Mediation preparation for both the plaintiff’s attorney and the defendant’s attorney rests on an ability to determine the litigation value of the case at issue. The typical approach to this task has its roots in what aspiring litigators learn in their first year of law school: Discern the facts and the applicable law to determine the strength of the plaintiff’s case. From that determination predict the outcome of a potential trial of the case. According to the conventional wisdom, this approach works well. Empirical research on the topic demonstrates consistently, however, that the case outcome predictions of litigators, irrespective of experience level, are no better than predictions generated by using the toss of a fair coin. Because clients rely on their lawyer’s estimate of the value of the case, estimates based on poorly calibrated outcomes have the potential to result in unwise decisions, including settlement decisions. As a corrective to this reality, I describe a decision analysis approach that any litigator can use to estimate the value of a case and the probability of winning said amount at trial.

To begin, it is necessary to understand the concept of expected value. A simple example should suffice. Suppose I can purchase a lottery ticket for $10. If mine is the winning ticket, I will receive $10,000. At the same time, there are 10,000 such tickets. Should I pay $10 for a lottery ticket? I am aware the odds of having the winning ticket are not in my favor: 9,999:1 against me. Still, maybe I have a hunch that I will draw the winning ticket. After all, in my mind, my hunches typically pan out. Certainly, that’s a common approach to decision making. Indeed, it’s not unknown for litigators to assert, “I have a gut feeling about this case.” In contrast, decision analysis doesn’t rely on hunches or gut feelings. Instead, it relies on recognizing that as a general matter, we humans live in a probabilistic world. Lotteries are a prominent example of that world.

Accepting the probabilistic nature of, for example, a lottery allows us to generate something referred to as an expected value to help inform decision making. The expected value of the hypothetical lottery described above is the product of two quantities: the probability of having the winning lottery ticket and the monetary value of that ticket. In the example, the quantities are 1/10,000 and $10,000, the product, i.e., expected value, of which is (1/10,000) x $10,000 = $1. The question for me is, “Should I spend $10 for a lottery ticket which has an expected value of $1?” Before I can answer, it would be useful to know more about what an expected value represents.

The $1 expected value for the lottery does not mean that if I spend $10 to purchase the ticket, I should expect to win $1. Instead, $1 represents my average winnings if I were to play the exact same lottery many, many times. Stated another way, if I were to play the exact same lottery tens of thousands of times, I could expect to win $10,000, on average, one out of ten thousand times. Of course, to play the lottery 10,000 times would cost me $10/per time x 10,000 times = $100,000. Consequently, I would choose not to purchase a lottery ticket given that reality.

Decision analysis can be a useful aid to both parties as they prepare for a mediation session. Imagine a scenario in which a plaintiff has alleged that her former employer discriminated against her on the basis of sex when it terminated her employment. According to the plaintiff, she can demonstrate that, as of the date of the mediation, she has lost $50,000 in the form of wages and benefits as the result of the termination. In addition, she contends she has suffered emotional distress damages in the range of $15,000 to $50,000. In preparation for the mediation the plaintiff’s attorney generates an estimate of the litigation value of the case. The litigation value is the expected monetary value to the plaintiff of proceeding to trial. Again, under the “traditional approach” to determining that value the plaintiff’s lawyer would ascertain the facts and their intersection with the applicable law. Stated another way, the lawyer would attempt to answer the question “How likely are we to be able to prove what we need to prove?” The answer to the question would depend on the lawyer’s assessment of the fact/law intersection.

Increasingly in recent years, numerous negotiation scholars have suggested litigators would do well to apply decision analysis to generate estimates of litigation value. For the plaintiff’s lawyer, application of decision analysis to the scenario above would require estimating (a) the probability of the plaintiff’s prevailing at trial and (b) the monetary recovery the plaintiff would receive if she were to win. For sake of illustration, suppose that at the low end the plaintiff would recover $50,000 in lost wages and at the high end she would recover $50,000 in lost wages plus $50,000 in emotional distress damages. The mid-point in the range of possible recoveries would recover comprise $50,000 in lost wages plus $25,000 in emotional distress damages. To generate a litigation value, it would be necessary to estimate probabilities for each of these three possible recovery amounts. The obvious question, then, is how to generate those estimated probabilities.

As a first step it is important to note that calculating an expected, i.e., litigation, value is different from determining an expected value in the lottery problem described above. In the lottery case, the applicable probability is objective. “Objective” means that the applicable probability is not a matter of belief. Again, if there is one winning lottery ticket among 10,000 tickets in circulation, and if there are numerous repeated draws of the winning ticket under the exact same conditions on each draw, over time any given ticket will have a .0001 probability of being the winner. In the litigation context we cannot generate a similar estimate. The reason for that limitation is straightforward. Again, to estimate the probability of having the winning lottery ticket, we can rely on the fact that the drawing of a winning lottery ticket can be performed in exactly the same manner numerous times. In the litigation context, however, every trial is a unique activity, unlike the drawing of a winning lottery ticket or, for example, the flip of a fair coin. Consequently, any probability estimate of the plaintiff’s prevailing at trial will not be objective. Instead, necessarily it will be subjective — a matter of belief.

Under the traditional approach to estimating litigation value, litigators rely on their beliefs regarding the probability of the plaintiff’s prevailing at trial. As the research noted above tells us, that method is not well calibrated. For that reason alone, litigators are susceptible to providing poor decision-making advice to their clients as to whether to settle. So, what can litigators do to improve their ability to predict trial outcomes and make better informed estimates of litigation value?

Numerous decision-making scholars advocate that decision makers make use of base rates to inform probability estimates. In simple terms, a base rate is the overall rate at which a given event occurs. Whether we recognize it, all of us rely on base rates to inform our decisions every day. Should I drive my car to the grocery store? I know there is a non-zero probability doing so could result in my being in an accident. At the same time, I am aware that accidents in those circumstances are infrequent, on average. Of course, my trip will be unique and not analogous to the drawing of a lottery ticket. Still, the base, i.e., average, rate for accidents for trips like mine is low. Consequently, at least implicitly, I will rely on that base rate to inform my decision to drive to the grocery store. In the context of the litigation scenario above, if I know the base rate for plaintiffs’ prevailing at trial in, say, employment cases, I can use that rate to estimate litigation value my employment case.

Suppose, for example, that the base rate for plaintiffs’ prevailing at trial in employment cases in the federal courts is .50. Indeed, over the period January 1, 2001, through March 28, 2023, employment plaintiffs in trials in the federal courts have prevailed 50.1 percent of the time. Further, suppose I estimate that if the plaintiff prevails at trial, she will recover $50,000 with a .25 probability, $75,000 with a .50 probability and $100,000 with a .25 probability.

The resulting estimated litigation value would be:

.50 x [(.25 x $50,000) + (.50 x $75,000) + (.25 x $100,000)] = $34,375

Again, the expected value of a lottery ticket is not the value I would receive, or expect to receive, if I purchased a ticket. Similarly, the litigation value of a case is not the monetary value that the plaintiff could expect to receive if she went to trial. Regardless, in both instances utilizing a base rate helps to inform the decision maker’s decision process. As the potential purchaser of a lottery ticket, I rely on the base-rate-informed expected value to tell me I shouldn’t play the lottery. Similarly, the hypothetical employment discrimination plaintiff can utilize base rate information to help her decide whether to settle or proceed to trial. While each case that goes to trial is unique, very few cases are outliers. Instead, most cases are, by definition, like each other in terms of the rate at which plaintiffs prevail, the base rate for the class of cases of which the subject case is a member.

Rick Gautschi spent over five decades as teacher, social scientist, litigator, and now mediator at Pacific ADR. Rick has extensive employment litigation experience, including class action matters, primarily having represented employees. His business background however, (MBA and Ph.D in business administration) enables him to communicate effectively with employers. Rick currently teaches Negotiation at the UW School of Law one of several almae matres. These practical skills are directly interlinked with Rick’s successes as a Mediator and Arbitrator in various areas of legal practice.

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