This book was developed to help students and researchers in the fields of economics, finance, law and other social science areas to understand and apply neuroscience. With the use of neuroscience technologies, it is now possible to understand how people make decisions in practice, using friendly and ecological experimental setups. The first half of the book studies the decision-making process and explains how the brain is organized. It presents the brain as a distributed processing system, shows how to record brain activities, and how to combine neurosciences and statistical tools to design experiments. In the last chapters, experiments on stock market decision, dilemma judgment, vote decision and understanding of media propaganda are described and discussed.
One of the most challenging issues facing our current information society is the accelerating accumulation of data trails in transactional and communication systems, which may be used not only to profile the behaviour of individuals for commercial, marketing and law enforcement purposes, but also to locate and follow things and actions. Data mining, convergence, interoperability, ever increasing computer capacities and the extreme miniaturisation of the hardware are all elements which contribute to a major contemporary challenge: the profiled world. This interdisciplinary volume offers twenty contributions that delve deeper into some of the complex but urgent questions that this profiled world addresses to data protection and privacy.
One of the most challenging issues facing our current information society is the accelerating accumulation of data trails in transactional and communication systems, which may be used not only to profile the behaviour of individuals for commercial, marketing and law enforcement purposes, but also to locate and follow things and actions. Data mining, convergence, interoperability, ever- increasing computer capacities and the extreme miniaturisation of the hardware are all elements which contribute to a major contemporary challenge: the profiled world. This interdisciplinary volume offers twenty contributions that delve deeper into some of the complex but urgent questions that this profiled world addresses to data protection and privacy. The chapters of this volume were all presented at the second Conference on Privacy and Data Protection (CPDP2009) held in Brussels in January 2009 (www.cpdpconferences.org). The yearly CPDP conferences aim to become Europe's most important meeting where academics, practitioners, policy-makers and activists come together to exchange ideas and discuss emerging issues in information technology, privacy and data protection and law. This volume reflects the richness of the conference, containing chapters by leading lawyers, policymakers, computer, technology assessment and social scientists. The chapters cover generic themes such as the evolution of a new generation of data protection laws and the constitutionalisation of data protection and more specific issues like security breaches, unsolicited adjustments, social networks, surveillance and electronic voting. This book not only offers a very close and timely look on the state of data protection and privacy in our profiled world, but it also explores and invents ways to make sure this world remains a world we want to live in.
In clear, easy-to-grasp language, the author covers many of the topics that you will need to know in order to launch and run a successful business venture.
This handbook serves as a complement to the Handbook on Data Envelopment Analysis (eds, W.W. Cooper, L.M. Seiford and J, Zhu, 2011, Springer) in an effort to extend the frontier of DEA research. It provides a comprehensive source for the state-of-the art DEA modeling on internal structures and network DEA. Chapter 1 provides a survey on two-stage network performance decomposition and modeling techniques. Chapter 2 discusses the pitfalls in network DEA modeling. Chapter 3 discusses efficiency decompositions in network DEA under three types of structures, namely series, parallel and dynamic. Chapter 4 studies the determination of the network DEA frontier. In chapter 5 additive efficiency decomposition in network DEA is discussed. An approach in scale efficiency measurement in two-stage networks is presented in chapter 6. Chapter 7 further discusses the scale efficiency decomposition in two stage networks. Chapter 8 offers a bargaining game approach to modeling two-stage networks. Chapter 9 studies shared resources and efficiency decomposition in two-stage networks. Chapter 10 introduces an approach to computing the technical efficiency scores for a dynamic production network and its sub-processes. Chapter 11 presents a slacks-based network DEA. Chapter 12 discusses a DEA modeling technique for a two-stage network process where the inputs of the second stage include both the outputs from the first stage and additional inputs to the second stage.Chapter 13 presents an efficiency measurement methodology for multi-stage production systems. Chapter 14 discusses network DEA models, both static and dynamic. The discussion also explores various useful objective functions that can be applied to the models to find the optimal allocation of resources for processes within the black box, that are normally invisible to DEA. Chapter 15 provides a comprehensive review of various type network DEA modeling techniques. Chapter 16 presents shared resources models for deriving aggregate measures of bank-branch performance, with accompanying component measures that make up that aggregate value. Chapter 17 examines a set of manufacturing plants operating under a single umbrella, with the objective being to use the component or function measures to decide what might be considered as each plant s core business.Chapter 18 considers problem settings where there may be clusters or groups of DMUs that form a hierarchy. The specific case of a set off electric power plants is examined in this context.Chapter 19 models bad outputs in two-stage network DEA. Chapter 20 presents an application of network DEA to performance measurement of Major League Baseball (MLB) teams. Chapter 21 presents an application of a two-stage network DEA model for examining the performance of 30 U.S. airline companies. Chapter 22 then presents two distinct network efficiency models that are applied to engineering systems."
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