Allocate capacity nash bargaining

What it's for

Adds a defensible fairness-and-efficiency mechanism for capacity conflicts instead of letting the largest raw business case consume every marginal unit.

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.

{
  "total_capacity_units": 4,
  "units": [
    {
      "id": "platform",
      "minimum_capacity_units": 1,
      "utility_by_capacity": [
        0,
        10,
        18,
        24,
        28
      ]
    },
    {
      "id": "product",
      "minimum_capacity_units": 1,
      "utility_by_capacity": [
        0,
        4,
        8,
        12,
        16
      ]
    }
  ]
}

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.

{
  "allocation": [
    {
      "capacity_units": 2,
      "next_unit_marginal_utility": 6,
      "normalized_bargaining_weight": 0.5,
      "unit_id": "platform",
      "utility": 18,
      "utility_gain_over_disagreement": 18
    },
    {
      "capacity_units": 2,
      "next_unit_marginal_utility": 4,
      "normalized_bargaining_weight": 0.5,
      "unit_id": "product",
      "utility": 8,
      "utility_gain_over_disagreement": 8
    }
  ],
  "assumptions": [
    "Utility curves are versioned team-level decision inputs with diminishing marginal returns and comparable utility units.",
    "The disagreement utility represents the credible outcome if bargaining fails, not a punitive baseline.",
    "Weighted Nash welfare balances proportional gains; bargaining weights require explicit governance and must not encode organizational power by accident.",
    "The optimizer allocates abstract capacity units, not named people, and local execution constraints remain a human planning layer."
  ],
  "capacity": {
    "allocated": 4,
    "available": 4,
    "shadow_log_welfare_of_next_unit": 0.4055,
    "unallocated": 0
  },
  "decision": "bargaining_allocation_found",
  "method": "weighted_discrete_nash_capacity_bargaining_v1",
  "nash_social_welfare": {
    "total_utility": 26,
    "weighted_log_product": 4.9698
  },
  "utilitarian_counterfactual": {
    "allocation": {
      "platform": 3,
      "product": 1
    },
    "price_of_fairness_fraction": 0.0714,
    "total_utility": 28

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 Allocate discrete shared capacity by weighted Nash social welfare over concave team utility curves, with disagreement guarantees and a utilitarian counterfactual.
2. 2 Evaluate the method-specific diagnostics and gates returned by the function, then abstain unless the declared decision clears them under locally governed thresholds.

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": "allocate discrete shared capacity by weighted" }
  → finds "allocate_capacity_nash_bargaining"

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

gitrevio_capability_run
  { "capability_id": "allocate_capacity_nash_bargaining", "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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