.

9.3 Risk Treatment

Risk treatment involves the selection of one or more options to modify the level of each risk and the elaboration of treatment plans that, once implemented, will entail new controls or modification of existing ones.

• 1. Avoiding risk is the decision not to start or discontinue the activity, or to get rid of the object subject to the risk.

• 2. Reducing or mitigating risk consists of taking measures to reduce the likelihood or consequence of the risks or even both. The procedures that an organization establishes to address risks are called internal control activities.

• 3. Sharing or transferring risk is the special case of mitigating the consequence or probability of occurrence of the risk through the transfer or sharing of a part of the risk, by contracting insurance or outsourcing activities in which the organization does not have sufficient domain.

• 4. To accept or tolerate risk is to deliberately take no action to alter the likelihood or consequence of the risk. It occurs when the risk is within the organization’s tolerance level (e.g., when the risk is considered low), the ability to do anything about the risk is limited, or the cost of taking any action is disproportionate to the potential benefit (e.g., spending more financial resources to protect an asset than the value of the asset itself).

9.4 Critical Monitoring and Analysis

Critical monitoring and analysis is an essential step in risk management and aims to: detect changes in the external and internal context, including changes in risk criteria and in the risk itself, which may require review of risk treatments and their priorities, as well as identify emerging risks.

Risk management includes activities such as:

• (a) Continuous (or at least frequent) monitoring by the functions that manage and have ownership of risks and by the functions that supervise risks, with a view to measuring the performance of risk management, through key risk indicators, analysis of the pace of activities, operations or current flows in comparison with what would be necessary for the achievement of objectives or maintenance of activities within the established risk criteria.

• (b) Critical analysis of risks and their treatments carried out by the functions that manage and have risk ownership and/or by the functions that supervise risks, through control and risk self-assessment (CRSA).

• (c) Audits carried out by the functions that provide independent assessments, either through internal or external audit, focusing on the structure and process of risk management, at all relevant levels of organizational activities, that is, seeking to test the systemic aspects of risk management instead of specific situations.