Naive Allocation is spreading limited resources evenly across options regardless of merit. It sits in the Cognition dimension (COG) of the Human Behavior Taxonomy™ as element HBT-COG-0491, within the Decision family. The core principle: spreading limited resources evenly across options regardless of merit. 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
Spreading limited resources evenly across options regardless of merit.
Plain-English Definition
Spreading limited resources evenly across options regardless of merit.
Feynman Explanation
1/N for the brain that does not want to do math.
Core Principle
Spreading limited resources evenly across options regardless of merit.
Mechanisms
Pending editorial review.
Spreading limited resources evenly across options regardless of merit.
Pending editorial review.
Pending editorial review.
Pending editorial review.
Pending editorial review.
Portfolio and budget allocations skew toward the menu structure, not the optimum.
Inputs (Triggers)
Pending editorial review.
Outputs (Behaviors)
Pending editorial review.
Behavioral Signature
1/N for the brain that does not want to do math.
Examples
- 401(k) participants splitting equally across whichever funds are listed.
- Portfolio and budget allocations skew toward the menu structure, not the optimum.
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: spreading limited resources evenly across options regardless of merit. You can recognize it in the field by its signature: 1/N for the brain that does not want to do math. 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, portfolio and budget allocations skew toward the menu structure, not the optimum. It is amplified whenever portfolio and budget allocations skew toward the menu structure, not the optimum. Inside organizations that shows up as portfolio and budget allocations skew toward the menu structure, not the optimum. 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 force-rank options. Then allocate. Not the other way around. 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
- Force-rank options. Then allocate. Not the other way around.
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.
- Force-rank options. Then allocate. Not the other way around.
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.
How will I feel about this in 10 minutes / 10 months / 10 years?
Inattention or forgetfulness caused by low attention, hyperfocus, or distraction.
Forgetting to compare an offer with the next-best alternative.
Outcomes that could have happened but did not.
Overthinking a situation so that decision-making stalls.
The first number on the table silently sets the range for every number after it.
Tversky & Kahneman's classic: identical outcomes flip from 'risk averse' to 'risk seeking' when framed as lives saved vs. lives lost.
Our perception is shaped by what we selectively pay attention to.
Favoring suggestions from automated systems over conflicting human judgment.
A color appears different depending on adjacent colors.
Decisions are constrained by available information, cognitive limits, and time.
The ability to focus on one voice in a noisy environment.
Where Naive Allocation 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 Periodic Table of Human Behavior
The full 1,267-element map this page belongs to.
- ReferenceThe incentive glossary
Definitions for every mental model, bias, and fallacy in the corpus.
Questions about Naive Allocation
- What is Naive Allocation?
- Naive Allocation is spreading limited resources evenly across options regardless of merit. It sits in the Cognition dimension (COG) of the Human Behavior Taxonomy™ as element HBT-COG-0491, within the Decision family. The core principle: spreading limited resources evenly across options regardless of merit. 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 Naive Allocation?
- Portfolio and budget allocations skew toward the menu structure, not the optimum. The Incentives Lab catalogs everyday, organizational, and historical instances of this element on its Human Behavior Taxonomy™ page (HBT-COG-0491).
- How is Naive Allocation exploited?
- Portfolio and budget allocations skew toward the menu structure, not the optimum.
- How do you design around Naive Allocation?
- Force-rank options. Then allocate. Not the other way around.
- Which behavioral dimension does Naive Allocation belong to?
- Naive Allocation is classified in the Cognition dimension (COG) of the Human Behavior Taxonomy™, family "Decision", class "Mental Model". Its permanent identifier is HBT-COG-0491 and its evidence grade is B.