Wisdom of Crowds is aggregated independent judgments can be more accurate than individual experts. It sits in the Cognition dimension (COG) of the Human Behavior Taxonomy™ as element HBT-COG-0703, within the Social family. The core principle: aggregated independent judgments can be more accurate than individual experts. In incentive terms, it matters because it changes the payoff people perceive before they choose — which means it can be designed for, or exploited.
Scientific Definition
Aggregated independent judgments can be more accurate than individual experts.
Plain-English Definition
Aggregated independent judgments can be more accurate than individual experts.
Feynman Explanation
A thousand guesses average closer to the truth than one expert guess.
Core Principle
Aggregated independent judgments can be more accurate than individual experts.
Mechanisms
Pending editorial review.
Aggregated independent judgments can be more accurate than individual experts.
Pending editorial review.
Pending editorial review.
Pending editorial review.
Pending editorial review.
Diverse, independent opinions improve forecasting.
Inputs (Triggers)
Pending editorial review.
Outputs (Behaviors)
Pending editorial review.
Behavioral Signature
A thousand guesses average closer to the truth than one expert guess.
Examples
- Prediction markets forecast elections better than pundits.
- Diverse, independent opinions improve forecasting.
Pending editorial review.
Original analysis from The Incentives Lab — how this element behaves inside real payoff structures.
Why this element matters to incentive design
This element is common enough to feel like human nature and specific enough to be engineered around. The mechanism underneath it is straightforward: aggregated independent judgments can be more accurate than individual experts. You can recognize it in the field by its signature: a thousand guesses average closer to the truth than one expert guess. Every element in the Cognition dimension changes the perceived payoff of an action before the action happens, which is exactly where incentive design has leverage.
How it gets exploited
Left undesigned, diverse, independent opinions improve forecasting. It is amplified whenever diverse, independent opinions improve forecasting. Inside organizations that shows up as diverse, independent opinions improve forecasting. The pattern is the same one Goodhart's Law describes: the measurable proxy attracts the effort, and the purpose behind it quietly loses funding.
How the Lab designs around it
The redesign move is to aggregate judgments before group discussion to avoid herding. Measure the behavior, not the sentiment. A survey will tell you how people feel about this; only observed action tells you whether it changed.
Famous Experiments
Pending editorial review.
Design Principles
- Aggregate judgments before group discussion to avoid herding.
Measurement Approaches
Pending editorial review.
Evidence
Pending editorial review.
Pending editorial review.
The Perverse Incentive Lens™
How this behavior is exploited — and how to redesign around it.
- Aggregate judgments before group discussion to avoid herding.
Pending editorial review.
Pending editorial review.
Interactive Mini Network
Click any neighbor to re-center the graph and follow the threads of connection.
Knowledge Graph Neighbors
Auto-linked to the rest of the Human Behavior Taxonomy by family, domain, dimension, and shared keywords.
A group decides on a course of action that nobody actually wants, because everyone assumes others prefer it.
Power tends to corrupt, and absolute power corrupts absolutely.
We attribute our own actions to situations but others' actions to their character.
We favor people who are similar to us or who like us.
Sacrificing for others without expecting a personal reward.
Masking the sponsors of a message to make it appear grassroots.
Disagreement grows more extreme as the parties think more about the issue.
Systematic errors in explaining the causes of behavior — yours or others'.
We accept vague, general statements as personally meaningful.
Adding the word 'because' (with any reason) increases compliance dramatically.
Behaviors spread through groups via observation and imitation.
We come to like people we've done a favor for, more than people who've helped us.
Where Wisdom of Crowds is cited in the corpus
Essays, field guides, and diagnostics from The Incentives Lab that apply this element.
- EssayGoodhart's Law in the Real World
How measurable proxies capture judgment.
- EssayThe Perverse Incentives Hiding in Your KPIs
Cognitive shortcuts turned into scorecards.
- EssayAI Agents Inherit Your Incentives
How this element propagates into automated systems.
- ReferenceThe incentive glossary
Definitions for every mental model, bias, and fallacy in the corpus.
- CourseIncentives 101
The free ten-part primer on reading a payoff structure.
- ReferenceThe Periodic Table of Human Behavior
The full 1,267-element map this page belongs to.
Questions about Wisdom of Crowds
- What is Wisdom of Crowds?
- Wisdom of Crowds is aggregated independent judgments can be more accurate than individual experts. It sits in the Cognition dimension (COG) of the Human Behavior Taxonomy™ as element HBT-COG-0703, within the Social family. The core principle: aggregated independent judgments can be more accurate than individual experts. In incentive terms, it matters because it changes the payoff people perceive before they choose — which means it can be designed for, or exploited.
- What is an example of Wisdom of Crowds?
- Diverse, independent opinions improve forecasting. The Incentives Lab catalogs everyday, organizational, and historical instances of this element on its Human Behavior Taxonomy™ page (HBT-COG-0703).
- How is Wisdom of Crowds exploited?
- Diverse, independent opinions improve forecasting.
- How do you design around Wisdom of Crowds?
- Aggregate judgments before group discussion to avoid herding.
- Which behavioral dimension does Wisdom of Crowds belong to?
- Wisdom of Crowds is classified in the Cognition dimension (COG) of the Human Behavior Taxonomy™, family "Social", class "Mental Model". Its permanent identifier is HBT-COG-0703 and its evidence grade is B.