robustness-under-uncertainty

name	robustness-under-uncertainty
description	Select portfolios that perform well across multiple future scenarios using Minimax regret, Robust optimization, Scenario planning, and Info-gap methods.
dependencies	{"tactics":["pareto-frontier-construction","scenario-stress-testing"],"sops":["objective-definition","optimization-run","portfolio-evaluation-per-scenario","portfolio-synthesis","scenario-construction"]}

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

1. Construct diverse scenarios spanning the uncertainty space
2. Evaluate each candidate portfolio under each scenario
3. Compute regret matrix (gap vs best possible in each scenario)
4. Find portfolio minimizing maximum regret (minimax regret)
5. Alternatively: find portfolio meeting minimum threshold in all scenarios
6. 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.