erotetic

name	Erotetic
description	Question-driven reasoning based on Koralus's Erotetic Theory. USE WHEN erotetic, question-driven reasoning, enumerate alternatives, check reasoning, reasoning audit, fallacy check, what am I not considering, stable conclusion.

Erotetic Reasoning Skill

Question-driven reasoning grounded in Philipp Koralus's Erotetic Theory of Reasoning (Oxford, 2023). Forces explicit alternative enumeration, systematic question-raising, and equilibrium checking before settling on conclusions.

Core insight: The same mechanism that produces valid reasoning also produces fallacies — the variable is how many questions you raise before settling. More questions → closer to erotetic equilibrium → convergence with classical validity, probabilistic coherence, and expected utility maximization.

How it differs from other thinking approaches:

• First Principles deconstructs constraints and rebuilds from fundamentals
• Devil's Advocate / Red Team attacks arguments from adversarial perspectives
• Chain-of-thought asks to "think step by step" without a formal stopping criterion
• Erotetic forces you to enumerate ALL alternatives, raise the questions you haven't asked, and test whether conclusions survive further questioning

Workflow Routing

Workflow	Trigger	File
Reason	"think through this erotetically", "what am I not considering", "enumerate alternatives", "question-driven analysis"	Workflows/Reason.md
Audit	"audit this reasoning", "check for fallacies", "is this conclusion stable", "erotetic audit"	Workflows/Audit.md

Quick Reference

The Erotetic Reasoning Loop:

1. Enumerate — List ALL disjunctive alternatives (not just the obvious ones)
2. Question — What questions does this raise? What haven't I asked?
3. Filter — Given new information, which alternatives survive?
4. Equilibrium Check — Is this conclusion stable under one more question?
5. Iterate or Settle — If unstable, return to step 2. If stable, conclude.

ETR-Predicted Fallacies (what to watch for):

• Settling on first best-match without enumerating alternatives (conjunction fallacy)
• Ignoring base rates when a vivid alternative is present
• Illusory inferences from disjunctions
• Premature closure — stopping questioning too early

Examples

Example 1: Strategic decision

User: "Should I raise prices 20%? Think through this erotetically."
→ Invokes Reason workflow
→ Enumerates: price stays / +10% / +20% / +30% / restructure pricing / freemium shift
→ Raises questions: What's the churn elasticity? What do competitors charge?
→ Filters alternatives based on available evidence
→ Checks equilibrium: "Is +20% stable if I ask one more question?"
→ Returns structured analysis with equilibrium assessment

Example 2: Audit existing reasoning

User: "I concluded we should use microservices. Audit this reasoning."
→ Invokes Audit workflow
→ Identifies: What alternatives were considered? (monolith, modular monolith, serverless?)
→ Checks: Was this a best-match latch without enumeration?
→ Tests equilibrium: Does "microservices" survive the question "what's our team size?"
→ Returns fallacy classification and stability assessment

Example 3: Combined with other skills

User: "Red team this idea, but use erotetic reasoning"
→ Invoke adversarial analysis with Erotetic as the reasoning framework
→ Each agent enumerates alternatives and raises questions
→ Equilibrium check on the steelman and counter-argument

Integration Pattern

Other skills can invoke Erotetic reasoning:

## Before Concluding
→ Use Erotetic/Reason to enumerate alternatives you haven't considered
→ Use Erotetic/Audit to check if your conclusion is stable

## When Stuck
→ Use Erotetic/Reason — the bottleneck is usually an unasked question

## For High-Stakes Decisions
→ Adversarial analysis + Erotetic: attack with explicit alternative enumeration
→ First Principles + Erotetic: deconstruct, then question every reconstruction

Theoretical Foundation

• Source: Koralus, P. (2023). Reason and Inquiry. Oxford University Press.
• Formalization: PyETR (Oxford HAI Lab)
• Key result: At erotetic equilibrium, reasoning converges to classical validity, probabilistic coherence, AND expected utility maximization
• AI finding: Richardson et al. (2025) — as LLMs scale, errors become more human-like in ETR-predicted ways; scaling alone does not solve reasoning