Solve robust multiobjective portfolio

What it's for

Finds initiatives that remain defensible across forecast error and legitimate stakeholder disagreement, while showing the exact priorities and scenarios that pressure the recommendation.

What you give it

Inputs split into evidence read from your connected systems, calibration your team owns, and numerical controls that affect precision but never the result's meaning.

{
  "budget": 400000,
  "initiatives": [
    {
      "cost": 400000,
      "dependency_ids": [],
      "exclusion_ids": [
        "resilience"
      ],
      "id": "growth",
      "objective_scenarios": {
        "resilience": [
          10,
          20,
          30
        ],
        "value": [
          200000,
          900000,
          1500000
        ]
      }
    },
    {
      "cost": 300000,
      "dependency_ids": [],
      "exclusion_ids": [
        "growth"
      ],
      "id": "resilience",
      "objective_scenarios": {
        "resilience": [
          70,
          90,
          100
        ],
        "value": [
          100000,
          300000,
          500000
        ]
      }
    },
    {

What you get back

This is the actual output of running the example above — computed by the same function the platform calls, not an illustration.

{
  "adversarial_case": {
    "objective_weights": {
      "resilience": 0.3,
      "value": 0.7
    },
    "probability_shifts": [
      {
        "adversarial_probability": 0.15,
        "nominal_probability": 0.3,
        "probability_change": -0.15,
        "scenario_index": 2
      },
      {
        "adversarial_probability": 0.35,
        "nominal_probability": 0.2,
        "probability_change": 0.15,
        "scenario_index": 0
      }
    ],
    "truncated_probability_shift_count": 0
  },
  "assumptions": [
    "Every objective uses a governed portfolio-level unacceptable and aspiration anchor; clipping creates a transparent zero-to-one utility scale and makes saturation explicit rather than normalizing to whichever candidates happened to be submitted.",
    "Admissible stakeholder preferences are exactly the bounded-simplex polytope, and the solver checks its vertices; objectives are additively compensatory inside that declared set unless a hard constraint or exclusion encodes a veto.",
    "Total-variation ambiguity moves probability only among represented joint scenarios; dependencies and exclusions are complete, initiative objective contributions are additive, and omitted interactions or regimes can reverse the portfolio.",
    "Supported tradeoffs are optima for at least one weight vertex, not the complete Pareto set; exact mode certifies only the submitted model, heuristic mode has no global optimality certificate, and neither result is a person-level ranking."
  ],
  "configuration": {
    "budget": 400000,
    "initiative_count": 3,
    "maximum_exact_states": 65536,
    "minimum_robust_utility": 0.3,
    "objective_count": 2,
    "represented_state_count": 8,
    "scenario_count": 3,
    "solver_mode": "exact_enumeration",
    "state_count_is_capped": false,
    "total_variation_radius": 0.15,
    "weight_vertex_count": 2
  },
  "decision": "robust_multiobjective_portfolio_optimized_exact",
  "method": "bounded_preference_total_variation_multiobjective_portfolio_v1",
  "objective_diagnostics": [

How it works

Constrained optimization — Pick the best feasible option under real limits — budget, headcount, dependencies, capacity — rather than ranking a list and hoping it fits.

1. 1 Define each objective's direction, portfolio-level unacceptable and aspiration anchors, practical utility difference, and admissible weight interval; enumerate every vertex of the resulting bounded simplex.
2. 2 For each dependency- and exclusion-feasible portfolio, aggregate aligned objective scenarios, map them onto anchored zero-to-one utilities, and adversarially reweight scenario probability inside a total-variation radius for every preference vertex.
3. 3 Maximize the worst preference-and-probability utility, compare with the nominal central-weight optimum, and return weight-vertex-supported tradeoffs after practical-dominance filtering with exact or heuristic provenance.

Before you trust it

Every tool in the catalog ships with the conditions under which its answer is meaningful — and the conditions under which it should abstain instead of guessing.

Calibrating it to your org

Calibration workflow

1. 1 Define the management decision, target outcome, aggregate unit, privacy boundary, cadence, and prediction/intervention horizon for this organization.
2. 2 Build a tenant-scoped historical cohort using only information available before each prediction or decision; preserve zero periods, censoring, assignment probabilities, and unresolved outcomes when the method requires them.
3. 3 Estimate statistical parameters on training history, but obtain costs, utilities, risk tolerance, practical-effect thresholds, capacity, and policy constraints from accountable decision owners.
4. 4 Validate on later time windows or held-out aggregate units at the deployment grain, against a simple baseline and the function-specific validation strategy.
5. 5 Deploy only if the returned decision clears evidence, overlap, calibration, robustness, and guardrail checks; warning, unsupported, schema-gap, and fallback decisions are abstentions.
6. 6 Monitor realized outcomes, data drift, coverage, and decision regret; recalibrate at a governed cadence or after a detected regime/definition change, never merely because a stakeholder dislikes the result.

Call it from your AI

You don't wire up 388 tools in your MCP client. The GitRevio MCP server exposes 18 tools, three of which let an agent search the catalog, read a tool's schema, and run it — so the assistant finds this one on its own.

gitrevio_capabilities_search
  { "q": "solve a budgeted dependencysafe portfolio against" }
  → finds "solve_robust_multiobjective_portfolio"

gitrevio_capability_describe
  { "capability_id": "solve_robust_multiobjective_portfolio" }
  → returns the input schema and agent guidance shown on this page

gitrevio_capability_run
  { "capability_id": "solve_robust_multiobjective_portfolio", "arguments": { ... } }
  → returns the result shown above

Works in Claude Desktop, Claude Code, Cursor, Cline, Continue.dev, Goose and Aider. See the MCP server.

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