Robinson Everett: The Citizen Lawyer Ideal Lives On

In this tribute to Professor Robinson O. Everett, Dean David Levi questions the view that the citizen-lawyer or lawyer-statesmen models are in decline. Tracing Professor Everett’s varied career, accomplishments, and commitments to individuals and institutions; Levi contends that Everett combined the lawyer\u27s traditional focus on the individual with an overall dedication to the larger community. Everett was not just a model citizen; he was a lawyer-citizen. Levi contends that the survival of the lawyer-citizen and lawyer-statesmen models is a matter of choice and character. Nothing in the current structure of the legal economy places these models out of reach for those who would follow in Robinson Everett\u27s footsteps

Sensitivity, safety, and the law: A reply to Pardo

ABSTRACTIn a recent paper, Michael Pardo argues that the epistemic property that is legally relevant is the one called Safety, rather than Sensitivity. In the process, he argues against our Sensitivity-related account of statistical evidence. Here we revisit these issues, partly in order to respond to Pardo, and partly in order to make general claims about legal epistemology. We clarify our account, we show how it adequately deals with counterexamples and other worries, we raise suspicions about Safety's value here, and we revisit our general skepticism about the role that epistemological considerations should play in determining legal policy

Judging Measures

The question of how to optimally design judicial institutions is one of central importance to the scholarship focused on courts. Basic questions such as whether there should be mandatory retirement for judges, whether judges should be expected to write their own opinions and whether greater racial or gender diversity on the courts improves decision making are optimal design questions. Given the vast variation in the types of judicial system designs used around the world (and even within the United States), it should be possible to conduct a comparative analysis of the relative efficacy of the different designs. These comparisons cannot be evaluated, however, without first tackling the matter of how to measure justice or judicial performance. Although within the legal academy and the judiciary there is considerable skepticism and hostility to the measurement project, we argue that the project is worth attempting for both judges and academics. That said, the simple measures often used today, while necessary, cannot be relied on exclusively. To achieve a more reliable and useful measurement, judges must be involved in the process of arriving at the right characteristics to measure and the right ways to measure them. If judges get involved in improving the quality of data collection and measurement, the inherent dangers in empirical analysis of the judiciary will both be recognized and more effectively navigated. At the same time, empirical analysis with judicial participation is more likely to assist judges and judicial policymakers

Lie point symmetries and ODEs passing the Painlev\'e test

The Lie point symmetries of ordinary differential equations (ODEs) that are candidates for having the Painlev\'e property are explored for ODEs of order $n =2, \dots ,5$. Among the 6 ODEs identifying the Painlev\'e transcendents only $P_{III}$, $P_V$ and $P_{VI}$ have nontrivial symmetry algebras and that only for very special values of the parameters. In those cases the transcendents can be expressed in terms of simpler functions, i.e. elementary functions, solutions of linear equations, elliptic functions or Painlev\'e transcendents occurring at lower order. For higher order or higher degree ODEs that pass the Painlev\'e test only very partial classifications have been published. We consider many examples that exist in the literature and show how their symmetry groups help to identify those that may define genuinely new transcendents

A Practically Competitive and Provably Consistent Algorithm for Uplift Modeling

Randomized experiments have been critical tools of decision making for decades. However, subjects can show significant heterogeneity in response to treatments in many important applications. Therefore it is not enough to simply know which treatment is optimal for the entire population. What we need is a model that correctly customize treatment assignment base on subject characteristics. The problem of constructing such models from randomized experiments data is known as Uplift Modeling in the literature. Many algorithms have been proposed for uplift modeling and some have generated promising results on various data sets. Yet little is known about the theoretical properties of these algorithms. In this paper, we propose a new tree-based ensemble algorithm for uplift modeling. Experiments show that our algorithm can achieve competitive results on both synthetic and industry-provided data. In addition, by properly tuning the "node size" parameter, our algorithm is proved to be consistent under mild regularity conditions. This is the first consistent algorithm for uplift modeling that we are aware of.Comment: Accepted by 2017 IEEE International Conference on Data Minin