Jury Nullification: The Current State of the Law

In 2018, the Utah legislature considered a proposed bill that would have explicitly granted jurors the right to nullify in criminal cases. This research, done in preparation for committee testimony, contains the most up-to-date law on the topic. It includes a fifty-state survey on whether juries in various jurisdictions are (1) given the right to consider the possible sentencing penalty before rendering a verdict; (2) told they may disregard the law; or (3) instructed on the right to nullify. Additionally, the research includes fifty-state survey data on whether judges may lie to juries about the right to nullify, and how various jurisdictions treat attempts by outside organizations to notify potential jurors of their right to nullify

The Philosophy of Education

Much has been done to establish the need for, and benefits of, pre-college philosophy. However, there is an important argument to this effect that is unfortunately missing from much of the discussion. This is Mary Warnock\u27s work, where she argues in favor of pre-college philosophy, because it can increase what she calls candor, meaning being open and honest. To accomplish this, I will be drawing on Mary Warnock’s writings Philosophy in Education, Education for Pleasure, and Honesty and Cynicism. The addition of philosophy in the education system will allow students to gain a holistic and critical view of the subjects they are learning in school, which will lead to them having candor and lead to more trust of the education system rather than them being bitter cynics. Also, I will argue that the current grading system of the United States should be reworked rather than having a student’s performance be denoted on an A through F scale that only serves to drive the student to get good grades. After all, the goal of education should be to learn and that is easier to accomplish if the students enjoy learning rather than viewing school as simply a chore to accomplish

Extreme value analysis for the sample autocovariance matrices of heavy-tailed multivariate time series

We provide some asymptotic theory for the largest eigenvalues of a sample covariance matrix of a p-dimensional time series where the dimension p = p_n converges to infinity when the sample size n increases. We give a short overview of the literature on the topic both in the light- and heavy-tailed cases when the data have finite (infinite) fourth moment, respectively. Our main focus is on the heavytailed case. In this case, one has a theory for the point process of the normalized eigenvalues of the sample covariance matrix in the iid case but also when rows and columns of the data are linearly dependent. We provide limit results for the weak convergence of these point processes to Poisson or cluster Poisson processes. Based on this convergence we can also derive the limit laws of various function als of the ordered eigenvalues such as the joint convergence of a finite number of the largest order statistics, the joint limit law of the largest eigenvalue and the trace, limit laws for successive ratios of ordered eigenvalues, etc. We also develop some limit theory for the singular values of the sample autocovariance matrices and their sums of squares. The theory is illustrated for simulated data and for the components of the S&P 500 stock index.Comment: in Extremes; Statistical Theory and Applications in Science, Engineering and Economics; ISSN 1386-1999; (2016

Do You See What I See? The Science Behind Utah Rule of Evidence 617

Eyewitness identifications play a key role in many investigations and are often central to a prosecutor’s case. At the same time, eyewitness identifications can be tainted, accidentally or purposely, thus tainting the justice system as well. There are myriad reasons for this phenomenon, but the primary responsibility lies not with the witness, but rather a system that fails to recognize, and often amplifies, mistakes and assumptions in the identification process

Essays on Information and Knowledge in Microeconomic Theory

Diese Dissertation besteht aus drei unabhängigen Kapiteln, die sich mit Wissen und Informationen in der mikroökonomischen Theorie beschäftigen. In Kapitel 1 untersuchen wir ein Duopolmodell mit Preisdiskriminierung, bei dem die Verbraucher über ihren Datenschutz entscheiden. Wir stellen zwei Datenumgebungen gegenüber und finden für jede ein Gleichgewicht. In einer offenen Datenumgebung geben alle Verbraucher ihre Daten preis. Unternehmen diskriminieren bei der Preisgestaltung, was zu Wohlfahrtsverlusten aufgrund von Abwerbung führt. In einer Umgebung mit exklusiven Daten anonymisieren sich die Verbraucher, die Preise sind einheitlich, und der Markt ist effizient. Wir testen die Gleichgewichte in einem Experiment. In Kapitel 2 untersuchen wir ein Modell einer Organisation, die wissensintensive Produktion betreibt. Der Organisationsdesigner stellt Arbeiter ein, die mit Wissen ausgestattet sind, um Probleme zu lösen, deren Art ex ante unbekannt ist. Der Designer bestimmt, ob die Arbeitnehmer einzeln oder im Team produzieren. Als Team können die Arbeitnehmer kommunizieren und ihr Wissen teilen, während sie bei Einzelarbeit nur ihr eigenes Wissen nutzen können. Wir stellen fest, dass Teamarbeit optimal ist, wenn Spillovers ausreichend hoch sind. Insbesondere dann, wenn Spillovers perfekt oder alle Problemtypen gleich wahrscheinlich sind, sind selbstverwaltete Teams optimal. In Kapitel 3 untersuche ich ein dynamisches Modell mit einem Moral-Hazard-Problem und einem kostspieligen Wissenstransfer. Ein Auftraggeber stellt zwei risikoneutrale, vermögensbeschränkte Agenten ein, die jeweils eine individuelle Aufgabe in einem Projekt übernehmen. Bevor sie sich ihren Aufgaben zuwenden, können die Agenten beschließen, Wissen zu transferieren, das die Produktivität des Empfängers erhöht. Der Auftraggeber kann durch ein gemeinsames Leistungssignal einen Transfer mit oder ohne Verpflichtungsmacht veranlassen.This dissertation consists of three independent chapters that contribute to understanding how knowledge and information is used in microeconomic theory. In Chapter 1, we study a duopoly model of behavior-based pricing where consumers decide on their data privacy. Contrasting two data environments, we find unique equilibria for each. In an open data environment, all consumers reveal their data. Firms price discriminate causing welfare losses due to poaching. In an exclusive data environment, consumers anonymize, prices are uniform, and the market is efficient. We test the predictions in an experiment. In the open data treatment, subjects act as predicted. In the exclusive data treatment, buyers initially share data but anonymize when sellers poach. In Chapter 2, we study a model of an organization engaging in knowledge-intensive production. The organizational designer hires workers endowed with knowledge to solve problems whose types are ex ante unknown. The designer determines whether workers produce individually or as team. As team, workers can communicate and share their knowledge, while when working individually they can only use their own knowledge. We find that teamwork is optimal when spillovers are sufficiently high. Particularly, when spillovers are perfect, or all problem types are equally likely, self-managed teams arise as a special form of teamwork. In Chapter 3, I explore a dynamic model with a moral hazard problem and knowledge transfer. A principal hires two risk-neutral, wealth-constrained agents to each perform an individual task in a project. Before they address their tasks, the agents can decide to transfer knowledge that increases the task-related productivity of the receiver. The transfer is costly for both. I find that the principal can induce a transfer with or without commitment power through a joint performance signal. It is not clear that commitment is always better, even though with commitment the first-best allocation can be achieved

Joint Size Estimation Using Joint Traces on Borehole Walls

One approach to characterizing subsurface joint populations is to assumesurface joint patterns are representative of joints at depth. Yet, many times, either the analogous surface joints are unexposed, or absent because surface rocks did not experience the same deformation history. The alternative of direct subsurface characterization has been limited by joints not being resolvable in seismic data and borehole data not yielding fracture size. The present approach uses the subsurface geometry of joint/borehole intersections to estimate mean joint size (mean joint length and width) and aspect ratio (joint length to width ratio), and presents a new method for determining the volumetric joint intensity as estimated with cycloidal scanline samples. This study focused on bed-normal joints in sedimentary rocks that typically terminate at bedding surfaces, have bed-parallel lengths greater than or equal to the bed-perpendicular lengths, and are rectangular. Rectangular joint/boreholeintersections have six geometries: complete, long-edge, short-edge, corner, end, and pierced, which are differentiated by completeness of borehole intersection and joint trace position on the borehole wall as a function of borehole and joint orientation. The counts for the intersection geometries are used to estimate mean joint size and aspect ratio. The approach yielded accurate mean size estimates for synthetic trace populations. Based on this success, the estimators were applied to borehole joint populations from FMI (Formation MicroImager) data logs in the Mesaverde Group of the Piceance Basin, Colorado. Subsurface estimates yielded bed-parallel and perpendicular lengths greater than for exposed joints along the basin perimeter, but the surface and subsurface shared small aspect ratios of ~1:1. The difference in estimated size may reflect differences in deformation history between the basin center and perimeter

Maximum interpoint distance of high-dimensional random vectors

A limit theorem for the largest interpoint distance of $p$ independent and identically distributed points in $\mathbb{R}^n$ to the Gumbel distribution is proved, where the number of points $p=p_n$ tends to infinity as the dimension of the points $n\to\infty$. The theorem holds under moment assumptions and corresponding conditions on the growth rate of $p$. We obtain a plethora of ancillary results such as the joint convergence of maximum and minimum interpoint distances. Using the inherent sum structure of interpoint distances, our result is generalized to maxima of dependent random walks with non-decaying correlations and we also derive point process convergence. An application of the maximum interpoint distance to testing the equality of means for high-dimensional random vectors is presented. Moreover, we study the largest off-diagonal entry of a sample covariance matrix. The proofs are based on the Chen-Stein Poisson approximation method and Gaussian approximation to large deviation probabilities.Comment: 34 pages, 2 figure

Large sample autocovariance matrices of linear processes with heavy tails

We provide asymptotic theory for certain functions of the sample autocovariance matrices of a high-dimensional time series with infinite fourth moment. The time series exhibits linear dependence across the coordinates and through time. Assuming that the dimension increases with the sample size, we provide theory for the eigenvectors of the sample autocovariance matrices and find explicit approximations of a simple structure, whose finite sample quality is illustrated for simulated data. We also obtain the limits of the normalized eigenvalues of functions of the sample autocovariance matrices in terms of cluster Poisson point processes. In turn, we derive the distributional limits of the largest eigenvalues and functionals acting on them. In our proofs, we use large deviation techniques for heavy-tailed processes, point process techniques motivated by extreme value theory, and related continuous mapping arguments.Comment: 28 page