casesim-settlement-vs-trial-ev-calculator
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name: casesim-settlement-vs-trial-ev-calculator
description: Use when an attorney or client needs to compare the expected value of accepting a settlement offer against the expected value of proceeding to trial — incorporating P(win), damages range, costs to trial, loser-pays rules, discount rate, and strategic or reputational factors. Produces an EV comparison (trial vs. settlement), a recommendation (take/counter/decline), and a sensitivity analysis showing which assumptions matter most. Always disclaimed as a probabilistic estimate. Always paired with the outcome probability estimator.
license: MIT
metadata:
id: casesim.settlement-vs-trial-EV-calculator
category: casesim
jurisdictions: [multi]
priority: P1
intent: [settlement, ev, client-counseling, litigation-strategy]
related: [casesim-outcome-probability-estimator, casesim-opposing-counsel-simulator, casesim-judge-bench-perspective, casesim-client-q-and-a-prep]
source: Louis — HAQQ Legal AI (github.com/sboghossian/mini-claude-for-legal)
version: "1.0"
Settlement vs. Trial EV Calculator — Should You Take the Offer?
When to use this
Invoke when:
- Opposing counsel has made a settlement offer and the client asks "should we take it?"
- A litigation team is preparing for a settlement conference and needs an internal floor / ceiling analysis
- A client's board asks for a "go / no-go" recommendation on litigation versus early resolution
- An attorney needs to explain the settlement vs. trial decision in quantitative terms to a client who wants numbers
- A funding decision for continued litigation needs to be made
This skill works alongside [[casesim-outcome-probability-estimator]], which generates the P(win) and damages range inputs. Use both together for a complete client counseling package.
Inputs
| Input | Required | Notes |
|---|---|---|
| Settlement offer amount | Yes | The net figure the client would receive (or pay) |
| P(win at trial) | Yes | From [[casesim-outcome-probability-estimator]] or attorney's own assessment |
| Damages range at trial (low / mid / high) | Yes | If liability established, what would the court award? |
| Costs to trial (own side) | Yes | Estimate to judgment; separate from costs already spent |
| Loser-pays rule | Yes | Does the forum apply loser-pays (English rule) or each side bears its own costs (American rule)? |
| Discount rate | Optional | Default: 5–8% depending on jurisdiction and client risk profile |
| Time to judgment | Optional | Months from today to expected judgment date |
| Strategic / reputational factors | Optional | Confidentiality value, business relationship, precedent-setting risk, management distraction |
| Appeal probability | Optional | Will opposing counsel appeal a first-instance win? |
Calculation methodology
EV(Trial) — Expected Value of Going to Trial
EV(Trial) = [P(win) × E(award | win) - Costs(own)]
+ [P(lose) × -Costs(own)]
± [Loser-pays adjustment]
× [Time discount factor]
Where:
- E(award | win) = probability-weighted average across the low / mid / high damages scenarios
- Costs(own) = estimate of legal fees and disbursements to trial
- Loser-pays adjustment:
- If win: add recovery of opponent's costs (partial — typically 60–70% recovery in English-rule forums)
- If lose: subtract payment of opponent's costs
- Time discount: apply the discount rate over the expected time to judgment
Example:
| Variable | Value |
|---|---|
| P(win on liability) | 65% |
| Expected award (mid scenario) | USD 800,000 |
| Costs to trial (own) | USD 120,000 |
| Loser-pays rule | English rule (DIFC) |
| Opposing costs if lose | USD 100,000 |
| Discount rate | 6% |
| Time to judgment | 18 months |
EV(Trial) = [0.65 × (800,000 + 0.65 × 100,000) - 120,000]
+ [0.35 × -(120,000 + 100,000)]
discounted at 6% over 18 months
≈ [0.65 × 865,000 - 120,000] + [0.35 × -220,000] × 0.915
= [562,250 - 120,000 - 77,000] × 0.915
= 365,250 × 0.915
≈ USD 334,200
EV(Settlement) — Expected Value of Accepting Settlement
EV(Settlement) = Settlement amount - Costs already spent (sunk) + [Optional: value of strategic benefits]
Note: sunk costs do not affect the forward-looking decision. The only relevant comparison is future EV(Trial) against the net benefit of settlement from today. Costs already spent are the same whether the client settles or litigates; they do not change the calculus.
However, where the client is framing the question as "what do I net from this whole matter?" — include sunk costs in the total cost presentation (not in the EV comparison but in the client narrative).
Net Comparison
Settlement advantage / disadvantage = EV(Settlement) - EV(Trial)
- Positive: settlement is worth more (in expected value terms) than going to trial
- Negative: trial has higher expected value; settlement offer is below fair value
- Near zero: the decision turns on strategic / non-quantitative factors
Sensitivity analysis
The most important output after the EV comparison is the sensitivity analysis: which assumption matters most?
Louis generates a table showing how the recommendation changes as key variables shift:
| Variable | Base case | Sensitivity test | Effect on recommendation |
|---|---|---|---|
| P(win) | 65% | 55% / 75% | At 55%, settlement more attractive; at 75%, trial clearly preferred |
| Costs to trial | USD 120k | USD 80k / USD 180k | Higher costs make settlement more attractive |
| Time to judgment | 18 months | 12 months / 30 months | Longer time = greater discounting = settlement more attractive |
| Award (mid scenario) | USD 800k | USD 600k / USD 1M | Affects the upside case significantly |
This tells the attorney: "The decision is most sensitive to P(win). If we have reason to believe our win probability is above 70%, we should push back on this offer. If it's below 60%, the settlement looks fair."
Recommendation framework
Based on the EV calculation and sensitivity analysis, Louis outputs one of:
TAKE — settlement offer exceeds or approximates EV(trial)
- Recommended when: settlement is within 10–15% of EV(trial) and strategic factors favor resolution
- Key message to client: "The offer is fair value. The certainty of settlement is worth the small discount versus the expected trial outcome."
COUNTER — settlement offer is below EV(trial) but trial is not clearly superior
- Recommended when: settlement offer is 20–40% below EV(trial) but costs, time, or strategic risks make trial unattractive
- Key message to client: "Push back. Make a counter at [X], which reflects a reasonable settlement range. Trial is an option but not our preference given [costs / timing / relationship]."
DECLINE — EV(trial) materially exceeds the settlement offer
- Recommended when: settlement offer is more than 40% below EV(trial) and the case is strong
- Key message to client: "This offer does not reflect the value of your claim. Proceed to trial unless opposing counsel substantially improves their position."
Strategic and non-quantitative factors
The EV calculation captures the financial dimension. These factors may change the recommendation:
| Factor | Effect |
|---|---|
| Confidentiality | If reputational or commercial harm from a public trial is material, settlement has a premium beyond its financial value |
| Business relationship | Settlement may preserve a commercial relationship that has ongoing value |
| Precedent / principle | A public judgment may be worth litigating for even if the EV is comparable — signals to other potential claimants or deters future breach |
| Management distraction | Litigation absorbs executive time; cost is real but hard to quantify |
| Counterparty financial risk | Is the counterparty likely to be able to pay a judgment? An insolvent win has limited value |
Mandatory disclaimer
Every output of this skill must include: "This expected value analysis is a probabilistic planning tool, not a prediction of actual outcomes. Legal proceedings involve inherent uncertainty. All assumptions should be reviewed with qualified legal counsel before making any decision."
Related skills
- [[casesim-outcome-probability-estimator]]
- [[casesim-opposing-counsel-simulator]]
- [[casesim-judge-bench-perspective]]
- [[casesim-client-q-and-a-prep]]