When managers are making decisions where the outcomes of alternatives are not known they are working under conditions of?

1. What is the difference between programmed and nonprogrammed decisions?

Because managers have limited time and must use that time wisely to be effective, it is important for them to distinguish between decisions that can have structure and routine applied to them (called programmed decisions) and decisions that are novel and require thought and attention (nonprogrammed decisions).

Programmed decisions are those that are repeated over time and for which an existing set of rules can be developed to guide the process. These decisions might be simple, or they could be fairly complex, but the criteria that go into making the decision are all known or can at least be estimated with a reasonable degree of accuracy. For example, deciding how many raw materials to order should be a programmed decision based on anticipated production, existing stock, and anticipated length of time for the delivery of the final product. As another example, consider a retail store manager developing the weekly work schedule for part-time employees. The manager must consider how busy the store is likely to be, taking into account seasonal fluctuations in business. Then, they must consider the availability of the workers by taking into account requests for vacation and for other obligations that employees might have (such as school). Establishing the schedule might be complex, but it is still a programmed decision: it is made on a regular basis based on well-understood criteria, so structure can be applied to the process. For programmed decisions, managers often develop heuristics, or mental shortcuts, to help reach a decision. For example, the retail store manager may not know how busy the store will be the week of a big sale, but might routinely increase staff by 30% every time there is a big sale (because this has been fairly effective in the past). Heuristics are efficient—they save time for the decision maker by generating an adequate solution quickly. Heuristics don’t necessarily yield the optimal solution—deeper cognitive processing may be required for that. However, they generally yield a good solution. Heuristics are often used for programmed decisions, because experience in making the decision over and over helps the decision maker know what to expect and how to react. Programmed decision-making can also be taught fairly easily to another person. The rules and criteria, and how they relate to outcomes, can be clearly laid out so that a good decision can be reached by the new decision maker. Programmed decisions are also sometimes referred to as routine or low-involvement decisions because they don’t require in-depth mental processing to reach a decision. High- and low-involvement decisions are illustrated in Exhibit 2.3.

Exhibit 2.3 High-Involvement and Low-Involvement Decisions. (Attribution: Copyright Rice University, OpenStax, under CC-BY 4.0 license)

While decisions makers can use mental shortcuts with programmed decisions, they should use a systematic process with nonprogrammed decisions. The decision-making process is illustrated in Exhibit 2.4 and can be broken down into a series of six steps, as follows:

1. Recognize that a decision needs to be made.
2. Generate multiple alternatives.
3. Analyze the alternatives.
4. Select an alternative.
5. Implement the selected alternative.
6. Evaluate its effectiveness.

While these steps may seem straightforward, individuals often skip steps or spend too little time on some steps. In fact, sometimes people will refuse to acknowledge a problem (Step 1) because they aren’t sure how to address it. We’ll discuss the steps more later in the chapter, when we review ways to improve the quality of decision-making.

Exhibit 2.4 The Decision-Making Process. (Attribution: Copyright Rice University, OpenStax, under CC-BY 4.0 license)

You may notice similarities between the two systems of decision-making in our brains and the two types of decisions (programmed and nonprogrammed). Nonprogrammed decisions will generally need to be processed via the reflective system in our brains in order for us to reach a good decision. But with programmed decisions, heuristics can allow decision makers to switch to the quick, reactive system and then move along quickly to other issues.

1. Give an example of a programmed decision that a manager might face.
2. Give an example of a nonprogrammed decision.
3. What are heuristics, and when are they helpful?
4. How are programmed and nonprogrammed decisions connected to the reflective and reactive systems in the brain?

Decision making is the process of making choices. It is about identifying a problem or decision, gathering information, and assessing alternatives and solutions.

Using a step-by-step decision-making process can help you consistently make more deliberate, thoughtful decisions by organising relevant information and defining alternatives.

There are three conditions that you will face when making decisions: certainty, risk, and uncertainty.

Depending on the amount and degree of knowledge you have, the conditions are:

1. Making decisions under pure uncertainty (“I don’t know”) – You are ignorant or have absolutely no knowledge, not even about the likelihood of occurrence for an event. Your behaviour is purely based on your attitude toward the unknown.
2. Making decisions under risk (“I know the probability estimates”) – You have some knowledge and can assign subjective probabilities regarding each event.
3. Making decisions by acquiring more information (“I can acquire reliable information”) – You acquire more information and knowledge to reach a certain level of ‘certainty’.

When you feel as if you are not sure if you want to take a new job or not, this is an example of uncertainty. When the economy is going bad and causing everyone to worry about what will happen next, this is another example of uncertainty.

Causes of uncertainty include:

1. Lack of information (or knowledge).
2. An abundance of information (or knowledge).
3. Conflicting nature of pieces of information.
4. Measurement errors.
5. The subjectivity of opinions derives from the subjective interpretation of the available pieces of information.

In response to uncertainties, you could either cope with the uncertainty or reduce the uncertainty.

1. Uncertainty coping impacts your exposure across a wide range of uncertainties. In some cases, this requires you to change your actions or strategies. There are three ways you can reduce uncertainty – information gathering, proactive collaboration and networking.
2. Uncertainty reduction, on the other hand, minimises your exposure to uncertainties without changing your actions or strategies. This is a natural, primary motivator and fundamental need that guides your behaviour and actions. There are five approaches for coping with uncertainty – flexibility through diversification, imitation, reactive collaboration, vertical or horizontal integration with other organisations, and avoiding uncertainty altogether:

Making decisions under certainty

A condition of certainty exists when you know with reasonable certainty what the alternatives are, what conditions are associated with each alternative and the outcome of each alternative. This is one end of the certainty-uncertainty spectrum,

Under conditions of certainty, accurate, measurable, and reliable information on which to base decisions is available to you. The future and outcome are highly predictable under conditions of certainty.

Such conditions exist in case of routine and repetitive decisions concerning the day-to-day operations of the business.

The more information the decision-maker has, the better the decision will be.

Making decisions under uncertainty

Even the simplest of decisions carry some level of uncertainty.

In choosing a cup of coffee, there will be at least the possibility that the coffee doesn’t taste good, is not hot, or will not provide the usual pleasurable feeling.

Conditions of uncertainty exist at the other end of the certainty-uncertainty spectrum. This is when the future and outcome are unpredictable. Everything is in a state of flux. You are not aware of all available alternatives, the opportunities and risks associated with each alternative, the likelihood and consequences of each alternative, and the likelihood and extent of your success.

In making decisions under pure uncertainty, you do not have any information about the outcomes.

There are many unknowns. Nobody knows what will happen. There is no possibility of knowing what could occur in the future to alter the outcome of your decision. You feel uncertainty about a situation.

In the face of such uncertainty, you make certain assumptions about the situation. This provides a reasonable framework for decision-making. You depend on your judgment and experience to make decisions.

There are several techniques to improve the quality of decision-making under conditions of uncertainty. These include risk analysis and decision trees.

Making decisions under risk

You are making decisions under risk when you have incomplete or some information about the opportunities and risks associated with each alternative, the likelihood and consequences of each alternative, and the likelihood and extent of your success,

In making decisions under risk, you have some knowledge regarding the likelihood of occurrence of each outcome. Factor probability into the decision-making process. This is a substitute for certainty. It could also be a substitute for complete knowledge.

Measure the likelihood of occurrence for an event with probability.

Distinguishing between making decisions uncertainty versus making decisions under risk

When laypersons talk about risk, they generally mean uncertainty.

But decision making under both conditions of uncertainty and risk are distinguishable.

In making decisions under risk, you can predict the possibility of a future outcome. But when making decisions under uncertainty, you cannot.

Risks can be managed while uncertainty is uncontrollable. You can assign a probability to risks events. While with uncertainty, you can’t.

Therefore, risk is present when future events occur with some measurable probability. Uncertainty is present when the likelihood of future events is indefinite or incalculable.

This is where the definition of ‘risk’ in the international risk standard, ISO 31000 – “The effect of uncertainty on objectives” – comes in. It is not about the uncertainty itself, but the potential impact of the uncertainty. A probability rating can reasonably be assigned to the potential consequences of the uncertainty.

Based on the ISO 31000 definition of risk, your objectives are important both in identifying problems and in evaluating alternative solutions. Objectives are the criteria that reflect the attributes of alternatives relevant to the choice.

How you frame your situation or problem, either is uncertainty or risk, can make a significant difference to your conclusion. It also impacts how you approach your decision making.

Decision making under uncertain and risky situations

Therefore, there are two possible extremes in decision-making along the certainty-uncertainty spectrum. It depends upon the degree of knowledge that can enable you to predict the likelihood and extend of your success.

A good decision can be judged solely by the outcome alone when there is a certainty. This is at one end of the certainty-uncertainty spectrum.

The opposite end of the certainty-uncertainty spectrum is pure uncertainty.

Between these two extremes are decision-making under risk.

The main idea here is that for any given situation, the degree of certainty and risk along the certainty-uncertainty spectrum varies depending upon how much knowledge you have.

Information gap between what is known, and what needs to be known for an optimal decision to be made can be quantified with probability. Use probability to protect any adverse uncertainty or the exploitation of uncertainty.

A better way to manage risk and uncertainty is to use probabilities and ranged estimates, instead of just single-point estimates.

Suppose you are a marketing manager working on a market entry strategy for a new product. A previous survey indicated a 70 per cent probability of achieving your desired market share, but a more recent survey indicates only a 55 per cent probability. Should you proceed with the market entry strategy? Call it off? Conduct a third survey?

This is where concepts of risk and risk management come into play for making effective decisions.

Risk and risk management

Risk has been regarded solely as a negative concept where people should try to avoid or transfer to others.

Now, it is recognised that risk is simply a fact of life that cannot be avoided or denied, but managed.

When you understand risk and how it is caused and influenced, you can change it so that you are more likely to achieve your objectives. You might even perform faster, more efficiently or with improved results.

Risk is implicit in all decisions you make

As shown in the example above, how you frame your situation – whether you look at your decision from the perspective of uncertainty or risk – and how you make those decisions will affect how successful you are in achieving your objectives.

The international risk management standard, ISO 31000, places risk in the context of what an organisation or individual wishes to achieve – its objectives. Risk arises because those objectives are pursued against an uncertain background.

You may set your objectives. To achieve them, you often must contend with internal and external factors and influences. These factors and influences may not be within your control and which generate uncertainty and thus risk. These factors might assist or speed up the achievement of objectives. They may also prevent or delay you from achieving your objectives.

For example, the risk isn’t the chance of the share market crashing but the chance that a crash will disrupt or affect you or your organisation’s objectives by limiting capital for expansion.

Hence, ISO 31000’s definition of risk is “the effect of uncertainty on objectives.”

Risk management enables you to achieve your objectives

Based on ISO 31000, the risk is characterised and described in terms of both the consequences of what could happen and the likelihood of those consequences on the achievement of your objectives. One simple way of describing potential consequences is to say what could happen and what could it lead to.

The consequences may involve loss, harm and detrimental effects. It could involve opportunity, benefit and advantage. Whether you describe the consequences in a negative or positive frame depends on your point of view, where your loss will be someone else’s gain.

Consequences and their likelihoods are often combined to define a level of risk.

Risk management is the process of taking steps to either maximise opportunities or reduce threats by introducing the appropriate measures. Decision making is closely linked to risk management. It is the process of identifying risks and planning actions to manage the risks. Assess and prioritise the identified risks. The goal is to create, protect and enhance value by managing uncertainties that are influencing the achievements of your objectives.