Sources of Power | Alessandro Desantis

Highlights

Yet the sources of power that are needed in natural settings are usually not analytical at all—the power of intuition, mental simulation, metaphor, and storytelling.
They were not actually making a decision; they were constructing a justification.
The commanders’ secret was that their experience let them see a situation, even a nonroutine one, as an example of a prototype, so they knew the typical course of action right away. Their experience let them identify a reasonable reaction as the first one they considered, so they did not bother thinking of others. They were not being perverse. They were being skillful. We now call this strategy recognition-primed decision making.
We can call this strategy a singular evaluation approach, to distinguish it from comparative evaluation. Singular evaluation means evaluating each option on its own merits, even if we cycle through several possibilities.
Deliberating about options makes a lot of sense for novices, who have to think their way through the decision.
This is one basis for what we call intuition: recognizing things without knowing how we do the recognizing.
Many people think of intuition as an inborn trait—something we are born with. I am not aware of any evidence showing that some people are blessed with intuition, and others are not. My claim in this chapter is that intuition grows out of experience.
People seem more likely to use singular strategies in the following situations: When time pressure is greater, evaluating one option at a time until an acceptable one is found. The reason is that it takes too much time to lay out the alternatives and analyze the evaluation criteria. When people are more experienced in the domain. With experience, they can be more confident in their ability to size up the situation and recognize plausible courses of action as the first ones they consider. When the conditions are more dynamic. The time and effort needed to set up an analysis can be rendered useless when the context shifts. When the goals are ill defined. Ambiguity makes it hard to come up with evaluation criteria that apply across all options.
people seem more likely to use comparative evaluation in these situations: When they have to justify their choice. Higher authorities usually look for evidence that alternatives were considered. When conflict resolution is a factor. In the case of the Denver bullets, different stakeholders held different priorities, and a common analytical metric was needed to place all the considerations into a common format. When the decision maker is trying to optimize, finding the best course of action. This is what the comparison is all about. The singular evaluation that uses mental simulation tries to find the first workable option, not necessarily the best. When the situation is computationally complex. This is not something anyone can easily recognize, such as analyzing an investment portfolio to select the best strategy.
Table 7.1 suggests that we will be more likely to compare options when faced with unfamiliar situations. The reason is that a lack of experience will prevent us from generating reasonable options, or will at least reduce our confidence in the options we do generate. If time pressure is low, the conditions are fairly stable, and the goals arc clear, then we should expect to find the highest level of option comparisons.
The analytical methods are not the ideal; they are the fallback for those without enough experience to know what to do.
The strategy of compiling an extensive experience bank appears important. But the mere accumulation of experiences may not be sufficient. The experiences need to include feedback that is accurate, diagnostic, and timely.8 In domains were it is possible to obtain such feedback (e.g., weather forecasting), decision-making expertise develops.
For ill-defined goals, we can expect to see a lot of goal modification during the problem-solving effort.5
De Groot (1945) and Isenberg (1984) have suggested that what triggers active problem solving is the ability to recognize when a goal is reachable.
The criterion for claiming that a goal is ill defined is that experienced people might not agree about how to satisfy the goal.
According to Reitman (1965), a problem can be ill structured if the initial state is not defined, the terminal state is undefined, or the procedure for transforming the initial state into the terminal state is undefined.
accumulation of experience does not weigh people down; it lightens them up.
One critical type of cue that surprises experts, but not novices, is the absence of a key event. Since novices do not know what is supposed to happen, they are slow to appreciate the significance of something’s not happening. Experts pick this up right away.
The appropriate time horizon depends on the reaction time of the system at different levels.6
Experts are often sensitive to their own memory limitations, including their working memory for holding something like a telephone number and their long-term memory for anticipating that they will not be able to recall where they hid that birthday present several months into the future.
In the storytelling of the pilots and the mothers, experiences are being crystallized into expertise. Tricks of the trade are being discovered.
In trying to understand how people solve ill-defined problems, one strategy is to try to reach a goal while simultaneously trying to define the goal, using failures to specify the goal more clearly.
Julian Weitzenfeld (1984) determined that similarity does not make sense without purpose.4 If your purpose is to start a car with a dead battery, letting it roll downhill and pushing it on a level stretch of highway are similar actions.
If we can find an analogy with which we feel comfortable, we can use it because it reflects the full set of causal factors, even the ones we cannot yet identify. The analogy also reflects the interactions between causal factors, interactions we cannot specify, so it lets us make a prediction that reflects factors whose existence we do not know and whose properties we do not know. That is the power of analogical reasoning.
First, we learned that they do not select analogues just based on similarity. If you are buying a green car, you do not try to find the reliability records for green cars. You would select an analogue that shares the same dynamics, the same factors that affect what you are trying to predict. If you do not have enough experience to take causal factors into account, you can get into trouble. The engineers we studied were all knowledgeable.
Julian concluded that we select analogues on the “mysterious” features (the qualitative categories that we cannot easily adjust) and adjust on the more straightforward features. We would select an analogue airplane that flew the same type of mission and adjust on a size dimension; we would not select an analogue that matched on size and adjust on type of mission.
when we use an analogy, we are trying to set up a condition where the same causal factors are at work, even though we do not know what they are. The analogue is selected to match on the causal variables as closely as possible, and we adjust the data to take into account aspects where we know the matching is inadequate.
There is a delicate balance to using analogues. If we know a great amount, we do not need the analogues; we can just figure out the formulas. If we know very little, analogical reasoning may be as likely to get us into trouble as to help us. Analogical reasoning seems to help most when we are in between: we know something about the area but not enough for a satisfactory analysis.
If we can work with people who understand the culture, the task, and what we are trying to accomplish, then we can trust them to read our minds and fill in unspecified details. A team that has much experience working together can outperform a newly assembled team.
There are seven types of information that a person could present to help the people receiving the request to understand what to do: The purpose of the task (the higher-level goals). The objective of the task (an image of the desired outcome). The sequence of steps in the plan. The rationale for the plan. The key decisions that may have to be made. Antigoals (unwanted outcomes). Constraints and other considerations.
The art of describing your intent is to give as little information as you can. The more details you pack in, the more you obscure your main points. However, if you leave out an important consideration, you run the risk that the person will become confused at a critical decision point.
Experienced teams have integrated identities; the members identify themselves in relationship to the whole team. Inexperienced teams have fragmentary identities and focus on individual assignments more than team requirements.
The next time you are in a group or take part in a team discussion, consider the possibility that you may be seeing the way your own mind works from the inside. The chaos, the accidents, the inhibited thoughts, the chance connections, the serendipity: that is what is going on inside your own head.
Rational analysis is a specialized and powerful source of power that may play a limited role in many tasks, a dominant role in a few tasks, and sometimes no role at all.
When the calculations require people to estimate probabilities or utilities, to estimate their values or to make other unnatural judgments, we are going to have trouble. The experiential sources of power do not appear to be helpful in generating the estimates that would be used in analyses.
In our everyday lives, we do not face combinatorial explosions because we are not relying on calculations. We use experiential sources of power to frame situations and arrive at manageable representations. Then, if necessary, we bring in the analytical methods to add precision. The analytical methods run into limits when we try to use them without recourse to the experiential sources of power. The problem is not rationality but hyperrationality.
Once we see the error or the missed opportunity, we can trace it back to the beliefs that did not get connected. It is too much to expect that every piece of information in memory be continually matched against every other piece to catch these connections and draw their implications.
Rigor is not a substitute for imagination. Consistency is not a replacement for insight.
Poor outcomes are different from poor decisions. The best decision possible given the knowledge available can still turn out unhappily.
By way of analogy, when radar was introduced into commercial shipping, it was for the intent of improving safety so that ships could avoid collisions when visibility was poor. The actual impact was that ships increased their speed, and accident rates stayed constant. On the decision front, we expect to find the same thing. Planning cycles will be expedited, and the plans will be made with the same level of uncertainty as there was before.
If managers find themselves having success—getting projects completed on schedule and under budget—does that success stem from their own skills, the skills of their subordinates, temporary good luck, interventions of higher-level administrators, a blend of these factors, or some other causes altogether? There is no easy way to tell. We can learn the wrong lessons from experience. Each time we compile a story about an experience, we run the risk of getting it wrong and stamping in the wrong strategy.
Jim Shanteau (1992) has suggested that we will not build up real expertise when: The domain is dynamic. We have to predict human behavior. We have less chance for feedback. The task does not have enough repetition to build a sense of typicality. We have fewer trials. Under these conditions, we should be cautious about assuming that experience translates into expertise.
Expertise depends on perceptual skills. You rarely get someone to jump a skill level by teaching more facts and rules. Perhaps in a field such as mathematics, a teacher can move a student rapidly through some concepts. However, in natural settings, perceptual learning takes many cases to develop. Therefore, we cannot expect to grow instant experts by using powerful training methods. We can make training more efficient but cannot radically replace the accumulation of experiences.