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Complex Evidence in Litigation

Complex litigation tends to get framed as a problem for the jury system, but it is more properly viewed as a problem for any fact finder—juror, judge, arbitrator, expert panel—and for the litigants and their attorneys. Still, the jury framing is useful because it brings into focus some of the resources a fact finder needs to tackle the problem: attention, memory storage and retrieval, education and training, and life experience. In these respects, groups are advantaged over individuals, and experts are advantaged over nonexperts. Since judges have greater average expertise but juries act as groups, it is difficult to identify a net advantage either way. And, of course, accuracy is only one criterion by which we evaluate legal judgment; a full assessment requires considerations of efficiency, fairness, legitimacy, and community representation.

The task of studying the topic of complex litigation recapitulates the key features of the problem. Complex litigation produces a vast and gnarly multidimensional search space, yet legal fact finders and jury researchers alike attempt to draw inferences from only fragmentary glimpses of isolated regions of that space. As a result, legal fact finders and jury researchers each combine sparse data with inferences that go beyond the data given. Theory is always important in sociolegal research, but for this topic, it is essential if we are to say much at all.

This entry presents a theoretical framework for evaluating expertise and collective decision making and describes the research done in this area. It also examines the types of complexity with respect to the number of parties and issues in a dispute and the amount and complexity of the evidence presented in the trial.

Theoretical Issues

Expertise

The typical jury is obviously far less expert than the judge in one key respect—expertise on the law as it pertains to the case. But because juries do not provide a rationale for their verdict, we only rarely know that a jury has made a “mistake” on the law, and juries may not feel particularly hindered by their lack of legal expertise. What may matter far more is expertise with respect to the technical issues that may arise at the trial, involving the economic analysis of market power, the engineering of heavy machinery, the etiology of a disease, or the epidemiology of toxic exposure. Here, judges may outperform the average juror; judges are above average in education and intelligence, and they may have relevant experience from past trials. But we shouldn't overestimate either intelligence or experience. Studies of expertise show that it can take a decade or more of concerted effort to develop true mastery of a technical skill. Graduate students are highly intelligent and still struggle for months to successfully complete their more technical graduate courses. And today's judges are likely to have far less actual trial experience than their predecessors of earlier generations. As a sample of the community, the jury may collectively have more relevant expertise in nonlegal issues than the relevant judge.

Groups as Information Processors

In the 1950s, Irving Lorge and Herbert Solomon deduced that, ceteris paribus, groups are better situated than their individual members to find correct answers. If p is the probability that any given individual will find the “correct” answer, then the predicted probability P that a collectivity of size r will find the answer is P = 1 − (1 − p)r. More recently, Lu Hong and Scott Page have derived theorems proving that cognitively diverse groups—defined with respect to the perspectives and schemas they use to tackle a problem—can outperform even their best members. But this model, like that of Lorge and Solomon, proves group competence, not group performance. Empirically, we know that performance often falls short of competence.

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