Additive Bias is we solve problems by adding, even when subtracting would be better. It sits in the Cognition dimension (COG) of the Human Behavior Taxonomy™ as element HBT-COG-0014, within the Reasoning family. The core principle: we solve problems by adding, even when subtracting would be better. 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
We solve problems by adding, even when subtracting would be better.
Plain-English Definition
We solve problems by adding, even when subtracting would be better.
Feynman Explanation
Every problem looks like a missing feature.
Core Principle
We solve problems by adding, even when subtracting would be better.
Mechanisms
Pending editorial review.
We solve problems by adding, even when subtracting would be better.
Pending editorial review.
Pending editorial review.
Pending editorial review.
Pending editorial review.
Organizations grow complexity because subtraction is politically harder.
Inputs (Triggers)
Pending editorial review.
Outputs (Behaviors)
Pending editorial review.
Behavioral Signature
Every problem looks like a missing feature.
Examples
- Adding another meeting instead of canceling a redundant one.
- Organizations grow complexity because subtraction is politically harder.
Pending editorial review.
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: we solve problems by adding, even when subtracting would be better. You can recognize it in the field by its signature: every problem looks like a missing feature. 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, organizations grow complexity because subtraction is politically harder. It is amplified whenever organizations grow complexity because subtraction is politically harder. Inside organizations that shows up as organizations grow complexity because subtraction is politically harder. 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 require a 'what can we remove?' question before approving any addition. 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
- Require a 'what can we remove?' question before approving any addition.
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.
- Require a 'what can we remove?' question before approving any addition.
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.
The brain evolved to reason adaptively, not always truthfully, to reduce the cost of errors.
Researchers favor conclusions aligned with their school, team, or sponsor.
Relying on examples that come to mind easily, not on actual frequency.
Systematic deviations from rationality in judgment.
Treating multiple data points as independent when they came from one source.
Testing hypotheses only by looking for confirming evidence.
Applying higher scrutiny to evidence we disagree with than to evidence we agree with.
Over-weighting one's own perspective when reconstructing events.
Mind latches onto the first plausible explanation and resists alternatives.
Research outcomes tend to favor the interests of the funders.
Deriving general rules from specific examples; the leap from instance to concept.
Assuming that if one option is true, another must be false, when both can be true.
Where Additive Bias 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.
- 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 Additive Bias
- What is Additive Bias?
- Additive Bias is we solve problems by adding, even when subtracting would be better. It sits in the Cognition dimension (COG) of the Human Behavior Taxonomy™ as element HBT-COG-0014, within the Reasoning family. The core principle: we solve problems by adding, even when subtracting would be better. 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 Additive Bias?
- Organizations grow complexity because subtraction is politically harder. The Incentives Lab catalogs everyday, organizational, and historical instances of this element on its Human Behavior Taxonomy™ page (HBT-COG-0014).
- How is Additive Bias exploited?
- Organizations grow complexity because subtraction is politically harder.
- How do you design around Additive Bias?
- Require a 'what can we remove?' question before approving any addition.
- Which behavioral dimension does Additive Bias belong to?
- Additive Bias is classified in the Cognition dimension (COG) of the Human Behavior Taxonomy™, family "Reasoning", class "Mental Model". Its permanent identifier is HBT-COG-0014 and its evidence grade is B.