Dawes' Rule
Dawes’ Rule gives cues equal weight: it counts favorable binary cues and subtracts unfavorable ones. The calculation is simple, but it still requires examining every cue. Its usefulness depends on the accuracy gained for that effort.
THE IDEA
Equal Weights and the Cost of Using Every Cue
Some psychologists propose multiple linear regression as a description of human judgment; others argue that it is too complex a model for humans to instinctively perform. Nevertheless, both camps often regard it as an approximation of the optimal strategy people should use, Bayesian models aside. A more psychologically plausible version of a linear strategy employs unit weights (rather than beta weights), as suggested by Robyn Dawes (e.g., 1979). This heuristic adds up the number of unitary (1) cue values and subtracts the number of zero (0) cue values. Thus, it is fast (it does not involve much computation), but not frugal (it looks up all cues). For simplicity, we call this heuristic Dawes’ Rule.
The source compares this rule with other heuristics and standard linear models, using city homelessness rates as an example and reporting results across 20 empirical data sets. The question is how much accuracy a method gains for the information and computation it requires. A heuristic that examines twice as many cues may be only slightly more accurate. Those comparative results are not included in this extract.
When to use it
Quickly evaluating job candidates by tallying binary qualifications.
Deciding which product to buy by summing up pros and cons as binary cues.
Assessing risk in simple binary scenarios, like predicting loan defaults based on a few indicators.
Prioritizing tasks by counting positive cues such as urgency, impact, and feasibility.
FROM THEORY TO EVERYDAY LIFE
In practice
Predicting City Homelessness
Suppose you need to predict which of two U.S. cities has a higher homelessness rate using binary cues (1 = evidence for a higher rate, 0 = evidence against). For this illustration, assume every cue has been coded in that direction. City A has cues: unemployment=1, poverty=1, housing cost=0, social services=0. Sum of 1s = 2, sum of 0s = 2, net = 0. City B has cues: unemployment=0, poverty=1, housing cost=1, social services=1. Sum of 1s = 3, sum of 0s = 1, net = 2. Dawes' Rule predicts City B has higher homelessness.
College Admission Decisions
A simple admissions committee uses binary indicators: high GPA (1), high test scores (1), strong recommendations (1), extracurriculars (1). With the same indicators assessed for each applicant, counting positive indicators produces the same ranking as subtracting absent ones as well. Equal weights simplify the calculation but ignore possible differences in importance.
A MOMENT TO REFLECT
Put it to work.
Think of a personal decision where you considered multiple factors. How would using Dawes' Rule (adding up binary pros and cons) change your process?
In what situations might using unit weights be misleading compared to weighted models?
How could you measure the accuracy–effort trade-off when using Dawes' Rule in a real-world context?
Often confused with
References
- The robust beauty of improper linear models — Robyn Dawes (1979)
CONTINUE THE CONVERSATION
Source texts
- Heuristics and Biases The Psychology of Intuitive Judgment (Thomas Gilovich, Dale Griffin, Daniel Kahneman)