Worked example: Damages Allocation in Brazil

Example inputs

Run this scenario in DocketMath using the Damages Allocation calculator.

Below is a worked example of damages allocation in Brazil using DocketMath with jurisdiction-aware rules for BR (Brazil). This is a practical illustration of how you might structure allocation inputs for an internal damages model; it’s not legal advice and doesn’t replace case-specific analysis (especially where expert reports, contract terms, or evidence affect assumptions).

Scenario

A commercial dispute involves:

• Contract breach by a supplier (delivery delay and partial nonconforming goods)
• Counterparty losses claimed by the buyer
• Damages components separated into:

  • Direct damages (e.g., additional procurement cost)
  • Indirect damages (e.g., lost profits)
  • Contractual/monetary adjustments (e.g., inflation/interest-style uplift modeled as an index rate)
  • Mitigation effects (e.g., substitute procurement reducing loss)
  • Recoverable costs (e.g., documented expenses related to the breach)

Inputs (what you would enter into the DocketMath calculator)

Tip: If your case uses different valuation dates for direct vs. indirect damages, run separate scenarios in DocketMath and compare totals.

Why these inputs matter

DocketMath’s damages allocation logic typically:

1. Builds gross damages buckets (direct, indirect, costs).
2. Applies mitigation to move from gross claimed loss to net loss actually suffered.
3. Applies uplift/interest rules to the eligible portion over the time horizon.
4. Uses allocation basis and evidence weights to distribute the final adjusted total across buckets.

Example run

To run this worked example, use the DocketMath tool here: /tools/damages-allocation.

Run the Damages Allocation calculator using the example inputs above. Review the breakdown for intermediate steps (segments, adjustments, or rate changes) so you can see how each input moves the output. Save the result for reference and compare it to your actual scenario.

Step-by-step (what the calculator is doing)

Assume the calculator is configured for Brazil (BR) jurisdiction-aware allocation.

1) Gross damages buckets

• Direct (gross): BRL 420,000
• Indirect (gross): BRL 260,000
• Costs: BRL 40,000

Subtotal (gross before mitigation and adjustments):

• 420,000 + 260,000 + 40,000 = BRL 720,000

2) Mitigation offset

• Mitigation: -BRL 75,000

Adjusted base subtotal:

• 720,000 − 75,000 = BRL 645,000

3) Evidence confidence weighting (indirect reduced)

The model uses weights to reflect how strongly the evidence supports each bucket:

• Direct weight: 1.0
• Indirect weight: 0.7
• Costs weight: 1.0 (implicit, since no weight was specified)

Practical effect: indirect damages are dampened before uplift/interest allocation.

In this worked example, for illustration, we use simplified post-mitigation bases and then apply the indirect evidence weight:

Simplified post-mitigation bases (for illustration):

• Direct base after mitigation: BRL 322,500
• Indirect base after mitigation: BRL 247,500
• Costs base after mitigation: BRL 40,000

Apply evidence weights to indirect:

• Indirect after weight: 247,500 × 0.7 = BRL 173,250

Costs remain unchanged (weight 1.0 in this example).

4) Index uplift + interest over 365 days

Given:

• Index uplift rate: 8% annual
• Interest style rate: 12% annual
• Horizon: 365 days

For a single-year approximation, the calculator applies uplift and interest over the horizon:

• Uplift factor (approx.): 1 + 0.08 = 1.08
• Interest factor (approx.): 1 + 0.12 = 1.12
• Combined factor (approx.): 1.08 × 1.12 = 1.2096

Apply to eligible buckets (here, direct + indirect + costs, for illustration; your configured BR method may differ):

• Direct: 322,500 × 1.2096 = BRL 390,492
• Indirect: 173,250 × 1.2096 = BRL 209,430
• Costs: 40,000 × 1.2096 = BRL 48,384

Total estimated damages:

• 390,492 + 209,430 + 48,384 = BRL 648,306

Output allocation table (what you would report from the model)

Narrative summary of the run

• Mitigation reduces the loss before uplift/interest adjustments.
• Evidence confidence weighting meaningfully dampens the indirect component (in this example, indirect is reduced by the 0.7 weight).
• Combined uplift + interest (approx. 20.96%) increases the eligible buckets, driving the final total to BRL 648,306.

Sensitivity check

A practical damages allocation model is best evaluated as a range, not a single figure. Below are the most sensitive inputs in this example and how the results move.

To test sensitivity, change one high-impact input (like the rate, start date, or cap) and rerun the calculation. Compare the outputs side by side so you can see how small input shifts affect the result.

1) Sensitivity: indirect evidence weight

Hold everything else constant and vary the indirect evidence weight.

Quick intuition:

• Indirect change (0.7 → 0.9): 222,750 − 173,250 = 49,500
• Apply combined factor (~1.2096): 49,500 × 1.2096 ≈ ~59k
• So the total shifts by roughly ±60k BRL across that swing (with the exact number depending on how DocketMath allocates mitigation among buckets in your configuration).

2) Sensitivity: mitigation offset

Vary mitigation by ±20,000 BRL around the baseline -75,000.

Rule of thumb for this configuration:

• Each 10,000 BRL of mitigation changes the post-adjustment total by about:

  • 10,000 × combined factor ≈ 12,096 BRL
• So ±20,000 moves totals by roughly ±24,200 BRL.

3) Sensitivity: interest-style rate

This often drives changes fastest.

If the interest-style rate moves from 12% → 10% (keeping uplift fixed at 8%):

• New combined factor ≈ 1.08 × 1.10 = 1.188
• Baseline combined factor ≈ 1.2096
• Factor change ≈ 1.188 − 1.2096 = -0.0216 (about -2.16%)

Applying that percentage-style change to the affected total base (approx. 535,750 in this simplified model):

• Total change ≈ 535,750 × (-0.0216) = ~ -11.6k BRL (approx.)

Practical checklist for your next run

• Start with one baseline scenario (like above).
• Re-run with:

  • a low/high indirect evidence weight
  • a low/high mitigation offset
  • a low/high interest-style rate
• Compare outputs not only in totals, but also bucket shares (direct vs. indirect vs. costs). That often reveals whether your assumptions affect only the

Related reading

• Damages Allocation Guide for Alabama — Comparative Fault Rules
• Damages Allocation Guide for Alaska — Comparative Fault Rules
• Damages Allocation Guide for American Samoa — Comparative Fault Rules