Incentives

Also called incentive-caused bias · Economics & psychology

Incentives are the rewards and punishments that drive behaviour. To predict what people will do, look not at what they say or intend but at what they are actually rewarded for. As Munger put it: "show me the incentive and I will show you the outcome."

Foundational — cross-referenced 25× across this reference (17 related ideas · 5 hubs · 3 books) · The State of Thinking 2026 →

By the ReadGlobe Editors · Reviewed 2026-05-29

How it works

Whenever you want to explain or predict behaviour — your own or an organisation’s — map the real rewards and penalties at play, including hidden and perverse ones. People rationalise, but they respond to incentives, often unconsciously, and the structure usually beats the intention.


Show me the incentive and I'll show you the outcome.

How to use it


  • Diagnosing why a team or company behaves "irrationally" — usually it’s responding rationally to its incentives.
  • Designing systems: align what you reward with the outcome you actually want.
  • Spotting perverse incentives, where a well-meant reward produces the opposite of its goal.

Worked example

A call centre rewarded for calls-per-hour will rush customers and resolve nothing, driving repeat calls — the metric is hit while the goal (served, happy customers) is missed. The incentive, not the staff, produced the failure.

Where it fails

Over-applied, it slides into cynicism — treating every action as pure self-interest and missing the genuine values, norms, and altruism that also shape behaviour.

  • Explicit rewards can crowd out intrinsic motivation — paying for behavior that was previously done from duty or pride sometimes reduces it.
  • People respond to incentives as they perceive them, filtered through identity and fairness norms — the designed incentive and the experienced one routinely diverge.
  • Incentive analysis reads the current payoff structure but not taste for it; the same bonus scheme produces different behavior across cultures, roles, and individuals.

The counter-model: Goodhart's lawIncentive design assumes rewarding a measure produces the behavior you want; Goodhart's law shows the measure degrades once rewarded — every incentive scheme must anticipate its own gaming.

How to apply it, step by step


  1. For a behavior you want to predict or change, list what each party is actually rewarded and punished for — money, status, safety, convenience.
  2. Ignore stated intentions where they conflict with the payoff structure.
  3. Identify the gap between what you want and what the current structure pays for.
  4. Redesign one reward or penalty to close that gap.
  5. Watch the first weeks for gaming — behavior that satisfies the letter of the incentive while defeating its purpose.

The deeper point

It is the most reliable predictor of behaviour we have, precisely because it bypasses what people believe about themselves. Almost everyone thinks they’re above their incentives — which is exactly why the incentives win.

Frequently asked


What does "show me the incentive" mean?
It’s Charlie Munger’s shorthand for incentive-caused bias: to predict behaviour, look at what people are rewarded for, not what they claim to intend. Incentives shape outcomes more reliably than stated goals.
What is a perverse incentive?
A reward that unintentionally encourages the opposite of its goal — like paying for dead rats to cut a rat problem, which leads people to breed rats. Behaviour follows the reward, not the intent.
How do you use the incentives model?
When behaviour seems irrational, map the real rewards and penalties, including hidden ones. To change behaviour, change the incentives so the reward aligns with the outcome you actually want.

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APA

ReadGlobe. (2026). Incentives. https://readglobe.com/model/incentives/

MLA

"Incentives." ReadGlobe, 29 May 2026, readglobe.com/model/incentives/.

Primary source: Wikipedia

Editorial synthesis © ReadGlobe 2026, drawing on the mental-models tradition (Charlie Munger, Farnam Street) and the primary sources for each model. · Last reviewed 2026-05-29.