Algorithms is a finite set of well-defined instructions for solving a problem or performing a computation. It sits in the Cognition dimension (COG) of the Human Behavior Taxonomy™ as element HBT-COG-0024, within the Reasoning family. The core principle: a finite set of well-defined instructions for solving a problem or performing a computation. 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
A finite set of well-defined instructions for solving a problem or performing a computation.
Plain-English Definition
A finite set of well-defined instructions for solving a problem or performing a computation.
Feynman Explanation
The recipe that never forgets the salt, but also never questions the recipe.
Core Principle
A finite set of well-defined instructions for solving a problem or performing a computation.
Mechanisms
Pending editorial review.
A finite set of well-defined instructions for solving a problem or performing a computation.
Pending editorial review.
Pending editorial review.
Pending editorial review.
Pending editorial review.
Algorithms scale decisions, but they also scale embedded assumptions.
Inputs (Triggers)
Pending editorial review.
Outputs (Behaviors)
Pending editorial review.
Behavioral Signature
The recipe that never forgets the salt, but also never questions the recipe.
Examples
- Credit scoring models that approve or reject loans automatically.
- Algorithms scale decisions, but they also scale embedded assumptions.
Pending editorial review.
Original analysis from The Incentives Lab — how this element behaves inside real payoff structures.
Why this element matters to incentive design
Most organizations meet this element as a personnel problem. It is not one. The mechanism underneath it is straightforward: a finite set of well-defined instructions for solving a problem or performing a computation. You can recognize it in the field by its signature: the recipe that never forgets the salt, but also never questions the recipe. 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, algorithms scale decisions, but they also scale embedded assumptions. It is amplified whenever algorithms scale decisions, but they also scale embedded assumptions. Inside organizations that shows up as algorithms scale decisions, but they also scale embedded assumptions. 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 audit algorithmic outputs for the edge cases humans would catch. 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
- Audit algorithmic outputs for the edge cases humans would catch.
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.
- Audit algorithmic outputs for the edge cases humans would catch.
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.
Deriving general rules from specific examples; the leap from instance to concept.
The brain evolved to reason adaptively, not always truthfully, to reduce the cost of errors.
We solve problems by adding, even when subtracting would be better.
Assuming that if one option is true, another must be false, when both can be true.
Presuming a purposeful actor behind events that may have no actor at all.
What cannot be settled by experiment is not worth debating.
Every model simplifies reality; some are still useful.
Researchers favor conclusions aligned with their school, team, or sponsor.
Using personal stories or isolated examples instead of evidence.
Claiming something is true or better because most people believe it.
Assuming something is true because it is probable or possible.
Using an authority's opinion as evidence, regardless of its merits.
Where Algorithms 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 incentive glossary
Definitions for every mental model, bias, and fallacy in the corpus.
- 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 Algorithms
- What is Algorithms?
- Algorithms is a finite set of well-defined instructions for solving a problem or performing a computation. It sits in the Cognition dimension (COG) of the Human Behavior Taxonomy™ as element HBT-COG-0024, within the Reasoning family. The core principle: a finite set of well-defined instructions for solving a problem or performing a computation. 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 Algorithms?
- Algorithms scale decisions, but they also scale embedded assumptions. The Incentives Lab catalogs everyday, organizational, and historical instances of this element on its Human Behavior Taxonomy™ page (HBT-COG-0024).
- How is Algorithms exploited?
- Algorithms scale decisions, but they also scale embedded assumptions.
- How do you design around Algorithms?
- Audit algorithmic outputs for the edge cases humans would catch.
- Which behavioral dimension does Algorithms belong to?
- Algorithms is classified in the Cognition dimension (COG) of the Human Behavior Taxonomy™, family "Reasoning", class "Mental Model". Its permanent identifier is HBT-COG-0024 and its evidence grade is B.