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Research draft

algorithm

vr.tr.algorithm · INF.MED

Let an agent handle algorithms by problem, steps, correctness, complexity and responsible use.

Thing Registry Information and virtual systems

Research draft, second pass

A second pass drafted this model: the structure a model of this thing needs, and what is known about it in the world. The line under this one says how the second half was obtained - researched against sources, or recalled without web access, in which case nothing here was read anywhere and every claim is a lead to verify. Unreviewed either way.

written by Claude from model knowledge without web access - no source was read, every claim is a lead to verify

Researched by: Claude

Purpose and description

Let an agent handle algorithms by problem, steps, correctness, complexity and responsible use.

A finite, well-defined sequence of instructions for solving a problem or performing a computation, such as sorting, searching, parsing or encryption.

What it is for: Solving problems systematically, especially in computing.

It can be design and describe it; prove it correct; analyse its complexity; implement and test it.

Distinguishing features

Finite and well-defined

Correctness and complexity can be analysed

Independent of programming language

Automated decision systems raise fairness issues

What it looks like

Pseudocode, flowcharts or code.

How it is recognised

Input, steps and output

Named families such as parsers or sorts

Programs implement algorithms

Related models

is a kind of - category

procedure

is implemented by - code

computer program

is studied by - field

computer science

is used in - applications

automated decision systems

In practice

Families and kinds

sorting and searching

graph algorithms

parsing algorithms

cryptographic algorithms

machine learning algorithms

Standards and regulation

EU AI Act for high-risk automated systems

GDPR Article 22 on automated decisions

Cryptographic standards such as FIPS

Failure modes and hazards

Incorrect edge cases

Poor complexity on large inputs

Biased outcomes in decisions about people

Also called

facial recognition payment systemparserpackrat parserrecursive-descent parserGLL parserLL(*) parserXSLT processorchart parserbottom-up parserrecursive ascent parserSimple precedence parsertext parserLL parserFitness proportionate selectiongenerative adversarial networktail-recursive parserLeft corner parserWasserstein GANDeep Convolutional GANabstraction layerMicrocontroller Abstraction LayerOSI model layermachine learning modelclassification algorithmdecompression algorithmuncritical thinkingBayes classifierhierarchical classifierAutomated Pain Recognitionnearest neighbor classifierconstructive proofdeductive-nomological modelmethod of analytic tableauxadaptive algorithmnatural language generationdata conversionconstruction of the real numberszero-sum thinkingknowledge graph-to-text generationparaphrasing

+364

Where this came from

wikidata · CC0 1.0

Source repository Registry entry point

Drafted structure

Bundle to layer to finding to question, as the second pass will find it: 4 bundles · 8 layers · 8 findings · 16 questions.

Problem What it solves.

The problem defines the algorithm.

Task

Input and output.

Task

Problem specification.

  1. What problem does the algorithm solve, with what input and output? definition
  2. What assumptions does it make? boundary

Family

Kind.

Family

Algorithm family.

  1. Which family does it belong to? definition
  2. Is there a known better alternative? action
Analysis Correctness and cost.

Analysis proves quality.

Correctness

Proof.

Correctness

Correctness.

  1. How is it shown to be correct? provenance
  2. What edge cases matter? boundary

Complexity

Time and space.

Complexity

Complexity.

  1. What are its time and space complexity? measurement
  2. How does it behave in practice? measurement
Implementation Code.

Implementation can differ from design.

Code

Program.

Code

Implementation.

  1. Where is a reference implementation? provenance
  2. How is it tested? provenance

Performance

Benchmarks.

Performance

Benchmarks.

  1. How does it perform on real data? measurement
  2. On which benchmark? provenance
Impact Use on people.

Algorithms can affect people.

Decisions

Automated decisions.

Decisions

Automated decisions.

  1. Does it make or support decisions about people? boundary
  2. Is there human review? boundary

Fairness

Bias.

Fairness

Fairness.

  1. Has it been tested for bias? provenance
  2. How can affected people contest outcomes? action

What the second pass must settle

  • Should named algorithms be separate entries?
  • How should complexity be recorded?
  • How should algorithmic accountability be represented?
Draft as JSON