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HBT-INC-0170 · Dimension INC · Incentives

Like and Follower Counts

Public metrics tie self-worth to engagement, training behavior toward what performs.

Social Perverse Pattern·Perverse Incentive·Grade C·draft· enriching…
In one paragraph

Like and Follower Counts is public metrics tie self-worth to engagement, training behavior toward what performs. It sits in the Incentives dimension (INC) of the Human Behavior Taxonomy™ as element HBT-INC-0170, within the Social Perverse Pattern family. The core principle: public metrics tie self-worth to engagement, training behavior toward what performs. 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

Public metrics tie self-worth to engagement, training behavior toward what performs.

Plain-English Definition

Public metrics tie self-worth to engagement, training behavior toward what performs.

Feynman Explanation

Your self-image now lives in a chart.

Core Principle

Public metrics tie self-worth to engagement, training behavior toward what performs.

Mechanisms

Psychological

Pending editorial review.

Behavioral Economic

Public metrics tie self-worth to engagement, training behavior toward what performs.

Neurological

Pending editorial review.

Evolutionary

Pending editorial review.

Sociological

Default visibility of metrics shapes user identity.

Computational

Pending editorial review.

Systems

Pending editorial review.

Inputs (Triggers)

Pending editorial review.

Outputs (Behaviors)

Pending editorial review.

Behavioral Signature

Your self-image now lives in a chart.

Examples

Everyday
  • Documented mental-health impacts of public counts.
Modern (Organizational)
  • Default visibility of metrics shapes user identity.
Historical

Pending editorial review.

Lab Commentary

Original analysis from The Incentives Lab — how this element behaves inside real payoff structures.

Why this element matters to incentive design

Executives usually notice this element only after it has cost something. By then it looks like a one-off. It is not. The mechanism underneath it is straightforward: public metrics tie self-worth to engagement, training behavior toward what performs. You can recognize it in the field by its signature: your self-image now lives in a chart. Every element in the Incentives 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, default visibility of metrics shapes user identity. It is amplified whenever default visibility of metrics shapes user identity. Inside organizations that shows up as default visibility of metrics shapes user identity. 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 hide counts by default. Private engagement modes. Watch for it at the boundaries: handoffs, promotions, incident reviews, and budget cycles are where this element gets its power.

Famous Experiments

Pending editorial review.

Design Principles

  • Hide counts by default. Private engagement modes.

Measurement Approaches

Pending editorial review.

Evidence

Evidence Grade
C (A strongest → E speculative)
Replication
★★★☆☆
Intervention Confidence
4 / 5
Consensus
Pending editorial review (HBT v1.0 auto-seed).
Limitations
Pending editorial review (HBT v1.0 auto-seed).
Open Research Questions

Pending editorial review.

Primary References

Pending editorial review.

Signature Section

The Perverse Incentive Lens™

How this behavior is exploited — and how to redesign around it.

Exploitation
Default visibility of metrics shapes user identity.
Amplifying Incentives
Default visibility of metrics shapes user identity.
Org Failure Modes
Default visibility of metrics shapes user identity.
Societal Failure Modes
Pending editorial review (HBT v1.0 auto-seed).
Ethical Considerations
Pending editorial review (HBT v1.0 auto-seed).
Redesign Strategies
Hide counts by default. Private engagement modes.
Diagnostic Questions
  • Hide counts by default. Private engagement modes.
Warning Signs

Pending editorial review.

Red Flags

Pending editorial review.

Intervention Playbook
Individual
Hide counts by default. Private engagement modes.
Team
Pending editorial review (HBT v1.0 auto-seed).
Organization
Pending editorial review (HBT v1.0 auto-seed).
Policy
Pending editorial review (HBT v1.0 auto-seed).
AI Implications
Detection
Pending editorial review (HBT v1.0 auto-seed).
Measurement
Pending editorial review (HBT v1.0 auto-seed).
Mitigation
Pending editorial review (HBT v1.0 auto-seed).
Responsible Use
Pending editorial review (HBT v1.0 auto-seed).

Interactive Mini Network

Click any neighbor to re-center the graph and follow the threads of connection.

HBT-INC-0170 · INC
Like and Follower Counts
LABABusyness as StatusCDCancel DynamicsCACelebrity Attention …CCComparison CultureDADating App Gamificat…HSHUD Section 8 Concen…HCHustle Culture Glori…IPIdentity-Coded Polit…NNNews Negativity BiasPCProductivity-Hack Cu…

Knowledge Graph Neighbors

Auto-linked to the rest of the Human Behavior Taxonomy by family, domain, dimension, and shared keywords.

Where Like and Follower Counts is cited in the corpus

Questions about Like and Follower Counts

What is Like and Follower Counts?
Like and Follower Counts is public metrics tie self-worth to engagement, training behavior toward what performs. It sits in the Incentives dimension (INC) of the Human Behavior Taxonomy™ as element HBT-INC-0170, within the Social Perverse Pattern family. The core principle: public metrics tie self-worth to engagement, training behavior toward what performs. 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 Like and Follower Counts?
Default visibility of metrics shapes user identity. The Incentives Lab catalogs everyday, organizational, and historical instances of this element on its Human Behavior Taxonomy™ page (HBT-INC-0170).
How is Like and Follower Counts exploited?
Default visibility of metrics shapes user identity.
How do you design around Like and Follower Counts?
Hide counts by default. Private engagement modes.
Which behavioral dimension does Like and Follower Counts belong to?
Like and Follower Counts is classified in the Incentives dimension (INC) of the Human Behavior Taxonomy™, family "Social Perverse Pattern", class "Perverse Incentive". Its permanent identifier is HBT-INC-0170 and its evidence grade is C.

Version History

v1.1.0 · 2026-06-28Initial auto-seed from corpus.