This includes Retained Executive Search, Facilitation, Leadership Development and a wide variety of other group process activities. Dan has earned a MEd. He is an adjunct faculty member for Peabody College of Vanderbilt University and has also taught at Belmont University. Skip to main content. Satisficing Versus Optimizing: Making your Choice Shopping is a process that people either tend to enjoy or hate.
Here are some of the factors I consider when I buy a car: price color reliability safety capacity cargo and people warranty appearance You may have other factors that you consider, but I suspect each one of us will optimize when we buy a car since we take a number of factors into account and then make the best choice when considering all of the factors.
Here are a few reasons to consider: time constraints impact of a wrong choice cost external perception organizational strategy, culture or direction When viewing this list, each of these factors can help us determine whether we need to optimize or satisfice.
Here are few cases where we need to optimize: hiring a new senior leader determining the location of a new office deciding upon a new product line expansion or retraction of a business unit Each of the factors listed above require optimization in most cases. Consider decisions that you make today and determine if you are optimizing or satisficing? Did you use the right approach? With limitations on information, thoughtful analysis may be impossible. There are two ends of a spectrum from which to approach this: satisficing and optimizing.
Optimizing involves collecting as much data as possible and trying to find the optimal choice. A branch of management called management science offers methods for solving complex problems. One way to describe this is a conscious and a subconscious perspective. The subconscious mind is automatic and intuitive , rapidly consolidating data and producing a decision almost immediately.
The subconscious mind works best with repeated experiences. The conscious, rational mind requires more effort, using logic and reason to make a choice.
For example, the subconscious mind throws a ball and hits the target, while the conscious mind slowly describes the physics and forces required to complete the action. Will walking also truly lead to exercise? Again, using a scale of one to ten, you can assess this probability. You feel that walking is decent exercise, so it probably gets a probability rating of six regarding your desired consequence of getting exercise.
You will not get to class on time, however, so walking gets a probability rating of only one for this consequence. You can rate other options the same way. Bicycling is good exercise prob.
Driving is dependable if you can find a parking space. If you cannot find a space, you will be late for class prob. Also, unless you park far from class, you will get no exercise prob.
You need to compute the expected utility, or "EU," of each option. You do so by multiplying how much each consequence of the option is worth to you which is its basic utility rating by the probability that it will occur. The product of this multiplication is the EU for the consequence. The EU of the entire option is the sum of the EU of all possible consequences within that option. For example, consider the option of riding your bicycle.
To find out if you should or not, you want to know the EU of that option. You believe that riding your bicycle is associated with two consequences: being on time and getting exercise. Each of these consequences has its own EU.
To find out just what should happen if you ride your bike, you need to examine the EU of both its consequences. Table Finally, you choose the outcome with the greatest EU. As you can see, you should ride your bicycle to class today. The SEU model is thus an optimizing decision model that is based on a person's own personal estimates of probability and value.
We can use it in circumstances in which it is difficult to obtain objective estimates of decision-making outcomes. This is often true with decisions that people make in the "real world. Yet millions of people decide every year to visit the Grand Canyon. On Time. Prob X. Using the SEU model, we can assume that people make their best decision when they try to get the best results for themselves or for whomever the decision should benefit.
This idea fits in with optimizing decision theory. Therefore, the option that is chosen as the "best" is likely to vary from person to person. Criticisms of the SEU model. However, the model falls prey to other criticisms. First, it assumes that decision making is in some sense "compensatory. In our example, bicycling received a bad utility rating because of the inconvenience of becoming sweaty. However, it also received a good estimate for the probability of getting to class on time.
Thus, bicycling was the best choice. The problem is that some circumstances clearly cannot meet this compensatory assumption. For instance, a situation can be "conjunctive. All other criteria are immaterial. Fischoff et al. The couple wishes to travel to a place that is reasonably priced, available, sunny, and quiet. They say they will stay at home if no place can meet all four criteria. For instance, if they arrive at a place that is cheap, available, and sunny, their whole vacation will be ruined if their hotel is close to a noisy highway.
Other situations may be "disjunctive. The investment is acceptable if it is a good speculation, a good tax shelter, or a good hedge against inflation. The person will make the investment if it is any of these three things. The point that Fischoff et al. Second, scientists have criticized the model because they are not sure that it accurately reveals the steps that people take as they make decisions. For example, assume that we have seen Janet bicycling to class.
We wish to discover how she made her decision to use her bicycle. We ask Janet to tell us of the various alternatives she had available to her, as well as the possible consequences of each.
We further ask her to tell us the probability and utility of each consequence, in relation to every possible action. We then compute the expected utility of each option. The model predicts that Janet would have bicycled.
We conclude that Janet used the SEU model to make her decision. Our conclusion could easily be wrong. It may be that Janet only considered the probabilities of getting sufficient exercise and of arriving at class on time.
To make her decision, she simply added the probabilities together. A model for her decision is illustrated in Table As you can see, Janet made the same decision that the SEU model predicted she would make. However, she did not consider the utility of each consequence. Janet was only concerned with the probability of whether the consequence would occur. It was true that Janet could report the utility of each consequence when we asked her.
Still, she did not use these utility ratings when she originally made the choice to bicycle to class. We can propose many other models that would make the same prediction.
Each would show that bicycling would be the best course of action for Janet, based on her situation. Note, for example, the probability ratings for getting sufficient exercise.
These alone could lead to a prediction that bicycling was the best option for Janet. Thus, many models can predict decisions as well as the SEU model. This means scientists must turn to other evidence to discover how people make decisions. Researchers have done just that. Some evidence has even cast doubt on the theory behind the SEU model. These findings suggest that people may not naturally optimize when they make decisions, even when scientists can predict their decisions by using the SEU model.
Satisficing Decision Theory. Simon was the first prominent theorist to doubt that people are able to calculate the optimal choice. He believed that it is impossible for people to consider all the options and all the information about those items that the SEU and similar models assume. Simon proposed his own model of decision making as an alternative to the optimizing approach.
He called his proposal the ''satisficing'' decision model. It implies that people think of options, one by one, and choose the first course of action that meets or surpasses some minimum criterion that will satisfy them.
People examine possible options in the order that they think of them. Eventually, they accept the first option that meets their criterion. To illustrate Simon's idea, we shall return to the example of choosing how to get to class.
Suppose four possible courses of action will help you get to class. Each has a number that represents its subjective value. One of the possibilities is walking, which has a value of 6.
The others are taking the bus 10 , bicycling 12 , and driving 5. Keeping these subjective values in mind, you begin the process of deciding on a course of action. First, you establish a level of aspiration. You decide, for example, that an option must have the value of at least 8 before it will satisfy you.
Next, you evaluate your options. You first think of walking. It has a value of 6. This does not meet the level of aspiration. Therefore, you reject it as a possibility. The second option that comes to your mind is taking the bus. It is worth This meets the level of aspiration, so you accept it.
Satisfactory versus optimal. You may wonder why our example above did not once again lead to the decision to bicycle to class. We know that bicycling is the optimal decision, because it has a value of However, Simon believed that you would never consider bicycling.
The idea of taking the bus came into your head before you had a chance to think about bicycling. Once you found the satisfactory option of taking the bus, you never thought of any other possibilities.
Hence, you end up with a satisfactory option, but not the optimal one. Despite the example above, Simon believed that, in the long run, the satisficing process leads to the optimal decision more often than not. He believed that a person's level of aspiration can rise and fall over time.
This fluctuation depends on the respective ease or difficulty of finding satisfactory options. In our example, you were able to find a satisfactory option fairly easily. Taking a bus was only the second alternative you considered. Perhaps you will become more demanding the next time you wonder how to get to class. You reached a decision so easily the first time you may feel more confident that there is an even better option available to you. In this situation, you will probably raise your level of aspiration.
It is hoped that the criterion will continue to shift upward over time. Ideally, it should reach the point where only the optimal choice will be satisfactory. If this happens, the results of the satisficing model will approximate the outcome of an optimizing model. People will make their best choice despite their inability to optimize. Decision Heuristics. Simon's satisficing model is an example of a "heuristic. These methods approximate the results of more complex optimizing models, but they are easier for people to use.
Many studies have shown that people usually use heuristics when they make judgments and decisions. This evidence continues to mount.
Tversky and Kahneman Heuristics. In a classic article in , Tversky and Kahneman proposed three heuristics that people seem to use when they estimate the probabilities of events. As with Simon's satisficing model, these heuristics are far simpler than analogous optimizing methods.
They also usually lead to the optimal judgment, as Simon's methodology does. However, heuristics do have a negative side. When they backfire, the errors that result are not random. Thus, the results will not cancel each other.
Instead, when people follow a heuristic model, their errors will be biased in ways that are harmful to decision making. This is an important aspect of the heuristics that we shall examine. Representativeness heuristic. The first heuristic that Tversky and Kahneman proposed was the representativeness heuristic.
The representative heuristic is relevant when people attempt to estimate the extent to which objects or events relate to one another. The representativeness heuristic maintains that, when people do this, they note how much objects or events resemble one another. They then tend to use this resemblance as a basis for judgment when they make their estimates.
As with other heuristics, the representativeness heuristic usually leads to correct judgments. Nisbett and Ross provide an example of this. Someone asks Peter to estimate how clearly an all-male jury relates to the United States population as a whole.
He needs to decide how representative of the population the jury is. He will no doubt give the jury a low estimate, and he would be correct. Clearly, the population of the United States is made up of both men and women.
Therefore, an all-male jury does not "look like" the general population. Peter notes this and makes the correct, low estimate. However, in many circumstances basing judgments on resemblance leads to error. For instance, people may have additional information that can help them find out the probability that the objects or events they consider are related.
In these situations, people are incorrect if they use resemblance as the sole basis for judgments. In one of Tversky and Kahneman's studies, the researchers gave participants a personality description of a fictional person. The scientists supposedly chose the person at random from a group of people.
They told participants that 70 people in the group were farmers and 30 were librarians. They then asked the participants to guess if the person was a farmer or librarian. The description of the fictional person was as follows:. Steve is very shy and withdrawn. He is invariably helpful, but he has little interest in people or in the world of reality. A meek and tidy soul, he has a need for order and structure and a passion for detail.
Most people in the experiment guessed that Steve was a librarian. They apparently felt that he resembled a stereotypical conception of librarians. In so doing, the participants ignored other information at their disposal. They knew that Steve was part of a sample in which 70 percent of the members were farmers.
Thus, the odds were that Steve was a farmer, despite his personality. The participants should have taken these odds into account when they made their decision. Cause and result. Individuals may also err when they judge whether an event is the result of a certain cause. This might happen if they look for the extent to which the event resembles one of its possible causes.
If people use this resemblance, they may choose an incorrect cause. For example, imagine being shown various series of the letters "H" and "T. One side of the coin was "H" "Heads" and the other side was "T" "Tails". This is because the series looked random to them. They are wrong. A random process can cause all of the different series. Many people misunderstand random processes.
They think the result of a random cause should "look" random. This is not necessarily true. We can see how a random process would lead to results that look rather unrandom. On the first toss of a coin, for example, there is a 50 percent chance of H and a 50 percent chance of T. No matter what the result of this first flip is, the second toss will have the same odds. There will again be a 50 percent chance of either H or T. Continuing this logic, for six tosses there is a 1.
As you can see, all the different series combinations have the same odds, and all have a random cause. A similar error is the "gambler's fallacy.
The "gambler" believes this because a T would "look" more random than another H would. However, as long as the coin is fair, there is still a chance that the next flip will be an H. Hence, the representativeness heuristic often leads to correct answers, but it can also cause people to err in their judgments. Outcomes that resemble one another are not necessarily related. Availability heuristic. Tversky and Kahneman's second proposal was the availability heuristic. This heuristic maintains that the ease with which examples of an object or an event come to mind is important.
People tend to estimate the probability that an event will occur or that an object exists, based on whether they can think of examples easily.
As with the representativeness heuristic, this strategy usually leads to satisfactory decisions. For example, someone may ask you if more words in the English language begin with "r" or with "k. You are then able to figure out the percentage of words that begin with each letter. In this way, you could no doubt correctly decide which letter starts the most words. Similarly, availability helps the satisficing model work as well.
One reason satisficing usually results in the optimum choice is that the best option usually comes to mind quickly. However, as with the representativeness approach, the availability heuristic can easily lead people astray. There are many factors that bring an object to our attention. Some of these factors are not conducive to good judgment.
One study revealed that the factor of how well known something is can cause people to make incorrect decisions. In the experiment, participants heard a list of names of men and women. The researchers then asked them to judge if the list had more men's names or more women's names. The list actually had an equal number of names from each gender. However, some of the names were more well-known than others. The well-known names were mainly from one gender, and the participants tended to choose that gender as the one that supposedly dominated the list.
In another study, experimenters asked participants which English words were more common, those with "r" as their first letter or those with "r" as their third letter. Most people said that words that begin with "r" are more numerous. They probably did so because it is easy to think of relevant examples, such as "rat," "rabbit," "really," etc. However, this was the wrong answer. You need only look at any random piece of writing to see this.
In fact, you can look at the words in the sentence that described this experiment: "participants," "words," "were," "more," and "first. This is because we tend to use first letters to organize words in our minds. Thus, the availability heuristic often leads to correct conclusions. However, it can also create errors. People may think quickly of well-known or vivid examples.
It may be, however, that the more well-known options are not the best decisions that people can make. Conjunctive fallacy. An implication of the representativeness and availability heuristics is the conjunctive fallacy.
The conjunctive fallacy is the tendency to believe that the conjunction, or combination, of two attributes or events A and B is more likely to occur than one of its parts A. The conjunctive fallacy occurs either because the conjunction is more representative of stereotypes or more available to our imagination. For example, imagine that the probability that Sue Blue is smart is 40 percent and the probability that Sue Blue wears glasses is 30 percent.
Given this information, what is the probability that Sue is both smart and wears glasses? The most it can be is 30 percent, and only when everyone who wears glasses is smart. Normally, the probability of a conjunction will be less than either of its parts. However, if we have a stereotype in our minds that smart people wear glasses, or find this easy to imagine, we might consider the probability to be higher than 40 percent. They gave participants descriptions such as:. Bill is 34 years old.
He is intelligent but imaginative, compulsive, and generally listless. In school, he was strong in mathematics but weak in social sciences and humanities. They then asked their participants to judge the probability that Bill. A - is an accountant. B - plays jazz for a hobby. About 85 percent of the participants gave a higher probability to the A-B conjunction than to B alone.
One can guess that the description of Bill meets the stereotype of an accountant but not the stereotype of a jazz musician. Nonetheless, given that participants thought it likely that Bill was an accountant, they must also have thought it reasonable that he might have an unusual hobby. Vividness criterion. Nisbett and Ross argued that there is one significant reason that the representativeness and availability heuristics sometimes lead to incorrect decisions. They proposed a "vividness criterion.
The criterion involves the idea that people recall information that is "vivid" far more often and far more easily than they recall "pallid" information. Something is vivid when it gets our attention and holds our attention. There are different ways in which information can get and hold our attention.
One way is the extent to which the data is emotionally interesting and relevant to ourselves or to someone whom we value. Another way in which information can be vivid is the extent to which it is image-provoking or concrete. Judging by news media attention, people appear to have far greater interest in events that happen near to them than in events that take place far away. For instance, they will have a large amount of interest in the murder of one person in their town.
This will be particularly true if the story is accompanied by vivid pictures of the victim. In contrast, people will be only somewhat interested in the murder of thousands of people in some far-off land. They will have even less interest if there are not pictures accompanying the report. We can see how the idea of the vividness criterion was at work in some of the heuristic examples we have already discussed. For instance, people placed a great deal of trust in the concrete description of "Steve.
In contrast, the participants did not pay much attention to the pallid, statistical information that 70 percent of the sample were farmers. Hence, the participants made incorrect decisions because they concentrated only on the vivid information. Nisbett and Ross have shown this to be a normal tendency in other studies they have described.
Anchoring heuristic. Tversky and Kahneman proposed a third heuristic called the anchoring heuristic. This approach involves the manner by which people adjust their estimates. When people make estimates, they often start at an initial value and then adjust that value as they go along. Researchers have found that people tend to make adjustments that are insufficient.
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