Decision-Making Strategies

How Time, Complexity, and Ambiguity Influence Which Method We Use

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You have to make decisions both large and small throughout every single day of your life. What do you want to have for breakfast? What time should you meet a friend for dinner? What college should you go to? How many children do you want to have?

When faced with some decisions, you might be tempted to just flip a coin and let chance determine your fate. In most cases, we follow a certain strategy or series of strategies in order to arrive at a decision.

For many of the relatively minor decisions that we make each and every day, flipping a coin wouldn't be such a terrible approach. For some of the complex and important decisions, we are more likely to invest a lot of time, research, effort, and mental energy into coming to the right conclusion.

So how exactly does this process work? The following are some of the major decision-making strategies that you might use:

The Single-Feature Model

This approach involves hinging your decision solely on a single-feature. For example, imagine that you are buying soap. Faced with a wide variety of options at your local superstore, you decide to base your decision on price and buy the cheapest type of soap available. In this case, you ignored other variables (such as scent, brand, reputation, and effectiveness) and focused on just a single feature.

The single-feature approach can be effective in situations where the decision is relatively simple and you are pressed for time.

However, it is generally not the best strategy when dealing with more complex decisions.

The Additive Feature Model

This method involves taking into account all the important features of the possible choices and then systematically evaluating each option. This approach tends to be a better method when making more complex decisions.

For example, imagine that you are interested in buying a new camera. You create a list of important features that you want the camera to have, then you rate each possible option on a scale of -5 to +5. Cameras that have important advantages might get a +5 rating for that factor, while those that have major drawbacks might get a -5 rating for that factor. Once you have looked at each option, you can then tally up the results to determine which option has the highest rating.

The additive feature model can be a great way to determine the best option among a variety of choices. As you can imagine, however, it can be quite time consuming and is probably not the best decision-making strategy to use if you are pressed for time.

The Elimination by Aspects Model

The elimination by aspects model was first proposed by psychologist Amos Tversky in 1972. In this approach, you evaluate each option one characteristic at a time beginning with whatever feature you believe is the most important. When an item fails to meet the criteria you have established, you cross the item off your list of options.

Your list of possible choices gets smaller and smaller as you cross items off the list until you eventually arrive at just one alternative.

Making Decisions in the Face of Uncertainty

The previous three processes are often used in cases where decisions are pretty straightforward, but what happens when there is a certain amount of risk, ambiguity, or uncertainty involved? For example, imagine that you are running late to your psychology class. Should you drive above the speed limit in order to get there on time, but risk getting a speeding ticket? Or should you drive the speed limit, risk being late, and possibly get docked points for missing a scheduled pop quiz? In this case, you have to weigh the possibility that you might be late for your appointment against the probability that you will get a speeding ticket.

When making a decision in such a situation, people tend to employ two different decision-making strategies: the availability heuristic and the representativeness heuristic. Remember, a heuristic is a rule-of-thumb mental short-cut that allows people to make decisions and judgments quickly.

  • The Availability Heuristic: When we are trying to determine how likely something is, we often base such estimates on how easily we can remember similar events happening in the past. For example, if you are trying to determine if you should drive over the speed limit and risk getting a ticket, you might think of how many times you have seen people getting pulled over by a police officer on a particular stretch of highway. If you cannot immediately think of any examples, you might decide to go ahead and take a chance, since the availability heuristic has led to you judge that few people get pulled over for speeding on your particular route. If you can think of numerous examples of people getting pulled over, you might decide to just play it safe and drive the suggested speed limit.
  • The Representativeness Heuristic: This mental shortcut involves comparing our current situation to our prototype of a particular event or behavior. For example, when trying to determine whether you should speed to get to your class on time, you might compare yourself to your image a person who is most likely to get a speeding ticket. If your prototype is that of a careless teen that drives a hot-rod car and you are a young businesswoman who drives a sedan, you might estimate that the probability of getting a speeding ticket is quite low.

The decision-making process can be both simple (such as randomly picking out of our available options) or complex (such as systematically rating different aspects of the existing choices). The strategy we use depends on various factors, including how much time we have to make the decision, the overall complexity of the decision, and the amount of ambiguity that is involved.

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Hockenbury, D. H. & Hockenbury, S. E. (2006). Psychology. New York: Worth Publishers.

Tversky, A. (1972). Elimination by aspects: A theory of choice. Psychological Review, 80, 281-299.

Tversky, A., & Kahneman, D. (1982). Judgment under uncertainty: Heuristics and biases. In Daniel Kahneman, Paul Slovic, & Amos Tversky (Eds.). Judgment under uncertainty: Heuristics and biases. New York: Cambridge University Press.

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