The Impact of Legal Advocacy Experience Within the US Supreme Court on Trial Decision Outcomes

Introduction

While the determination of any outcome to a trial process should be based on merit, there is little doubt that the experience of legal advocates does have a bearing on trial outcomes. But little is known about the degree to which the experience of legal representatives influences the decision making of judges. Lawyers often procure significant experience within the United States Supreme Court system and as such are likely to improve as advocates within the system through time. Evidence suggests that individuals frequently engaging in litigation display different rates of success to those that engage with the system only episodically and infrequently (Sheehan et al., 1992). Kevin T McGuire conducted a significant study into the impact of the experience of competing trial lawyers on judicial decision making (McGuire, 1995). The study identified the experience differentials of lawyers and sought to determine the impact of this differential on trial outcomes. The McGuire study considered trial outcomes of the US Supreme Court over a decade-long period, accounting for the experience of trial lawyers and, critically, the litigant strength differential a measure of the relative economic standing of litigants’ on trial outcomes (McGuire, 1995). Adopting a logistic regression model, McGuire found that trial experience and a favourable amicus curie (Please note that while amicus curie are not parties to the litigation, they remain an important source of guidance and influence and as such were included as a parameter in the model) are both positively associated with favourable trial outcomes.

The present study shall extend upon McGuire’s worthwhile research by firstly considering the direction and strength of the claimed associations noted by McGuire (1995), employing a highly innovative set of estimation techniques. The study shall employ a series of novel parametric estimation techniques that provide potentially superior estimators while accommodating the challenges of the available data. Pertinently, the study will then consider McGuire’s existing predictive model and posit an alternative: a multilayer neural network model. This modelling technique can potentially provide superior predictive outcomes. An artificial neural network is a structure that seeks to replicate a neuron structure. It is simply a series of weighted, aggregative, non-linear values that have the potential to provide more accurate predictive outcomes than traditional parametric estimation techniques, as well as alternative non-parametric techniques. The use of such methods is not uncommon in the social sciences but remains relatively underemployed within interdisciplinary legal research.

Importantly, there remains a dearth of research considering key empirical questions, such as the impact of trial experience of lawyers and the role of amicus curie on trial outcomes; and an even greater dearth of literature positing practically framed deterministic models of trial outcomes utilising non-parametric models. Notable parametric studies within this area of research include those of Matthew Sag, Tonya Jacobi and Barton Beebe. There is little if any non-parametric research within this field. The use of neural networks is not however absent within the broader body of legal research. Warner (1990, 1992, 1993), was amongst the first to posit the benefits of logic based legal expert systems and one of the first legal realists to employ neural networks within legal expert systems. Warner (1990, 1992, 1993), contends that neural networks have the unique capacity to replicate deontic logic. However, the use of neural networks has not been limited to legal realists alone. Zeleznikow and Nolan (2001) have advanced scholarship in the domain of law and artificial intelligence systems focusing on soft computing based intelligent decision support systems. Lothar Philipps (1991), emphasis the benefit of neural networks within the legal domain. Hobson & Slee (1994), emphasis the benefits of neural networks in their application to case analysis and case based reasoning. Rose & Belew (1989, 1991), claim that neural networks can retrieve appropriate data while employing a legal realistic perception of the law.