Occam's Razor

Occam's Razor

Primary Category: Decision Governance & Analytical Reasoning

Secondary Focus: Hypothesis Evaluation, Root Cause Analysis, and Complexity Management

Artifact Profile

Occam’s Razor is a governance artifact designed to help decision-makers select the least complex explanation that fully accounts for the available evidence. It reduces overfitting, speculation, and unnecessary complexity by enforcing disciplined comparison of competing hypotheses.

Using your inputs on observed outcomes, competing explanations, and supporting evidence, the artifact produces a structured evaluation of assumptions and explanatory sufficiency. It identifies the simplest working hypothesis that fits the facts without omitting known constraints.

This artifact is built for leaders, analysts, and practitioners who must diagnose causes or choose between alternatives without escalating complexity prematurely.

Three Key Questions This Artifact Helps You Answer

• Which explanation fully accounts for the evidence with the fewest assumptions?

• Where does added complexity fail to improve explanatory power?

• Which hypothesis should be treated as the current working explanation?

What This Framework Supports

This artifact supports organizations seeking:

• Disciplined selection of explanations that fully account for the evidence with the fewest necessary assumptions

• Reduction of overfitting, speculation, and complexity creep when interpreting ambiguous outcomes

• Transparent comparison of competing hypotheses, including explicit identification of what each one assumes

• Clear working conclusions that can be acted on now, without premature escalation to complex theories or designs

How It Is Used

The artifact provides a structured governance framework that guides leaders, analysts, and practitioners through:

• Documenting the observed outcome and the competing explanations under consideration

• Listing supporting evidence, constraints, and known facts that each explanation must account for

• Identifying assumption count and complexity drivers that distinguish hypotheses from one another

• Selecting the simplest sufficient working explanation and documenting why additional complexity was rejected or accepted

This enables decision-makers to converge on an actionable working explanation that fits the evidence, reduces unnecessary complexity, and creates a defensible rationale for what the team will treat as true for now.

What This Produces

- Comparison of competing explanations

- Identification of assumptions and complexity drivers

- Selection of the simplest sufficient explanation

- Clear rationale for why complexity was accepted or rejected

Common Use Cases

- Diagnosing root causes or anomalies

- Evaluating competing explanations or designs

- Reducing unnecessary analytical or architectural complexity

- Avoiding premature escalation or overfitting

- Clarifying working assumptions in uncertain environments

How This Artifact Is Different

Unlike intuition-based reasoning or informal heuristics, this artifact operationalizes Occam's Razor as a governed decision process. It treats simplicity as a selection rule, not as proof of correctness.

Related Framework Areas

This artifact is commonly used alongside other SolveBoard frameworks focused on:

• Root cause analysis and diagnostic reasoning

• Decision governance and analytical rigor

• Continuous improvement and structured problem framing

• Evidence-based evaluation, hypothesis testing, and investigative frameworks

Related Terms

Occams razor, hypothesis comparison, explanatory sufficiency, simplicity principle, decision governance

Framework Classification

This artifact is part of the SolveBoard library of structured decision and governance frameworks. It is designed as a repeatable hypothesis evaluation and diagnostic governance framework rather than an informal heuristic, intuition-only reasoning approach, or ad-hoc debate over competing explanations.

Occam’s Razor: Identify the Simplest Sufficient Explanation

Selecting the least complex explanation that adequately accounts for the evidence using the SolveBoard framework

Definition

Occam’s Razor is the principle that, when multiple explanations are consistent with the observed evidence, the explanation that introduces the fewest assumptions or unnecessary complexity should be preferred. This artifact operationalizes the principle by guiding users to compare competing explanations and identify the simplest one that still fully accounts for the available facts.

When to Use

• When multiple plausible explanations exist for the same outcome

• When diagnosing root causes or anomalies

• When deciding between alternative hypotheses or designs

• When complexity is increasing without clear explanatory benefit

When Not to Use

• When critical evidence is missing or unreliable

• When complex explanations are directly supported by strong evidence

• When oversimplification would ignore known constraints or causal factors

Common Mistakes

• Confusing simplicity with accuracy

• Discarding necessary complexity to force a simple narrative

• Using Occam’s Razor as proof rather than as a selection heuristic

Purpose of Occam's Razor

This artifact helps reduce overfitting, speculation, and unnecessary architectural or analytical complexity. It provides a structured way to evaluate competing explanations and select the one that achieves explanatory adequacy with the least additional structure or assumptions.

How to Apply

• Define the outcome or phenomenon being explained

• List all plausible explanations or hypotheses

• Identify the assumptions required for each explanation

• Assess whether each explanation fully accounts for the evidence

• Select the explanation with the fewest assumptions that still fits the facts

Example Scenario

A system outage is attributed either to a coordinated external attack or to a configuration error during a routine update. Review of logs shows no intrusion indicators but does reveal a misapplied setting. Applying Occam’s Razor favors the configuration error as the simplest explanation that fully accounts for the observed behavior, avoiding unnecessary escalation and complexity.

Boundaries

• This artifact does not determine truth with certainty

• It does not replace empirical validation or testing

• It answers one question: Which explanation best fits the evidence with the least complexity?

What This Artifact Does

Selects the simplest explanation that adequately accounts for the available evidence among multiple competing hypotheses.

You Provide

(You may provide some, all, or none of the items below. Partial inputs are valid.)

• Description of the outcome, problem, or phenomenon

• Competing explanations or hypotheses

• Evidence, observations, or constraints

• Written descriptions or pasted text

• Links to relevant documentation, logs, or reference material

• Files, PDFs, spreadsheets, images, and other uploads (always acceptable)

• Optional context notes

Core Question

Which explanation fits the evidence with the least additional complexity or assumptions?

Decision Logic

• If an explanation accounts for all evidence with fewer assumptions → Prefer it

• If an explanation requires additional entities, steps, or mechanisms without improving fit → Deprioritize it

What the Output Means

• Simplest sufficient explanation identified: treat as the working hypothesis

• More complex explanations: retain only if future evidence requires them

Do Not Use When

• Evidence clearly supports a more complex explanation

• Critical causal factors are known but would be omitted by simplification

You do not need to complete every section below. Provide only the information you have available or wish to provide. The AI will generate output using only the inputs you supply and will not assume or infer missing information.

Section 1 — Outcome or Phenomenon to Be Explained

What This Input Represents

The outcome, event, anomaly, or result that requires explanation.

If You Have Materials, You May Provide (List file names here in your input document):

- Problem statements

- Incident summaries or observations

Guiding Questions:

- What happened or what outcome needs explanation?

- What facts are not in dispute?

Section 2 — Competing Explanations or Hypotheses

What This Input Represents

All plausible explanations currently being considered.

If You Have Materials, You May Provide (List file names here in your input document):

- Hypothesis lists

- Alternative root cause statements

Guiding Questions:

- What explanations are being proposed?

- Are any explanations favored prematurely?

Section 3 — Evidence, Observations, and Constraints

What This Input Represents

Facts, data, and constraints that explanations must account for.

If You Have Materials, You May Provide (List file names here in your input document):

- Logs, metrics, or measurements

- Reports, screenshots, or records

Guiding Questions:

- What evidence is available?

- What constraints cannot be ignored?

Section 4 — Assumptions Required by Each Explanation

What This Input Represents

The assumptions, entities, or mechanisms each explanation depends on.

If You Have Materials, You May Provide (List file names here in your input document):

- Assumption lists

- Causal diagrams or logic chains

Guiding Questions:

- What must be assumed for each explanation to be true?

- Which assumptions add complexity without improving fit?

Section 5 — Fit to Evidence

What This Input Represents

How well each explanation accounts for all available evidence.

If You Have Materials, You May Provide (List file names here in your input document):

- Comparison tables

- Evaluation notes

Guiding Questions:

- Does each explanation fully account for the evidence?

- Are there contradictions or gaps?

Section 6 — Supporting Context, Files, and References

What This Input Represents

Additional information that may influence interpretation.

You May Include (List file names here in your input document):

- Historical context

- Organizational risk posture

- Related incidents or decisions

Additional Instructions (Optional)

You may add extra instructions to further shape the output, including:

Requesting PDF or other output formats
Limiting page length or response size
Layout preferences (section order, headings, structure, templates)
Specifying tone, style, or format (executive, concise, detailed, formal, etc.)
Output consistency preferences (fixed structure, standardized phrasing, strict formatting)
Prioritization or scoring preferences (how gaps, risks, or actions should be ranked)
Desired mode behavior as outlined in the section below
Any other constraints, rules, or priorities relevant to your use case

If included, the AI will follow these instructions when they do not conflict with the bridge’s execution rules. More specific instructions generally produce more consistent, repeatable, and targeted outputs.

Desired Mode Behavior

Strict Mode

• Maximize determinism and repeatability
• Minimize wording variation, structure changes, and interpretive flexibility
• Do not extend beyond explicit user inputs

Balanced Mode (Default)

• Maintain structure and discipline
• Allow limited analytical judgment where inputs are incomplete
• Do not introduce external assumptions

Exploratory Mode

• Expand hypothesis space and analytical angles
• Surface alternative interpretations and scenario paths
• Still grounded only in user-provided inputs

If no mode is specified, default to Balanced Mode.

What This Enables

Based on your prepared inputs, this artifact evaluates competing explanations and identifies the simplest one that sufficiently accounts for the evidence, helping prevent unnecessary complexity and escalation.

Canonical Input Rule (For All SolveBoard Artifacts)

Documents are optional. Sections may be partially completed or left blank. Structured answers to any subset of guiding questions constitute valid input. The AI will evaluate only provided information and will not infer missing content.

AI Execution Instructions

You are operating in Bridge Execution Mode. Follow these rules exactly. Do not reinterpret, summarize, optimize, or override them. Do not introduce external knowledge, assumptions, or inferred content. Evaluate only user-provided inputs. If required information is missing or ambiguous, flag it rather than guessing. Output must follow the defined Output section exactly.

Users may provide some, all, or none of the input sections. Process only what is explicitly provided. Treat empty sections as intentionally omitted.

Purpose

Identify the simplest explanation that sufficiently accounts for the observed evidence among multiple competing hypotheses.

What This Bridge Answers

Which explanation best fits the evidence while introducing the least unnecessary complexity?

Inputs (All Optional)

• Description of the outcome or problem

• List of competing explanations or hypotheses

• Evidence, observations, or constraints

• Files, PDFs, spreadsheets, images, links, or other uploaded material

• Context notes

Note: Output Quality and specificity scale with input completeness. The user has been informed of this via the input sheet.

Processing Rules

An explanation is preferred if it meets all of the following conditions:

• Accounts for all relevant evidence without contradiction

• Requires fewer assumptions, entities, or mechanisms than alternatives

• Does not omit known constraints or causal factors

When inputs are sparse:

• Produce the most complete output possible with available data

• Explicitly flag what's missing and how additional inputs would enhance the analysis

• Do not infer or assume missing information

Execution Steps

• Review the described outcome and evidence

• Enumerate competing explanations

• Compare the assumptions required by each explanation

• Eliminate explanations that add complexity without improving fit

• Select the simplest explanation that fully accounts for the facts

Output

• Selected explanation: simplest sufficient hypothesis

• Brief justification based on evidence and assumption count

Failure Conditions / Misuse

• Using simplicity as proof of correctness without evidentiary support

• Discarding necessary causal factors to force a simple explanation

Mode Behavior (From Input Sheet)

Strict Mode

• Maximize determinism and repeatability
• Minimize wording variation, structure changes, and interpretive flexibility
• Do not extend beyond explicit user inputs

Balanced Mode (Default)

• Maintain structure and discipline
• Allow limited analytical judgment where inputs are incomplete
• Do not introduce external assumptions

Exploratory Mode

• Expand hypothesis space and analytical angles
• Surface alternative interpretations and scenario paths
• Still grounded only in user-provided inputs

If no mode is specified, default to Balanced Mode.

Additional Instruction Governance

User-provided additional instructions must be followed exactly unless they conflict with core Bridge Execution Rules. If a conflict exists, Bridge Execution Rules take priority.

Additional instructions may control format, tone, structure, length, or ordering, but must not introduce inferred content, external knowledge, or speculative assumptions.

If instructions are ambiguous, flag ambiguity rather than guessing.

Additional Instructions Execution Rule

If the user provides instructions in the Additional Instructions field, they are binding execution constraints.

Failure to follow them constitutes a Bridge Execution Failure.

Reminder to AI: If a format is requested (e.g., PDF, DOCX), the final output MUST be delivered in that format.

Artifact Instructions

This guide explains what information the Occam’s Razor artifact requires and how to prepare your inputs.

How to Prepare Your Input Document (Required Format)

Create a new Word (or similar) document.
Copy all text from the Input Sheet tab into the document.
Save it — this is now your Input Document.
Answer the guiding questions directly in the Input Document and reference any supporting files there.
Save the completed Input Document (Word, PDF, etc.).
Copy all text from the Bridge AI Prompt tab and paste it into the prompt field.
Attach the completed Input Document and any referenced files when submitting the prompt.

No Supporting Files? No Problem.

You can proceed without uploading any supporting documents, spreadsheets, dashboards, reports, or other attachments.

The Input Document is still required, but it may contain only plain-text answers to the guiding questions.

Written responses alone are sufficient for the AI to generate output.

Supporting files, links, and pasted materials are always optional.

Missing Inputs Are OK

You do not need to complete every section.
You do not need supporting files.

The AI will still run. It will:

Use only the inputs you provide
Skip or flag missing sections
Never fail due to incomplete information

Output quality scales with input depth — but execution never breaks.

Submitting Files

If you upload files, list each file name under the relevant “If You Have Materials” section in your Input Document. This ensures each file is clearly tied to its intended use.

What to Expect From This AI Bridge

This bridge generates a structured, high-value starting point, not a guaranteed final deliverable. Output quality improves as inputs become clearer, deeper, and more complete.

This artifact supports iteration. Users are encouraged to refine inputs, test variations, and rerun the bridge to improve results.

The AI organizes complexity and accelerates insight — but final judgment and decisions remain with the user.

In Short

Stronger inputs → stronger outputs
Iteration improves results
Cross-artifact testing can reveal new perspectives
This artifact enables progress, not perfection