I. Foundations & Purpose
Define the overall problem objective.
Identify desired outputs from problem-solving.
Determine the scope and boundaries of the problem.
Clarify assumptions underlying the problem.
Specify constraints and limitations.
Identify stakeholders affected by the problem.
Determine required data or inputs.
Assess dependencies between problem components.
Define success criteria for solutions.
Clarify decision-making authority or control points.
II. Problem Breakdown
Identify sub-problems or modules.
Determine interdependencies among sub-problems.
Prioritize sub-problems by impact or urgency.
Assess complexity of each sub-problem.
Evaluate feasibility of solving each sub-problem.
Identify optional vs mandatory sub-problems.
Map sub-problem sequences for stepwise resolution.
Evaluate parallelizability of sub-problems.
Model cause-effect relationships among sub-problems.
Determine potential bottlenecks in decomposition.
III. Root Cause Analysis
Identify root causes of each sub-problem.
Evaluate contributing factors to complexity.
Model causal chains leading to the main problem.
Detect hidden dependencies.
Assess upstream and downstream effects.
Evaluate secondary impacts of root causes.
Determine most influential variables.
Assess historical patterns contributing to the problem.
Model cascading effects of root causes.
Prioritize root causes for targeted intervention.
IV. Sub-Problem Detailing
Specify inputs required for each sub-problem.
Determine outputs expected from sub-problems.
Model solution constraints per sub-problem.
Evaluate logical steps required for resolution.
Assess complexity vs impact for each sub-problem.
Detect missing components in sub-problem definitions.
Model ideal sequence for sub-problem resolution.
Evaluate risk associated with each sub-problem.
Assess cross-sub-problem dependencies.
Prioritize sub-problems for stepwise tackling.
V. Data & Evidence Integration
Identify required data for decomposition.
Evaluate reliability of input data.
Model stepwise analysis using evidence.
Assess uncertainty in each sub-problem.
Integrate qualitative and quantitative inputs.
Detect gaps in available evidence.
Model probabilistic reasoning for uncertain variables.
Assess sensitivity of sub-problems to data variations.
Evaluate impact of incomplete data on decomposition.
Prioritize high-impact data sources for analysis.
VI. Multi-Step Solution Mapping
Model solution steps for each sub-problem.
Assess sequential dependencies among solution steps.
Evaluate intermediate outputs.
Detect errors or gaps in stepwise logic.
Optimize order of steps for efficiency.
Model alternative solution pathways.
Evaluate trade-offs between speed and accuracy.
Assess cumulative impact of stepwise solutions.
Identify critical steps for monitoring.
Prioritize bottleneck steps for refinement.
VII. Scenario Analysis
Model “what-if” scenarios for sub-problems.
Evaluate alternative strategies for resolution.
Assess sensitivity of solutions to variable changes.
Detect cascading effects in complex scenarios.
Model adaptive responses to scenario changes.
Evaluate scenario-specific risks and opportunities.
Model forward-looking predictive outcomes.
Assess backward reasoning from desired outcomes.
Prioritize scenarios with highest strategic importance.
Integrate scenario outputs for comprehensive planning.
VIII. Risk & Uncertainty Assessment
Identify high-risk sub-problems.
Evaluate uncertainty propagation across steps.
Model probabilistic outcomes for each sub-problem.
Assess likelihood of solution failure.
Detect sensitivity to input assumptions.
Model contingency plans for critical risks.
Evaluate cascading risk effects across sub-problems.
Assess confidence intervals for outcomes.
Model worst-case vs best-case scenarios.
Prioritize risk mitigation for high-impact sub-problems.
IX. Interdependency Mapping
Model interactions among sub-problems.
Assess impact of changes in one sub-problem on others.
Detect feedback loops in problem structure.
Evaluate bottlenecks caused by interdependencies.
Model parallel vs sequential dependencies.
Assess critical nodes for overall problem resolution.
Identify redundant or overlapping sub-problems.
Evaluate synchronization needs across sub-problems.
Model network effects in complex problem systems.
Prioritize interdependent sub-problems for early action.
X. Multi-Agent & Collaborative Decomposition
Model problem decomposition across multiple stakeholders.
Assess allocation of sub-problems to agents or teams.
Evaluate coordination mechanisms for joint solutions.
Detect potential conflicts among agents.
Model knowledge sharing across participants.
Assess impact of different expertise levels.
Evaluate consensus-building for multi-agent solutions.
Model sequential collaboration across teams.
Assess cumulative learning across agents.
Prioritize collaborative sub-problems with highest impact.
XI. Complexity Reduction
Identify high-complexity sub-problems.
Evaluate simplification strategies.
Model abstraction levels for easier analysis.
Detect unnecessary detail that increases complexity.
Assess trade-offs between detail and clarity.
Model modularization of large problems.
Evaluate reduction of cross-dependencies.
Assess visualization strategies for problem clarity.
Detect over-complication in solution steps.
Prioritize simplification for high-complexity areas.
XII. Stepwise Verification
Define validation criteria for each sub-problem solution.
Evaluate intermediate results for correctness.
Detect logical inconsistencies across steps.
Assess accuracy of assumptions in sub-problems.
Model error propagation through solution steps.
Evaluate completeness of stepwise reasoning.
Assess redundancy in verification steps.
Model automated checks for intermediate outputs.
Prioritize critical sub-problems for verification.
Assess alignment with overall problem objectives.
XIII. Optimization & Efficiency
Evaluate efficiency of solution pathways.
Model time and resource constraints.
Assess parallelization opportunities.
Optimize sequence of sub-problem resolution.
Detect bottlenecks limiting speed or throughput.
Evaluate trade-offs between speed and accuracy.
Model automated task allocation for efficiency.
Assess cumulative resource usage across sub-problems.
Prioritize high-impact steps for optimization.
Model cost-benefit analysis of alternative decompositions.
XIV. Feedback & Learning
Incorporate feedback from previous solutions.
Evaluate performance of prior decomposition attempts.
Model iterative improvement cycles.
Assess recurring patterns in sub-problems.
Detect repeated errors and refine strategies.
Evaluate adaptive re-weighting of sub-problem importance.
Model knowledge retention for complex problems.
Assess learning from scenario variations.
Evaluate effectiveness of past interventions.
Prioritize feedback-driven refinement for critical sub-problems.
XV. Cross-Domain Decomposition
Model sub-problems across multiple domains.
Assess transfer of solutions between domains.
Evaluate inter-domain dependencies.
Detect conflicting domain-specific assumptions.
Model knowledge integration across disciplines.
Assess domain-specific constraints on solutions.
Evaluate multi-disciplinary collaboration needs.
Model adaptive strategies for cross-domain interactions.
Assess impact of domain-specific variability.
Prioritize cross-domain sub-problems for strategic focus.
XVI. Ethical & Value-Based Decomposition
Identify ethical considerations for sub-problems.
Assess alignment with organizational values.
Evaluate societal or environmental impacts.
Detect potential harm in proposed solutions.
Model fairness constraints in sub-problem prioritization.
Assess compliance with regulatory requirements.
Evaluate stakeholder perception of solution fairness.
Model decision trade-offs with ethical implications.
Assess transparency and accountability in decomposition.
Prioritize ethical constraints in critical sub-problems.
XVII. Scenario & Contingency Planning
Model alternative problem decomposition strategies.
Assess scenario-specific sub-problem prioritization.
Evaluate sensitivity to external changes.
Detect cascading failures in contingency scenarios.
Model stepwise adaptation to unexpected events.
Assess robustness of solution pathways.
Evaluate fallback strategies for high-risk sub-problems.
Model iterative scenario planning.
Assess cumulative impact of contingency measures.
Prioritize high-impact scenarios for early preparation.
XVIII. Multi-Step Reasoning Chains
Model reasoning for sequential problem decomposition.
Assess intermediate outputs for logical coherence.
Detect stepwise errors in reasoning.
Evaluate alternative reasoning chains.
Model iterative refinement of reasoning sequences.
Assess cumulative impact of multi-step solutions.
Evaluate branching and conditional logic.
Detect redundant or unnecessary steps.
Model step weighting based on importance.
Prioritize reasoning chains for high-value outcomes.
XIX. Visualization & Mapping
Map problem structure graphically.
Visualize sub-problem dependencies.
Model causal chains for root causes.
Evaluate clarity of visual representation.
Detect overlapping or redundant nodes.
Assess modularization through diagrams.
Model stepwise solution flows visually.
Evaluate scenario simulation using visualization tools.
Assess cross-domain interactions in visual maps.
Prioritize visualization for complex or critical sub-problems.
XX. Continuous Improvement & Learning
Track iterative improvements in decomposition strategies.
Assess recurring issues and refine methods.
Model learning from previous problem-solving attempts.
Evaluate performance metrics over time.
Detect bottlenecks in repeated processes.
Assess efficiency gains from iterative refinement.
Model automated adaptation of decomposition strategies.
Evaluate cumulative learning across multiple problems.
Assess long-term improvement in accuracy and efficiency.
Prioritize continuous improvement actions for high-impact problem areas.
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