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Computer Science & Algorithms

Average Rule

Model #0660Category: Computer Science & AlgorithmsDepth to apply:

By Updated 3 sources

4 min read

Contents

  1. 1. Core Idea
  2. 2. How to See It
  3. 3. How to Use It
  4. 4. Founders & Leaders
  5. 5. Connected Models
  6. 6. One Key Quote
  7. 7. Summary & Further Reading
Computer Science & Algorithms
Section 1

Core Idea

When multiple independent estimates or signals bear on the same quantity, combining them (e.g. by averaging) often beats relying on one. The average rule is a decision heuristic: use the mean of available estimates to reduce noise and bias from any single source. It appears in forecasting, ensemble methods, and simple aggregation of expert or model outputs.

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Section 2

How to See It

Forecasting
You're seeing Average Rule when a forecast is built from the mean (or median) of several models or experts instead of picking one. Errors tend to cancel; the aggregate is usually more stable than the best single predictor.
Decisions
You're seeing Average Rule when a team explicitly averages independent votes, scores, or estimates (e.g. on priority, risk, or impact) before committing, rather than letting one voice or one number dominate.
Section 3

How to Use It

When you have multiple independent estimates of the same thing, average them unless you have a strong reason to weight one more. Ensure independence: same source repeated does not count. Use median if outliers are likely.
Decision filter
"Do we have more than one estimate or signal for this? If yes, have we combined them (e.g. average) instead of cherry-picking one? If we're using one number, is that deliberate or default?"
As a founder
Use the average rule for roadmap prioritization (average scores across stakeholders), for deal or market size (average several methods), and for risk (average independent assessments). Avoid letting the loudest or most confident estimate stand alone.
Section 5

Founders & Leaders

Jim SimonsFounder, Renaissance Technologies
Renaissance uses massive ensembles of models and signals; aggregation and averaging over many independent predictors is central. Simons' edge came partly from combining many weak signals rather than betting on a few. Founders can apply the same principle at smaller scale: average independent estimates and votes instead of relying on a single forecast or voice.
Section 7

Connected Models

Reinforces
Law of Large Numbers
Averaging many independent observations converges to the true mean as noise cancels. The average rule is the practical application: combine estimates so that uncorrelated errors shrink.
Tension
Regression to the Mean
Regression to the mean says extreme outcomes tend to be followed by less extreme ones. The tension: averaging pulls toward the mean, which can hide real shifts. Use the average rule for estimation, but watch for when the process itself has changed.
Leads-to
Wisdom of Crowd
Wisdom of the crowd is the average rule with a crowd: under the right conditions, the average of many independent guesses beats experts. Same principle — independence and aggregation — at larger scale.
Section 8

One Key Quote

"The combination of multiple imperfect estimates is often more accurate than the best single estimate."
— Daniel Kahneman, Thinking, Fast and Slow (2011)
Section 11

Summary & Further Reading

The average rule: combine multiple independent estimates (e.g. by averaging) to reduce noise and improve decisions. Use it for forecasts, prioritization, and risk — and ensure estimates are genuinely independent.
01
Thinking, Fast and Slow — Daniel Kahneman (2011)
Book
Covers aggregation of judgments, noise, and when averaging beats single estimates.
02
The Wisdom of Crowds — James Surowiecki (2004)
Book
When and why the average of many independent guesses outperforms experts.
03
Superforecasting — Philip Tetlock (2015)
Book
How combining forecasts and updating improves accuracy; practical average-rule application.

Why this matters next

mental modelsAverage Rule

Average Rule applied the Average Rule mental model

mental modelsRegression to the Mean

Average Rule applied the Regression to the Mean mental model

mental modelsScale

Average Rule applied the Scale mental model

mental modelsAlgorithms

Average Rule applied the Algorithms mental model

mental modelsWisdom of Crowd

Average Rule applied the Wisdom of Crowd mental model

mental modelsSignal vs Noise

Average Rule applied the Signal vs Noise mental model

Frequently asked questions

What is Average Rule?

Average Rule is a mental model used for better thinking and decision-making.

How do you apply Average Rule?

To apply Average Rule, identify situations where this framework is relevant, then use it as a lens to evaluate your options and decisions. The model is most useful when combined with other complementary mental models.

What category does Average Rule fall under?

Average Rule falls under the Computer Science & Algorithms category of mental models. Other models in this category can be found on the Computer Science & Algorithms hub page.

Why is Average Rule important?

Average Rule is important because it provides a structured way to think about problems that would otherwise be approached with intuition alone. Understanding this model helps you avoid common reasoning errors and make better decisions.

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