Litigation Risk Management is the discipline of applying decision tree risk analysis to litigation. Dr. Bruce Beron, the founder and President of the Litigation Risk Management Institute has been assisting Fortune 500 companies and startups by applying these tools to complex, high-stakes litigation for over thirty years. He has also spent three years as the Chief Investment Officer of a third party litigation funding startup and is intimately familiar with the issues of case selection and settlement.

The Litigation Risk Management approach to valuing litigation for investment goes well beyond the simple assignment of a probability to liability and making a “conservative” or “realistic” estimate of damages. It is an iterative process. Analysis is started very simply and elaborated only as necessary until further refinements or complications do not change the results of the analysis or improve its quality. All relevant issues are considered, either explicitly or implicitly. Legal and factual matters are included, but only insofar as they will affect the likelihood of the findings of the trier-of-fact. Generally, the analysis will focus on the probability of liability and the expected range of damages if liability is established. Sometimes they are logically dependent, but often they are independent, although almost always linked probabilistically.

The process produces a case value that is both robust and defensible. Analysis reveals which issues are most critical to that value, both for purposes of further due diligence and for ongoing monitoring as the case progresses. Results are presented as an expected or mean value of the case and a probability distribution on recovery, either from the plaintiff’s perspective or that of the funder. The primary product of such an analysis is a communicable, defensible appreciation that the analysis captures the uncertainty in the simplest, most direct way possible.

In addition to being invaluable for investment decisions, these tools are extremely useful in determining the appropriate settlement value. Given that the funder has a diversified portfolio and should be willing to play the odds, any settlement offer meaningfully less than the expected value of the case, updated appropriately of course, should be rejected and the case tried. There are issues around how the funder can influence the settlement decision given that it does not control the case, but we have developed means to this end.

Beyond the decision tree analysis, when settlement negotiations are imminent or have already begun, our Negotiation Strategies services, the application of a combination of decision and game theory can forecast the outcome of the negotiation with uncanny accuracy, as well as produce a strategy to maximize the possible outcomes. We typically save defendants 30-40% beyond what the attorneys think is the best possible settlement, and see increases of 10-20% for plaintiffs.

We would be glad to provide further information on our services at your request .

I am constantly reading disparaging comments about how the probabilities used in decision tree analysis of litigation are “mere estimates”, and garbage in, garbage out, etc. The probabilities we use are not the same as the classical statistical frequencies which are used, for example, to deal with a tossed die or a flip of a coin. The probabilities we use are our quantified gut feel to express our understanding of how likely an event is or will be. These are almost always one time events. We can’t try the case 100 times and see how many times we would be liable.

The reasons we use numbers to express these judgments about the likelihood of future events are twofold. First, numbers are unambiguous. Any phrase you might use to describe the likelihood of a ruling or trial outcome is ambiguous. To some people “Very likely” means a probability from .8-.9, for others, the same phrase will be interpreted to mean .6-.7. The second reason we use numbers, is that we can used well known and understood rules of probability to combine these judgments to come to an overall conclusion. Any real case has at least several events that must be assessed to come to an overall conclusion. What is the likelihood of having to succeed on three events where the likelihoods of success are given as “Very Likely”, “Somewhat likely”, and “Likely?” However, if I tell you the likelihoods are .8, .4, and .3, the overall probability of success is .096, or .1 for all practical purposes.

When we talk about probabilities, there are two kinds of variables we are trying to describe. The first are the likelihood of discrete events. By discrete events, I mean events that have a finite (usually less than 5, more commonly 2 or 3) number of possible outcomes. The objective is to assign a probability to each of the possible outcomes. This is a rather straightforward process that has been described in detail elsewhere (ref Carl article, mention I can email pdf). Examples of such variables are the motion to dismiss is granted, we are found liable, damages are trebled, pre-judgment interest is included, etc. I find that is most useful to structure the decision tree, to the extent possible, using two branch nodes, only two possible outcomes. There are two reasons for this. First, it is much easier to assess yes/no events, and second, it is straight forward to do sensitivity analysis for such events/nodes in the tree. More complex (i.e. more outcomes) can be modeled by using sequential two branch nodes.
The second type of probabilities relate to issues where the event we are trying to assess is a numerical outcome. We call these continuous variables. These are outcomes such as the amount of damages, a date, an interest rate etc. These outcomes are described by continuous probability distributions. However, in order to include them in the tree, we must approximate them by a discrete number of branches, for example – High, Medium and Low. I have a slideshow (see Probability Assessment: How Do We Get “Good” Numbers For The Analysis? under Slideshows) that discusses this in more detail, but the basic questions that need to be asked are in the form of: “How likely is it that the jury will find damages greater than $15M?” After several of these questions are answered, you can draw a probability distribution and approximate it with a 3 branch node.

When we discussed the coin toss, the 50% probability of heads or tails was obvious (assuming, of course, a fair coin). Suppose now that I flip a coin and cover it up with my hand so that you can’t see it, but I peek at it. What is my probability that the coin is heads? It’s either 0 or 1 (depending on whether the coin landed tails or heads). What is the probability that the coin is heads (you still haven’t seen it!)? 50%! How do we explain the apparent discrepancy between the same coin toss and different probabilities? The probabilities represent a quantification of a state of knowledge and judgment. You and I have different states of knowledge about the coin, therefore we have different probabilities. Here we are using Bayesian statistics, not the classical statistics that most of us have been taught; the latter deal only with the frequency with which the coin will land heads or tails, not its state on any one toss.
Unlike the coin, for which there is an observable frequency that we could measure by tossing it many times and counting the number of heads and tails, each litigation case happens only once, and we can never measure a frequency for the particular trial under consideration. The probabilities represent the best judgment, knowledge, and experience that we can bring to bear on the particular uncertain outcome. There is no correct probability. If we asked someone who could foretell with perfect accuracy the outcome of the trial or of any single issue, “Will we win?” the answer will be a “yes” or “no”, not a probability. This is a very important point. A probability is correct only to the extent that it accurately represents the state of knowledge and judgment of the person being asked.

There are, however, well-known biases in the way we think about probabilities and uncertain outcomes ⚖. The most important of these is the tendency to think we know more than we do. We make our probability distributions too narrow for our true state of knowledge. This has been demonstrated in hundreds of tests in which executives and professionals were asked to encode their own probability distribution on knowable quantities (e.g., the air distance from Moscow to Beijing). They were asked to set outer limits on their distributions so that there would be only a 2% chance that the correct answer would lie outside their limits. In fact, the correct answers fall outside their limits about 50% of the time. The world is much more uncertain than we would like to think it is! Fortunately, techniques have been developed to counteract such biases,⚖ and they are straightforward to use.
The question remains, of course, whether or not a particular person is a good judge of an issue. We all have good intuitive ideas about who the best experts are on particular questions, but by encoding probabilities we can calibrate their judgments quite readily.

What we are trying to do with probabilities is to pick the best decision, the one with the highest expected or average outcome. In the face of uncertainty, that is the best we can do. In fact, when we make a decision without doing a decision tree, we are implicitly assigning probabilities. It makes much more sense to do the best job we can assessing the probabilities of all the critical events that determine the expected outcome and pick the best alternative.
⚖ Please request supplemental articles by using the Contact link and we will email them to you.

In love and in IP warfare, timing may not be everything, but it can be very important.

I recently consulted on an IP war between two companies. My client had had a significant bad outcome on the defense in the first case to get to trial. Our analysis of the seven ongoing cases showed that with the back and forth in the remaining cases, they would get to a total expected dollar break even in five years or so. The last case, in which the client is the plaintiff, is, on an expected value basis, a big win. Given the recent big win for the other side and the very distant threat of a significant loss, they were not inclined to negotiate an overall settlement and so all of the litigation and the accompanying costs are continuing.

That final case expected big win for the client as plaintiff had been delayed on our appeal of a pretrial ruling that, while not disastrous, was not favorable. However, pursuing that appeal before trial postponed the ultimate resolution (if there is such a thing) of the case off by at least 18 months. Our analysis showed that, again on an expected value basis, winning the appeal  added about 20% to the value of the case, not insignificant. However, foregoing the appeal and speeding up the schedule might have moved the other side to negotiate an overall settlement. We could have analyzed that decision, but unfortunately it was made well before we were consulted and at that point, there were only a few months to be gained which wouldn’t have changed anything.

Take-away: When engaged in an IP war, while it is important to maximize the value of each case, timing issues – often made apparent by an analysis of all the cases – can lead to different decisions in individual cases.

One of the fundamental bases of our approach to assisting our clients to make better litigation decisions is the distinction between decisions and outcomes. A decision is something over which you have control: What issue you will raise in your pleadings; how much you will spend on the case; what you are willing to settle for. Outcomes, on the other hand, are things over which you do not have control: The judges ruling on particular motions; the jury’s liability and damage findings; the minimum /maximum amount the opposing side is willing to settle for. That’s not to say that your actions can’t influence the likelihood of various outcomes, but you do not have direct control over them.

If you judge the quality of counsel’s or colleague’s work on the basis of outcomes, the message you are sending is that they should spend every last possible dollar and do everything in their power to reduce the probability of a bad outcome. In fact, what you really want them to do is maximize the expected or probability weighted average outcome. And the best way to do that is to analyze the decisions using decision analysis (decision tree analysis) using probabilities that reflect the best available judgment, knowledge and experience.

For example, if you judge outside counsel on a defense case by the outcome -“Lose this case and you’re history.” – they will naturally exert every possible billable effort to minimize the possibility of losing the case (whether or not any particular expenditure is worth it) and then, on the courthouse steps, recommend a settlement, thus guaranteeing that there won’t be a really bad outcome.

The alternative is to analyze the case using decision trees and probability inputs from the appropriate attorneys and business people, see how sensitive the expected or average value of the tree is to various inputs and assumptions, and determine the value of the case. That value is your reservation price. If you are a defendant, then any settlement that is less than that value plus the expected remaining litigation costs is a good settlement. If the case can’t be settled for less than that, take it to trial understanding that there is an explicit probability of losing, and that going to trial is the right decision. The decision to try the case is then a collaborative effort among outside and inside counsel and management.

All have agreed, before they know the outcome, that it is the right decision. This minimizes second-guessing and finger pointing after the fact. And, in the long run, this collaborative process saves you money and the aggravation of worrying about things you can’t control. The litigation is being conducted on a business-like basis which can be clearly communicated to all the stakeholders, in particular, to senior management. It also has the benefit of unambiguously conveying to them what they need to hear, not what they want to hear.