Sets and Prototypes
System 1 effortlessly computes averages but cannot easily compute sums, leading to a neglect of quantity in judgments. This is demonstrated by experiments on line lengths and the Exxon Valdez oil spill, where people's willingness to pay was insensitive to the number of birds saved.
THE IDEA
SETS AND PROTOTYPES
For another example, consider the question: What is the average length of the lines in figure 8?

Figure 8
This question is easy and System 1 answers it without prompting. Experiments have shown that a fraction of a second is sufficient for people to register the average length of an array of lines with considerable precision. Furthermore, the accuracy of these judgments is not impaired when the observer is cognitively busy with a memory task. They do not necessarily know how to describe the average in inches or centimeters, but they will be very accurate in adjusting the length of another line to match the average. System 2 is not needed to form an impression of the norm of length for an array. System 1 does it, automatically and effortlessly, just as it registers the color of the lines and the fact that they are not parallel. We also can form an immediate impression of the number of objects in an array—precisely if there are four or fewer objects, crudely if there are more.
Now to another question: What is the total length of the lines in figure 8? This is a different experience, because System 1 has no suggestions to offer. The only way you can answer this question is by activating System 2, which will laboriously estimate the average, estimate or count the lines, and multiply average length by the number of lines.
The failure of System 1 to compute the total length of a set of lines at a glance may look obvious to you; you never thought you could do it. It is in fact an instance of an important limitation of that system. Because System 1 represents categories by a prototype or a set of typical exemplars, it deals well with averages but poorly with sums. The size of the category, the number of instances it contains, tends to be ignored in judgments of what I will call sum-like variables.
Participants in one of the numerous experiments that were prompted by the litigation following the disastrous Exxon Valdez oil spill were asked their willingness to pay for nets to cover oil ponds in which migratory birds often drown. Different groups of participants stated their willingness to pay to save 2,000, 20,000, or 200,000 birds. If saving birds is an economic good it should be a sum-like variable: saving 200,000 birds should be worth much more than saving 2,000 birds. In fact, the average contributions of the three groups were $80, $78, and $88 respectively. The number of birds made very little difference. What the participants reacted to, in all three groups, was a prototype—the awful image of a helpless bird drowning, its feathers soaked in thick oil. The almost complete neglect of quantity in such emotional contexts has been confirmed many times.
When to use it
When evaluating proposals for disaster relief, consider the total number of affected individuals rather than relying solely on vivid stories.
In marketing, be aware that individual stories may resonate more than aggregate statistics, but also ensure the scale of the impact is communicated.
For policy-making, use quantitative data to counteract the natural tendency to focus on prototypes.
FROM THEORY TO EVERYDAY LIFE
In practice
Average vs. Total Length of Lines
When asked about the average length of lines in Figure 8, System 1 provides an accurate estimate effortlessly. However, when asked for the total length, System 2 must laboriously estimate or count and multiply. Averaging comes easily; sums require more work.
Exxon Valdez Bird Experiment
Participants were asked their willingness to pay for nets to save 2,000, 20,000, or 200,000 birds. The average contributions were $80, $78, and $88 respectively, showing that the number of birds made little difference. Participants reacted to the prototype of a single drowning bird, not the total number.
A MOMENT TO REFLECT
Put it to work.
How might the neglect of sums influence our decisions in environmental or humanitarian issues?
Can you identify a time when you focused on a prototype rather than the overall scale of a problem?
What strategies can help you avoid the sum neglect bias in data interpretation?
CONTINUE THE CONVERSATION
Source texts
- Thinking, Fast and Slow (Daniel Kahneman)