Aggregation has long been viewed as the primary if not sole vehicle for mass claims resolution. For a half-century, scholars have consistently viewed the consolidated litigation of similar claims through joinder, class actions and more recently multi-district litigation as the only mechanism for efficiently resolving mass claims. In this Article, I challenge that long-standing and fundamental conception. The Article seeks to reconceptualize our understanding of mass claims resolution, arguing that we are witnessing the birth of a second, unexplored branch of mass claims resolution mechanisms -- which I term “disaggregative” dispute resolution systems because they lack the traditional aggregation of common questions that has been the hallmark of traditional mass claims litigation. Disaggregation returns to a focus on the individual akin to that of the single-plaintiff system, but uses either procedural or substantive streamlining, or a shift of costs to the defendant, to correct the asymmetries that prompted the creation of class actions. Many of our most innovative claims structures -- from the BP GCCF and the fund created in the wake of the Costa Concordia disaster, to the common single-plaintiff arbitration clauses in consumer and employment agreements -- use this new, bottom-up model of disaggregative mass claims resolution instead of the familiar top-down aggregative model. These next-generation systems have been heralded as a significant advancement in mass claims resolution, capable of awarding more compensation to claimants more quickly and at lower cost than aggregate litigation. But like the single-plaintiff and aggregate litigation systems that preceded it, disaggregation has its flaws. Because the defendant typically designs these systems, they often give rise to questions about legitimacy and the accuracy of compensation. More shockingly, situating disaggregation within the existing doctrinal trends reveals that the rise of disaggregation allows corporations to avoid class actions in a far broader swath of cases than has previously been identified -- such that class actions will, as a practical matter, proceed only at the defendant's election, raising substantial questions about the viability of private actions as a mechanism for the enforcement of law. Yet, because these systems are the product of contract, attempts to restrict these systems have largely failed. The answer to these problems lies in an unlikely and potentially controversial approach: expanding rather than restricting the availability of disaggregation, by creating a public mechanism for disaggregation -- comparable to the existing public aggregation mechanisms.

The "leave no one behind" principle espoused by the 2030 Agenda for Sustainable Development requires measures of progress for different segments of the population. This entails detailed disaggregated data to identify subgroups that might be falling behind, to ensure progress toward achieving the Sustainable Development Goals (SDGs). The Asian Development Bank and the Statistics Division of the United Nations Department of Economic and Social Affairs developed this practical guidebook with tools to collect, compile, analyze, and disseminate disaggregated data. It also provides materials on issues and experiences of countries regarding data disaggregation for the SDGs. This guidebook is for statisticians and analysts from planning and sector ministries involved in the production, analysis, and communication of disaggregated data.

These proceedings present the technical contributions to the First Conference on A- tions, Market Mechanisms, and Their Applications (AMMA), held May 8-9, 2009 in Boston, Massachusetts, USA. The conference was devoted to issues that arise in all stages of deploying a market mechanism to solve a problem, including theoretical and empirical examinations. In addition to more traditional academic papers, the conf- ence placed emphasis on experiences from the real world, including case studies and new applications. The main goal of AMMA was to explore the synergy required for good mechanism design. This includes an understanding of the economic and game-theoretic issues, the ability to design protocols and algorithms for realizing desired outcomes, and the knowledge of specific institutional details that are important in practical applications. We were lucky enough to attract papers and talks from economists and computer scientists, theorists and empiricists, academics and practitioners. The program, as reflected in these proceedings, ranged from fundamental theory on auctions and m- kets to empirical design and analysis of matching mechanisms, peer-to-peer-systems, and prediction markets.

In this book, first published in 1990, leading theorists and applied economists address themselves to the key questions of aggregation. The issues are covered both theoretically and in wide-ranging applications. Of particular intrest is the optimal aggregation of trade data, the need for micro-modelling when imoprtant non-linearities are present (for example, tax exhaustion in modelling company behaviour) and the use of a micro-model to stimulate labour supply behaviour in a macro-model of the Netherlands.

As a member of the working group on data disaggregation, the Food and Agriculture Organization of the United Nations (FAO) has taken numerous steps towards supporting Member Countries in the production of disaggregated estimates. Within this framework, these Guidelines offer methodological and practical guidance for the production of direct and indirect disaggregated estimates of SDG indicators having surveys as their main or preferred data source. Furthermore, the publication provides tools to assess the accuracy of these estimates and presents strategies for the improvement of output quality, including Small Area Estimation methods.

This book describes the new generation of discrete choice methods, focusing on the many advances that are made possible by simulation. Researchers use these statistical methods to examine the choices that consumers, households, firms, and other agents make. Each of the major models is covered: logit, generalized extreme value, or GEV (including nested and cross-nested logits), probit, and mixed logit, plus a variety of specifications that build on these basics. Simulation-assisted estimation procedures are investigated and compared, including maximum stimulated likelihood, method of simulated moments, and method of simulated scores. Procedures for drawing from densities are described, including variance reduction techniques such as anithetics and Halton draws. Recent advances in Bayesian procedures are explored, including the use of the Metropolis-Hastings algorithm and its variant Gibbs sampling. The second edition adds chapters on endogeneity and expectation-maximization (EM) algorithms. No other book incorporates all these fields, which have arisen in the past 25 years. The procedures are applicable in many fields, including energy, transportation, environmental studies, health, labor, and marketing.