The Federal Rules of Civil Settlement

The Federal Rules of Civil Procedure were originally based upon a straightforward model of adjudication: Resolve the merits of cases at trial and use pretrial procedures to facilitate accurate trial outcomes. Though appealing in principle, this model has little relevance today. As is now well known, the endpoint around which the Federal Rules were structured — trial — virtually never occurs. Today, the vast majority of civil cases terminate in settlement. This Article is the first to argue that the current litigation process needs a new regime of civil procedure for the world of settlement This Article begins by providing a systemic analysis of why the Federal Rules inadequately prevent settlement outcomes from being distorted relative to the underlying merits — as defined by reference to substantive law — of a given dispute. It then explains how the Federal Rules can actually amplify these distortions. Indeed, notwithstanding the well-worn adage that settlement occurs in the “shadow of the law,” scholars have shown that non-merits factors exert significant influence on settlement outcomes. However, these insights have not been considered together and combined with a systemic focus on the ways in which the influence of these factors on settlement outcomes is actually a product of the basic structural features of the Federal Rules. This Article takes these next steps to explain that the “shadow of the law” that is cast on settlements is fading. Further, this Article discusses a new phenomenon in the current litigation environment — namely, that litigants’ increased reliance on prior settlements as “precedent” for future settlement decisions may move settlement even further out of the “shadow of the law” and into the “shadow of settlement” itself. This Article then traces these problems to three foundational assumptions underlying the Federal Rules of Civil Procedure, all of which have become outmoded in a world of settlement. In rethinking these assumptions, it provides a new conceptual account that contextualizes previously isolated procedural reform proposals as challenges to these foundational assumptions. It also explains how these reform efforts ought to be refined and extended with a specific view toward systematically redesigning the basic model and operation of the Federal Rules for a world of settlement. Lastly, it sets forth new proposals that seek to reorient current rules expressly toward the goal of aligning settlement outcomes with the merits of underlying claims. What emerges is a new vision of procedure — one in which the application of pretrial procedural rules do not merely facilitate trial but are designed to provide litigants with guidance regarding the merits of claims and are used to align settlement outcomes more meaningfully with the dictates of the substantive law. In describing this vision, this Article lays the groundwork for the design of a new Federal Rules of Civil Settlement

One Time to Sue: The Case for a Uniform Statute of Limitations for Consumers to Sue Under the Fair Debt Collection Practices Act

In 1977, Congress enacted the Fair Debt Collection Practices Act (FDCPA) in an effort to provide injured consumers with uniform protection against the systematically abusive practices of the debt collection industry. The FDCPA created a private right of action for victims to sue; however, an individual who wishes to bring a private suit under the FDCPA must do so “within one year from the date on which the violation occurs.” The effectiveness of this private right of action has been unsettled due to the circuit split over the meaning of this provision. For many FDCPA violations, the debt collector might engage in the violative conduct several days, weeks, months, or even years before that conduct actually harms the consumer. Thus, the principal disagreement focuses on when the “violation occurs”: Does it occur when the debt collector engages in the proscribed conduct, or does it occur when that conduct actually harms the consumer? Moreover, if the violation occurs when the debt collector engages in the proscribed act, can a “discovery rule” apply to delay the running of the statute of limitations until the consumer finds out about the violation? This Note explores the various analyses circuit courts apply to determine the date on which an FDCPA violation occurs. Unless federal courts adopt a uniform analysis to determine when an FDCPA violation occurs for the purpose of triggering the running of the statute of limitations, injured consumers will continue to receive inconsistent protection under the statute. This Note argues that in order to promote the FDCPA’s remedial nature, federal courts should adopt the following guidelines to determine the date on which an FDCPA violation occurs: (1) a violation occurs, and a cause of action accrues, when a consumer suffers the kind of harm for which Congress intended to provide a private damages remedy; and (2) where a debt collector fraudulently conceals his or her violative conduct from an injured consumer, the equitable tolling doctrine should apply to toll the running of the FDCPA’s statute of limitations for the duration of the concealment

Accessible user interface support for multi-device ubiquitous applications: architectural modifiability considerations

The market for personal computing devices is rapidly expanding from PC, to mobile, home entertainment systems, and even the automotive industry. When developing software targeting such ubiquitous devices, the balance between development costs and market coverage has turned out to be a challenging issue. With the rise of Web technology and the Internet of things, ubiquitous applications have become a reality. Nonetheless, the diversity of presentation and interaction modalities still drastically limit the number of targetable devices and the accessibility toward end users. This paper presents webinos, a multi-device application middleware platform founded on the Future Internet infrastructure. Hereto, the platform's architectural modifiability considerations are described and evaluated as a generic enabler for supporting applications, which are executed in ubiquitous computing environments

Mapping the American Shareholder Litigation Experience: A Survey of Empirical Studies of the Enforcement of the U.S. Securities Law

In this paper, we provide an overview of the most significant empirical research that has been conducted in recent years on the public and private enforcement of the federal securities laws. The existing studies of the U.S. enforcement system provide a rich tapestry for assessing the value of enforcement, both private and public, as well as market penalties for fraudulent financial reporting practices. The relevance of the U.S. experience is made broader by the introduction through the PSLRA in late 1995 of new procedures for the conduct of private suits and the numerous efforts to evaluate the effects of those provisions. We believe that the evidence reviewed here shows that the PSLRA\u27s provisions have largely achieved their intended purposes. For example, many more private suits are headed by an institutional lead plaintiff, such plaintiffs appear to fulfill the desired role of monitoring the suit\u27s prosecution and their presence is associated with suits yielding better settlements and lower attorneys\u27 fees awards. SEC enforcement efforts, while significant, have tended to focus on weaker targets, suggesting that the big fish get away. Equally importantly, markets impose their own discipline on companies whose managers release false financial reports and, in turn, firms discipline the managers who are responsible for false misleading reporting, perhaps because of the presence of, or potential for, private enforcement actions

Off-Policy Evaluation of Probabilistic Identity Data in Lookalike Modeling

We evaluate the impact of probabilistically-constructed digital identity data collected from Sep. to Dec. 2017 (approx.), in the context of Lookalike-targeted campaigns. The backbone of this study is a large set of probabilistically-constructed "identities", represented as small bags of cookies and mobile ad identifiers with associated metadata, that are likely all owned by the same underlying user. The identity data allows to generate "identity-based", rather than "identifier-based", user models, giving a fuller picture of the interests of the users underlying the identifiers. We employ off-policy techniques to evaluate the potential of identity-powered lookalike models without incurring the risk of allowing untested models to direct large amounts of ad spend or the large cost of performing A/B tests. We add to historical work on off-policy evaluation by noting a significant type of "finite-sample bias" that occurs for studies combining modestly-sized datasets and evaluation metrics involving rare events (e.g., conversions). We illustrate this bias using a simulation study that later informs the handling of inverse propensity weights in our analyses on real data. We demonstrate significant lift in identity-powered lookalikes versus an identity-ignorant baseline: on average ~70% lift in conversion rate. This rises to factors of ~(4-32)x for identifiers having little data themselves, but that can be inferred to belong to users with substantial data to aggregate across identifiers. This implies that identity-powered user modeling is especially important in the context of identifiers having very short lifespans (i.e., frequently churned cookies). Our work motivates and informs the use of probabilistically-constructed identities in marketing. It also deepens the canon of examples in which off-policy learning has been employed to evaluate the complex systems of the internet economy.Comment: Accepted by WSDM 201

Customer purchase behavior prediction in E-commerce: a conceptual framework and research agenda

Digital retailers are experiencing an increasing number of transactions coming from their consumers online, a consequence of the convenience in buying goods via E-commerce platforms. Such interactions compose complex behavioral patterns which can be analyzed through predictive analytics to enable businesses to understand consumer needs. In this abundance of big data and possible tools to analyze them, a systematic review of the literature is missing. Therefore, this paper presents a systematic literature review of recent research dealing with customer purchase prediction in the E-commerce context. The main contributions are a novel analytical framework and a research agenda in the field. The framework reveals three main tasks in this review, namely, the prediction of customer intents, buying sessions, and purchase decisions. Those are followed by their employed predictive methodologies and are analyzed from three perspectives. Finally, the research agenda provides major existing issues for further research in the field of purchase behavior prediction online

Mapping the American Shareholder Litigation Experience: A Survey of Empirical Studies of the Enforcement of the U.S. Securities Law

In this paper, we provide an overview of the most significant empirical research that has been conducted in recent years on the public and private enforcement of the federal securities laws. The existing studies of the U.S. enforcement system provide a rich tapestry for assessing the value of enforcement, both private and public, as well as market penalties for fraudulent financial reporting practices. The relevance of the U.S. experience is made broader by the introduction through the PSLRA in late 1995 of new procedures for the conduct of private suits and the numerous efforts to evaluate the effects of those provisions. We believe that the evidence reviewed here shows that the PSLRA\u27s provisions have largely achieved their intended purposes. For example, many more private suits are headed by an institutional lead plaintiff, such plaintiffs appear to fulfill the desired role of monitoring the suit\u27s prosecution and their presence is associated with suits yielding better settlements and lower attorneys\u27 fees awards. SEC enforcement efforts, while significant, have tended to focus on weaker targets, suggesting that the big fish get away. Equally importantly, markets impose their own discipline on companies whose managers release false financial reports and, in turn, firms discipline the managers who are responsible for false misleading reporting, perhaps because of the presence of, or potential for, private enforcement actions

Causal Confusion in Imitation Learning

Behavioral cloning reduces policy learning to supervised learning by training a discriminative model to predict expert actions given observations. Such discriminative models are non-causal: the training procedure is unaware of the causal structure of the interaction between the expert and the environment. We point out that ignoring causality is particularly damaging because of the distributional shift in imitation learning. In particular, it leads to a counter-intuitive "causal misidentification" phenomenon: access to more information can yield worse performance. We investigate how this problem arises, and propose a solution to combat it through targeted interventions---either environment interaction or expert queries---to determine the correct causal model. We show that causal misidentification occurs in several benchmark control domains as well as realistic driving settings, and validate our solution against DAgger and other baselines and ablations.Comment: Published at NeurIPS 2019 9 pages, plus references and appendice