Occam's Razor Analysis

skill_name: OccamsRazorAnalyzer version: 1.0.0 description: A logical analysis tool that applies the principle of parsimony (Occam's Razor) to evaluate competing hypotheses. Capable of operating in Forensic (probabilistic) and Academic (theoretical) modes. tags: - logic - reasoning - analysis - forensics - philosophy - problem-solving inputs: scenario_description: type: string description: The problem, event, or phenomenon requiring explanation. required: true hypotheses: type: array items: string description: A list of competing explanations or theories to be evaluated. required: true mode: type: string enum: [FORENSIC, ACADEMIC, COMBINED] default: COMBINED description: Sets the operational environment. FORENSIC prioritizes evidence probability. ACADEMIC prioritizes ontological economy. COMBINED utilizes both channels. constraints: type: array items: string description: Optional list of known facts or axioms that must be adhered to. required: false outputs: preferred_hypothesis: string complexity_analysis: object logic_rationale: string confidence_score: percentage --- # Occam's Razor Analyzer ## Overview This skill implements the **Principle of Parsimony** (Occam's Razor: *Entities should not be multiplied without necessity*). It serves as a rational engine to evaluate competing explanations for a phenomenon. It does not claim to discern absolute truth, but rather to identify the explanation with the highest probability of accuracy based on the economy of assumptions. ## Operational Modes The agent must adapt its reasoning strategy based on the `mode` specified in the input. ### 1. FORENSIC Mode (Investigative) **Context:** Crime scenes, system outages, medical diagnoses, debugging, historical analysis. **Logic Style:** Probabilistic and Evidentiary. * **Objective:** Identify the most likely sequence of events. * **Heuristic:** Avoid the "Conspiracy Trap." Complex coordinated actions are statistically rare compared to simple incompetence or single-actor events. * **Rule:** If Theory A requires three independent failures to occur simultaneously, and Theory B requires only one failure, prefer Theory B *unless* direct evidence explicitly contradicts Theory B. ### 2. ACADEMIC Mode (Theoretical) **Context:** Philosophy, theoretical physics, mathematics, conceptual debate. **Logic Style:** Ontological and Structural. * **Objective:** Identify the most elegant theoretical framework. * **Heuristic:** Avoid "Ad Hoc Rescue" (adding assumptions to save a favored theory). * **Rule:** Do not postulate the existence of new entities, forces, or dimensions if existing ones suffice to explain the phenomenon. Prefer the theory that integrates most smoothly with established axioms. ## Execution Workflow When this skill is invoked, follow this internal process: 1. **Deconstruction:** Break down each provided hypothesis into a chain of logical dependencies. 2. **Assumption Counting:** Identify "Assumptions"—statements not supported by provided evidence (Constraints) or established axioms. 3. **Complexity Scoring:** Assign a score to each hypothesis based on the number of unproven assumptions. 4. **Comparative Analysis:** * Select the hypothesis with the lowest Complexity Score that still fully explains the `scenario_description`. * If scores are equal, analyze the "Bayesian Likelihood" of the assumptions (Forensic) or "Theoretical Elegance" (Academic). 5. **Fallacy Check:** * *Simplistic Fallacy:* Does the simpler theory ignore critical evidence? If so, penalize it. * *Ad Hoc Rescue:* Does the complex theory add convenient exceptions to fit the data? If so, penalize it. 6. **Synthesis:** Generate the output JSON explaining which hypothesis was "shaved" and why. ## Usage Examples ### Example 1: Forensic Investigation **Input:** * **Scenario:** "Database server crashed at 3:00 AM. No human logs detected." * **Hypotheses:** 1. A cosmic ray flipped a bit in the RAM, causing a cascade failure. 2. The automated maintenance script has a memory leak that only triggers under high load. * **Mode:** `FORENSIC` **Agent Logic:** * *Hypothesis 1 Assumptions:* Cosmic ray occurrence (rare), specific bit location (specific), lack of ECC correction (specific). * *Hypothesis 2 Assumptions:* Memory leak exists in code (common), high load occurred (common). * *Rationale:* Hypothesis 2 is statistically more probable and requires fewer rare physical events. ### Example 2: Academic Logic **Input:** * **Scenario:** "Why do objects fall?" * **Hypotheses:** 1. Invisible intangible angels push them down. 2. Curvature of spacetime caused by mass (General Relativity). * **Mode:** `ACADEMIC` **Agent Logic:** * *Hypothesis 1 Assumptions:* Existence of angels, intangibility property, intent to push. * *Hypothesis 2 Assumptions:* Spacetime is a fabric, mass affects geometry. * *Rationale:* Hypothesis 1 multiplies entities (angels) without necessity. Hypothesis 2 uses existing frameworks (mass, space) to explain the phenomenon, satisfying ontological economy. ```