Optimize financing terms nash bargaining

What it's for

Moves financing negotiation beyond headline valuation to a visible founder–investor Pareto frontier and individually rational, downside-aware bargaining reference point.

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.

{
  "founder_reservation_value": 100,
  "investor_reservation_value": 100,
  "minimum_company_net_cash_proceeds": 150,
  "minimum_founder_control_fraction": 0.5,
  "minimum_investor_expected_moic": 1.2,
  "scenarios": [
    {
      "id": "downside",
      "probability": 0.5
    },
    {
      "id": "upside",
      "probability": 0.5
    }
  ],
  "tail_probability": 0.5,
  "term_sheets": [
    {
      "company_net_cash_proceeds": 200,
      "evidence_verified": true,
      "founder_control_fraction": 0.6,
      "founder_net_payoff_scenarios": [
        100,
        300
      ],
      "id": "higher-control",
      "investor_net_payoff_scenarios": [
        120,
        180
      ],
      "new_investor_investment": 100
    },
    {
      "company_net_cash_proceeds": 250,
      "evidence_verified": true,
      "founder_control_fraction": 0.55,
      "founder_net_payoff_scenarios": [
        150,
        230
      ],
      "id": "balanced",
      "investor_net_payoff_scenarios": [
        140,

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.

{
  "configuration": {
    "founder_bargaining_weight": 0.5,
    "founder_reservation_value": 100,
    "founder_tail_risk_weight": 0,
    "investor_reservation_value": 100,
    "investor_tail_risk_weight": 0,
    "maximum_founder_reservation_shortfall_probability": 1,
    "maximum_investor_capital_loss_probability": 1,
    "minimum_company_net_cash_proceeds": 150,
    "minimum_founder_control_fraction": 0.5,
    "minimum_investor_expected_moic": 1.2,
    "tail_probability": 0.5
  },
  "decision": "financing_terms_nash_bargaining_solution_supported",
  "guardrails": [
    "Nash bargaining is evaluated only after liquidity, control, return, downside, evidence and individual-rationality constraints. The disagreement values and bargaining weight are governed stakeholder inputs, not facts inferred from title, wealth, hierarchy or negotiating behavior.",
    "Founder and investor payoffs must be full-cost outputs of the same versioned cap table and coherent exit scenarios. Independently sorted payouts, headline post-money valuation, or omitted liquidation rights manufacture a false bargain.",
    "The selected term is a transparent analytical reference point, not legal, tax, securities, fiduciary, valuation or investment advice and not authority to accept or reject financing."
  ],
  "method": "risk_adjusted_pareto_and_weighted_nash_financing_bargaining",
  "pareto_frontier_term_sheet_ids": [
    "balanced",
    "higher-control"
  ],
  "selected_term_sheet": {
    "company_net_cash_proceeds": 250,
    "expected_founder_net_payoff": 190,
    "expected_investor_net_payoff": 180,
    "failed_constraints": [],
    "founder_control_fraction": 0.55,
    "founder_lower_tail_cvar_payoff": 150,
    "founder_reservation_shortfall_probability": 0,
    "founder_risk_adjusted_value": 190,
    "founder_surplus_over_reservation": 90,
    "investor_capital_loss_probability": 0,
    "investor_expected_moic": 1.8,
    "investor_lower_tail_cvar_payoff": 140,
    "investor_risk_adjusted_value": 180,
    "investor_surplus_over_reservation": 80,
    "log_nash_bargaining_score": 4.4409,
    "term_sheet_id": "balanced"
  },
  "summary": {

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 Freeze coherent scenario probabilities and full-cost founder/investor net payoff vectors for each counsel-approved candidate term sheet, plus net company cash, founder control and investor capital.
2. 2 Compute expected and lower-tail-CVaR payouts, risk-adjusted values, reservation surplus, expected investor MOIC and founder/investor downside probabilities; reject terms failing liquidity, control, return, downside, evidence or individual-rationality constraints.
3. 3 Construct the exact nondominated founder–investor risk-adjusted frontier and select the feasible term maximizing the weighted log Nash product, using governed reservation values and bargaining weight rather than titles, hierarchy or inferred negotiating power.

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": "select financing terms through exact riskadjusted" }
  → finds "optimize_financing_terms_nash_bargaining"

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

gitrevio_capability_run
  { "capability_id": "optimize_financing_terms_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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