Expected Value is probability × payoff, summed across outcomes. It sits in the Identity dimension (IDN) of the Human Behavior Taxonomy™ as element HBT-IDN-0011, within the Probability family. The core principle: probability × payoff, summed across outcomes. 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
Probability × payoff, summed across outcomes.
Plain-English Definition
Probability × payoff, summed across outcomes.
Feynman Explanation
Outcomes are noise. Decisions are signal.
Core Principle
Probability × payoff, summed across outcomes.
Mechanisms
Pending editorial review.
Probability × payoff, summed across outcomes.
Pending editorial review.
Pending editorial review.
Pending editorial review.
Pending editorial review.
Make decisions by expected value; judge them by process, not outcome.
Inputs (Triggers)
Pending editorial review.
Outputs (Behaviors)
Pending editorial review.
Behavioral Signature
Outcomes are noise. Decisions are signal.
Examples
- A 10% chance of winning $1M is worth $100K in expectation.
- Make decisions by expected value; judge them by process, not outcome.
Pending editorial review.
Original analysis from The Incentives Lab — how this element behaves inside real payoff structures.
Why this element matters to incentive design
When this element shows up in a diagnostic, the instinct is to train people out of it. Training rarely moves it. The mechanism underneath it is straightforward: probability × payoff, summed across outcomes. You can recognize it in the field by its signature: outcomes are noise. Decisions are signal. Every element in the Identity 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, make decisions by expected value; judge them by process, not outcome. It is amplified whenever make decisions by expected value; judge them by process, not outcome. Inside organizations that shows up as make decisions by expected value; judge them by process, not outcome. 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 document EV thinking on big decisions. Re-read after the outcome. Design against it the way you would design against a known failure mode — assume it will appear, and price the exploit before someone finds it.
Famous Experiments
Pending editorial review.
Design Principles
- Document EV thinking on big decisions. Re-read after the outcome.
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.
- Document EV thinking on big decisions. Re-read after the outcome.
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.
Decisions optimize utility, not value.
Evaluating an argument's logic based on whether you agree with the conclusion.
Decisions are shaped by who we believe we are — not only by monetary payoffs.
Assuming something is true because it is probable or possible.
Ignoring general statistics in favor of specific, vivid information.
Posterior = (likelihood × prior) / evidence.
Update beliefs in proportion to the strength of new evidence.
Start with a prior; update with new evidence.
Novices experiencing early success, often due to variance and small samples.
High-impact, hard-to-predict, retrospectively explainable events.
Striking pattern that is statistically expected in large samples.
Reasoning in distributions — ranges and probabilities — rather than points.
Where Expected Value is cited in the corpus
Essays, field guides, and diagnostics from The Incentives Lab that apply this element.
- EssayCulture Is the Residue of Incentives
Identity as an outcome of what gets rewarded.
- EssayIncentive Intelligence
The six dimensions of reading a payoff structure.
- EssayAI Agents Inherit Your Incentives
How this element propagates into automated systems.
- EssayThe Comp Plan Is the Strategy
Where this element meets compensation design.
- ReferenceThe incentive glossary
Definitions for every mental model, bias, and fallacy in the corpus.
- ReferenceThe Periodic Table of Human Behavior
The full 1,267-element map this page belongs to.
Questions about Expected Value
- What is Expected Value?
- Expected Value is probability × payoff, summed across outcomes. It sits in the Identity dimension (IDN) of the Human Behavior Taxonomy™ as element HBT-IDN-0011, within the Probability family. The core principle: probability × payoff, summed across outcomes. 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 Expected Value?
- Make decisions by expected value; judge them by process, not outcome. The Incentives Lab catalogs everyday, organizational, and historical instances of this element on its Human Behavior Taxonomy™ page (HBT-IDN-0011).
- How is Expected Value exploited?
- Make decisions by expected value; judge them by process, not outcome.
- How do you design around Expected Value?
- Document EV thinking on big decisions. Re-read after the outcome.
- Which behavioral dimension does Expected Value belong to?
- Expected Value is classified in the Identity dimension (IDN) of the Human Behavior Taxonomy™, family "Probability", class "Mental Model". Its permanent identifier is HBT-IDN-0011 and its evidence grade is B.