Conditionals And Prediction

This book offers a new and in-depth analysis of English conditional sentences. In a wide-ranging discussion, Dancygier classifies conditional constructions according to time-reference and modality. She shows how the basic meaning parameters of conditionality correlate to formal parameters of the linguistic constructions which are used to express them. Dancygier suggests that the function of prediction is central to the definition of conditionality, and that conditional sentences display certain formal features which correlate to aspects of interpretation. Although the analysis is based primarily on English, it provides a theoretical framework that can be extended cross-linguistically to a broad range of grammatical phenomena. It will be essential reading for scholars and students concerned with the role of conditionals in English and many other languages.

The volume brings together a selection of papers from a symposium on Conditionality held in the University of Duisburg on 25-26 March 1994. Ten years after the Stanford symposium, the Proceedings of which were edited by Traugott et al. (1986), the area of conditionality is revisited in a synthesis of issues and aspects with insights drawn from the wider framework of general processes of conceptualisation. One major question is therefore what conceptual categories fall under conditionality or how far the notion of conditionality can be extended. The volume represents the up-to-date research on most aspects of conditionality some of which include the relationship between conditionality, hypotheticality and counterfactuality, polarity, historical perspectives, concessives, the acquisition of conditionals.

Conditional constructions have long fascinated linguists, grammarians and philosophers. In this pioneering new study, Barbara Dancygier and Eve Sweetser offer a new descriptive framework for the study of conditionality, broadening the range of richly described conditional constructions. They explore theoretical issues such as the mental-space-building processes underlying conditional thinking and the form-meaning relationship involved in expressing conditionality. Using a broad range of attested English conditional constructions, the book examines inter-constructional relationships. Within the framework of Mental Spaces Theory, shared parameters of meaning are shown to be relevant to conditional constructions generally, as well as related temporal and causal constructions. This significant contribution to the field will be welcomed by a wide range of researchers in theoretical and cognitive linguistics.

This book is a full-scale exposition of Charles Manski's new methodology for analyzing empirical questions in the social sciences. He recommends that researchers first ask what can be learned from data alone, and then ask what can be learned when data are combined with credible weak assumptions. Inferences predicated on weak assumptions, he argues, can achieve wide consensus, while ones that require strong assumptions almost inevitably are subject to sharp disagreements. Building on the foundation laid in the author's Identification Problems in the Social Sciences (Harvard, 1995), the book's fifteen chapters are organized in three parts. Part I studies prediction with missing or otherwise incomplete data. Part II concerns the analysis of treatment response, which aims to predict outcomes when alternative treatment rules are applied to a population. Part III studies prediction of choice behavior. Each chapter juxtaposes developments of methodology with empirical or numerical illustrations. The book employs a simple notation and mathematical apparatus, using only basic elements of probability theory.

Based upon ten case studies, Prediction explores how science-based predictions guide policy making and what this means in terms of global warming, biogenetically modifying organisms and polluting the environment with chemicals.

This book is intended for anyone, regardless of discipline, who is interested in the use of statistical methods to help obtain scientific explanations or to predict the outcomes of actions, experiments or policies. Much of G. Udny Yule's work illustrates a vision of statistics whose goal is to investigate when and how causal influences may be reliably inferred, and their comparative strengths estimated, from statistical samples. Yule's enterprise has been largely replaced by Ronald Fisher's conception, in which there is a fundamental cleavage between experimental and non experimental inquiry, and statistics is largely unable to aid in causal inference without randomized experimental trials. Every now and then members of the statistical community express misgivings about this turn of events, and, in our view, rightly so. Our work represents a return to something like Yule's conception of the enterprise of theoretical statistics and its potential practical benefits. If intellectual history in the 20th century had gone otherwise, there might have been a discipline to which our work belongs. As it happens, there is not. We develop material that belongs to statistics, to computer science, and to philosophy; the combination may not be entirely satisfactory for specialists in any of these subjects. We hope it is nonetheless satisfactory for its purpose.

This book highlights the development of new methods for assessing and forecasting the state of various complex ageing systems in service; analyzing the influence of destabilizing factors on the accuracy of aircraft flight navigation support; and making recommendations on the ideal aircraft route, taking into consideration the available information on the reliability of the navigation and communication equipment.

This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.

Gregor Betz explores the following questions: Where are the limits of economics, in particular the limits of economic foreknowledge? Are macroeconomic forecasts credible predictions or mere prophecies and what would this imply for the way economic policy decisions are taken? Is rational economic decision making possible without forecasting at all?

This important text and reference for researchers and students in machine learning, game theory, statistics and information theory offers a comprehensive treatment of the problem of predicting individual sequences. Unlike standard statistical approaches to forecasting, prediction of individual sequences does not impose any probabilistic assumption on the data-generating mechanism. Yet, prediction algorithms can be constructed that work well for all possible sequences, in the sense that their performance is always nearly as good as the best forecasting strategy in a given reference class. The central theme is the model of prediction using expert advice, a general framework within which many related problems can be cast and discussed. Repeated game playing, adaptive data compression, sequential investment in the stock market, sequential pattern analysis, and several other problems are viewed as instances of the experts' framework and analyzed from a common nonstochastic standpoint that often reveals new and intriguing connections.