yogsoth-ai Robustness Under Uncertainty
Purpose
Select a portfolio that performs acceptably well across a range of plausible futures, rather than optimizing for a single expected scenario. Prioritizes resilience over peak performance.
When to use
- Future conditions are highly uncertain
- Multiple plausible scenarios with different implications
- Catastrophic failure in any scenario is unacceptable
- Stakeholders prefer robustness over maximum expected value
- Cannot assign reliable probabilities to scenarios
Budget
| Dimension | Target |
|---|---|
| Candidates evaluated | 8-20 |
| Scenarios constructed | >=3 distinct futures |
| Performance metrics | 2-4 per scenario |
| Robustness threshold | acceptable in all scenarios |
State Ledger
| Field | Type | Description |
|---|---|---|
| candidates | list | All candidates with scenario-dependent performance |
| scenarios | list | Distinct future scenarios |
| performance_matrix | matrix | Candidate performance per scenario |
| regret_matrix | matrix | Regret vs best-in-scenario for each candidate |
| robust_portfolio | list | Portfolio minimizing worst-case regret |
Available Tactics
| Tactic | When |
|---|---|
| scenario-stress-testing | Core tactic — evaluate across scenarios |
| pareto-frontier-construction | Trade off robustness vs expected value |
Available SOPs
| SOP | Purpose |
|---|---|
| scenario-construction | Build distinct future scenarios |
| portfolio-evaluation-per-scenario | Evaluate portfolio in each scenario |
| portfolio-synthesis | Synthesize robust recommendation |
| objective-definition | Define robustness criteria |
| optimization-run | Find minimax-regret or robust solutions |
Execution Guidance
- Construct diverse scenarios spanning the uncertainty space
- Evaluate each candidate portfolio under each scenario
- Compute regret matrix (gap vs best possible in each scenario)
- Find portfolio minimizing maximum regret (minimax regret)
- Alternatively: find portfolio meeting minimum threshold in all scenarios
- Report robustness score and vulnerability analysis
Output Format
strategy: robustness-under-uncertainty
selected_portfolio:
- candidate: <name>
worst_case_performance: <value>
best_case_performance: <value>
robustness_score: <0-1>
max_regret: <value>
vulnerable_scenarios:
- scenario: <name>
performance: <value>
gap_to_best: <value>
method_used: <minimax-regret|robust-optimization|info-gap>
Available Tactics
Optional, no fixed order; the final leaf is always a sop.
| Tactic | When to use |
|---|---|
| pareto-frontier-construction | Build the Pareto frontier from multi-objective optimization, visualize trade-offs, and select a portfolio from non-dominated solutions. |
| scenario-stress-testing | Construct distinct future scenarios, evaluate portfolio performance under each, and identify vulnerabilities and robustness characteristics. |
Available SOPs
Optional, no fixed order; the final leaf is always a sop.
| SOP | When to use |
|---|---|
| objective-definition | Define optimization objectives, constraints, and trade-off preferences from context and candidate information. |
| optimization-run | Execute multi-objective optimization on candidates to produce a Pareto front of non-dominated solutions. |
| portfolio-evaluation-per-scenario | Evaluate a specific portfolio's performance metrics and vulnerabilities under a given scenario. |
| portfolio-synthesis | Synthesize all per-scenario evaluations into a final portfolio recommendation with robustness score and actionable guidance. |
| scenario-construction | Construct distinct future scenarios spanning key uncertainties for portfolio stress testing. |