Improving Legal Case Outcomes with @RISK and PrecisionTree, Part ll

Decision Trees Explained

In our first post, we discussed how presenting objective, realistic models that show all possible scenarios helps manage expectations early in the litigation process, setting the stage for productive settlement discussions. In this post, we will be delving into why patent litigation is a particularly expensive process and walk through an example of a litigation decision tree.

On average, it takes 2.4 years for patent cases to complete the full litigation process and only 2-5% of patent cases reach the pre-trail and trial phase, according to James C. Yoon, partner at Wilson Sonsini Goodrich & Rosati. This drawn-out process leads to the accumulation of significant legal expenses.

Yoon noted that the total cost of patent litigation depends on the potential range of damages. For cases that go through trial, those with less than $25 million “at risk” total $2.6 million in expenses and cases with more than $25 million “at risk” amount to $5.5 million in expenses.

As covered in our previous legal blog series, the dynamic nature of patent litigation and the potential for significant financial loss are two main reasons why firms often get entangled in lengthy negotiations.

To lay the groundwork for swift settlements, many legal firms use PrecisionTree software to map out all possible routes a litigation path can take by building quantitative, probabilistic decision trees that calculate case outcomes along the way. This decision-making software can calculate for risks and rewards, forecast case budgets, identify key investigation areas, and create impartial, realistic models to cut down on time and costs.

Since PrecisionTree works within Microsoft Excel, it allows for great flexibility in defining decision model nodes and functions and may be added to any cell in your existing spreadsheets. Furthermore, PrecisionTree is designed to work with Palisade’s @RISK and TopRank software, allowing for the creation of the most accurate decision tree models anywhere.

Dive deeper into litigation decision trees with the example below.

Litigation Decision Tree Example

Decision Tree example in litigation
Figure titled “Baby Tree” from previous legal blog series, The Economics of Litigation.

A decision tree, like the example “Baby Tree” above, is read from left to right with each new decision or event occurring at the square decision node. For this litigation decision tree, the two options are to litigate or settle.

To the right of the Litigate decision branch, you have a chance node represented by a circle. Chance nodes represent uncertainties which are assigned probabilities of an event occurring. In this figure based on case win rate data, there is a 58% chance of winning and 42% chance of losing the case. These probabilities are represented under the Win and Lose decision branches. All paths through the decision tree conclude with a final outcome, represented here with the triangle. The final outcome for litigating in this figure is a $14 million judgement and was based on past average award data. This is shown in the Money Received column to the right of the figure.

We know that the settlement offer was $500,000 which is shown in the box by the Settle decision branch. To determine the expected value of litigating, we work backwards from the $14 million judgement. First, we’ll calculate net payoff by subtracting the $2 million in legal fees from the money received for winning and losing the case. Then we’ll multiply net payoff for winning by the probability winning the case ($12 million x .58 = $6,960,000) and do the same for the Lose decision branch (($2 million) x .42 = (840,000)). We add these together to find the expected value of litigating which is $6.1 million. Since the result of $6.1 million is greater than the settlement value, the plaintiff should choose to continue litigating.  

As shown in this basic example, the first goals when creating a decision tree should be to show the major choices, potential following event, probabilities these events could occur, and the result of the events.

In the next series post, we’ll discuss common decision analysis challenges and explore how Palisade’s @RISK software helps determine fair market value of intellectual property.

At Palisade, the makers of PrecisionTree, @RISK, and other leading risk and decision analysis solutions, we provide cost-effective, easy-to-use analytics to companies who want to make better decisions. To learn more about how we can help you speed up negotiations, determine fair market value of IP, or even solve a different challenge you might be facing, check out the first post of this blog series or request a free trial of @RISK and PrecisionTree, included in the DecisionTools Suite.

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