Satisficing is a neologism that combined satisfy and suffice. It’s a complicated word that describes a commonsense idea: good enough.
Our processes of decision-making just like processes of perception or memory assume that we make decisions in a structured world. That’s what they evolved for: decisions in our natural world. But many of our decisions don’t have the same sort of structure: the stock market, for example, is neither stable nor predictable and the very processes that help us make decisions in the natural world may hurt us when we make investments.
So now let’s turn to the specific case of bounded rationality. This concept grew in large part out of the work of Herbert Simon. And it is hard to characterize Herbert Simon’s work, he was clearly an economist at least he was recognized with the Nobel Prize in economics, but he was trained as a political scientist and he made major contributions to psychology, computer science and many other fields.
Throughout his career Simon continually question the assumptions of rational choice models and he sought not merely to reject those models, but create something new in their place. In 1955 Simon set forth his ideas in a paper called “A Behavioral Model of Rational Choice”. Notice that word: behavioral. Simon uses it in much the same sense well before behavioral economics becomes a field of its own.
Limited search and satisficing
Let’s look at the two main features of Simon’s bounded rationality: limited search and satisficing. Within the real-world example of a familiar and complex decision, buying a car, if you decided to buy a car today how many options would you have? 10, 100, 1000, probably more. In the US market alone there are several dozen manufacturers, each with many models in each of those models has a bewildering array optional features for you to choose from.
You can’t possibly evaluate every possible car against every other car. You have neither the time nor the energy to do so. So how can you make a good decision? Let’s think about the search process. When decision scientists or psychologists talk about search they mean exploring the set of options, we need some process for narrowing those overwhelming options down to a manageable set.
So instead of considering as much information as possible, we try to limit our search, we focus on a particular category sports cars or desired feature fuel economy or a preferred manufacturer. Doing so might limit our options down to only a handful of cars. Now suppose you identify three models that seem like reasonable options. How do you pick one?
A satisficing choice
A truly rational decision maker would approach the problem much like Benjamin Franklin, listing every advantage and disadvantage and determining how this factors trade off against each other. Simon recognize that this approach was mathematically complex. It’s time-consuming and it’s likely to emphasize minor factors at the expense of what’s really important so he proposed an alternative approach which he called: satisficing.
Satisficing is a neologism that combined satisfy and suffice. It’s a complicated word that describes a commonsense idea: good enough. Suppose that you are trying to decide between three car models and there are several features that are most important to you, say performance, reliability in fuel-efficient. For each feature that you are evaluating Simon argued you set up some aspiration level and you judge each car model based on whether it’s feature is better or worse the your aspiration.
So for fuel efficiency you might judge that more than 30 miles per gallon is good enough, for performance your aspiration level could be more subjective. When you evaluate your three models you find out that one satisfies your aspirations, it is good enough on every factor fuel-efficient fund drive and reliable and so you choose it and you drive off the lot excited about your new purchase.
Not optimal, but good enough
Now this might’ve seemed like a perfectly reasonable way to purchase a car: limit your search to a few models and then find something that is good enough on each feature, but it’s reasonable, it didn’t require any complex computations isn’t however optimal. You can make mistakes. A limited search process will most of the time leisure to a set of good options, but it can miss the best option.
Your search might be unnecessarily constrained by your own biases or by your limited knowledge. If you are looking only at sports cars you might never consider a car classified as a high performance sedan, even if the car would otherwise be perfect for you and satisficing he has a critical limitation: using an aspiration level throws away potentially important information.
Let’s suppose for the moment that you evaluated cars based on only two factors: gas mileage and reliability. For gas mileage or aspiration level was 30 miles per gallon and for liability your aspiration level was one of the top 10 most reliable cars as listed in the major consumer magazine. You look at two cars A and B. A meets both criteria while B only meets the reliability criteria so A is better right?! Not necessarily.
Suppose car A gets 31 miles per gallon and is the 10th most reliable, it’s clearly a good enough option given your criteria, but car B gets 28 miles per gallon and is the single most reliable car. Now the difference between 31 miles per gallon and 28 miles per gallon probably isn’t a big deal. In fact, it’s much less important than it might seem, but the difference between the first and 10th most reliable car probably does matter.