This book addresses two significant research areas in an interdependent fashion. It is first of all a comprehensive but concise text that covers the recently developed and widely applicable methods of qualitative choice analysis, illustrating the general theory through simulation models of automobile demand and use. It is also a detailed study of automobile demand and use, presenting forecasts based on these powerful new techniques. The book develops the general principles that underlie qualitative choice models that are now being applied in numerous fields in addition to transportation, such as housing, labor, energy, communications, and criminology. The general form, derivation, and estimation of qualitative choice models are explained, and the major models - logit, probit, and GEV - are discussed in detail. And continuous/discrete models are introduced. In these, qualitative choice methods and standard regression techniques are combined to analyze situations that neither alone can accurately forecast. Summarizing previous research on auto demand, the book shows how qualitative choice methods can be used by applying them to specific auto-related decisions as the aggregate of individuals' choices. The simulation model that is constructed is a significant improvement over older models, and should prove more useful to agencies and organizations requiring accurate forecasting of auto demand and use for planning and policy development. The book concludes with an actual case study based on a model designed for the investigations of the California Energy Commission. Kenneth Train is Visiting Associate Professor in Economics at the University of California, Berkeley, and Director of Economic Research at Cambridge Systematics, Inc., also in Berkeley. Qualitative Choice Analysisis included in The MIT Press Transportation Studies Series, edited by Marvin L. Manheim.

Optimal Regulation addresses the central issue of regulatory economics - how toregulate firms in a way that induces them to produce and price "optimally." It synthesizes the majorfindings of an extensive theoretical literature on what constitutes optimality in various situationsand which regulatory mechanisms can be used to achieve it. It is the first text to provide aunified, modern, and nontechnical treatment of the field.The book includes models for regulatingoptimal output, tariffs, and surplus subsidy schemes, and presents all of the material graphically,with clear explanations of often highly technical topics.Kenneth E. Train is Associate AdjunctProfessor in the Department of Economics and Graduate School of Public Policy at the University ofCalifornia, Berkeley. He is also Principal of the firm Cambridge Systematics.Topics include: Thecost structure of natural monopoly (economies of scale and scope). Characterization of firstandsecond-best optimality. Surplus subsidy schemes for attaining first-best optimality. Ramsey pricesand the Vogelsang-Finsinger mechanism for attaining them. Time-ofuse (TOU) prices and Riordan'smechanisms for attaining the optimal TOU prices' Multipart and self-selecting tariffs, and Sibley'smethod for using self-selecting tariffs to achieve optimality. The Averch-Johnson model of howrate-of-return regulation induces inefficiencies. Analysis of regulation based on the firm's returnon Output, costs, or sales. Price-cap regulation. Regulatory treatment of uncertainty and its impacton the firm's behavior. Methods of attaining optimality without direct regulation (contestability,auctioning the monopoly franchise.)

This book constitutes the revised selected papers of the 9th International Conference on Cloud Computing, Big Data & Emerging Topics, JCC-BD&ET 2021, held in La Plata, Argentina*, in June 2021. The 12 full papers and 2 short papers presented were carefully reviewed and selected from a total of 37 submissions. The papers are organized in topical sections on parallel and distributed computing; machine and deep learning; big data; web and mobile computing; visualization.. *The conference was held virtually due to the COVID-19 pandemic.