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The idea of artificial intelligence--job-killing robots, self-driving cars, and self-managing organizations--captures the imagination, evoking a combination of wonder and dread for those of us who will have to deal with the consequences. But what if it's not quite so complicated? The real job of artificial intelligence, argue these three eminent economists, is to lower the cost of prediction. And once you start talking about costs, you can use some well-established economics to cut through the hype. The constant challenge for all managers is to make decisions under uncertainty. And AI contributes by making knowing what's coming in the future cheaper and more certain. But decision making has another component: judgment, which is firmly in the realm of humans, not machines. Making prediction cheaper means that we can make more predictions more accurately and assess them with our better (human) judgment. Once managers can separate tasks into components of prediction and judgment, we can begin to understand how to optimize the interface between humans and machines. More than just an account of AI's powerful capabilities, Prediction Machines shows managers how they can most effectively leverage AI, disrupting business as usual only where required, and provides businesses with a toolkit to navigate the coming wave of challenges and opportunities.-- Provided by publisher

Master advanced topics in the analysis of large, dynamically dependent datasets with this insightful resourceStatistical Learning with Big Dependent Data delivers a comprehensive presentation of the statistical and machine learning methods useful for analyzing and forecasting large and dynamically dependent data sets. The book presents automatic procedures for modelling and forecasting large sets of time series data.  Beginning with some visualization tools, the book discusses procedures and methods for finding outliers, clusters, and other types of heterogeneity in big dependent data. It then introduces various dimension reduction methods, including regularization and factor models such as regularized Lasso in the presence of dynamical dependence and dynamic factor models. The book also covers other forecasting procedures, including index models, partial least squares, boosting, and now-casting. It further presents machine-learning methods, including neural network, deep learning, classification and regression trees and random forests.  Finally, procedures for modelling and forecasting spatio-temporal dependent data are also presented.Throughout the book, the advantages and disadvantages of the methods discussed are given.  The book uses real-world examples to demonstrate applications, including use of many R packages. Finally, an R package associated with the book is available to assist readers in reproducing the analyses of examples and to facilitate real applications.Analysis of Big Dependent Data includes a wide variety of topics for modeling and understanding big dependent data, like:New ways to plot large sets of time seriesAn automatic procedure to build univariate ARMA models for individual components of a large data setPowerful outlier detection procedures for large sets of related time seriesNew methods for finding the number of clusters of time series and discrimination methods , including vector support machines, for time seriesBroad coverage of dynamic factor models including new representations and estimation methods for generalized dynamic factor modelsDiscussion on the usefulness of lasso with time series and an evaluation of several machine learning procedure for forecasting large sets of time seriesForecasting large sets of time series with exogenous variables, including discussions of index models, partial least squares, and boosting.Introduction of modern procedures for modeling and forecasting spatio-temporal data Perfect for PhD students and researchers in business, economics, engineering, and science: Statistical Learning with Big Dependent Data also belongs to the bookshelves of practitioners in these fields who hope to improve their understanding of statistical and machine learning methods for analyzing and forecasting big dependent data.

Thomas Bayes was an eighteenth-century Presbyterian minister and amateur mathematician whose obscure life belied the profound impact of his work. Like most research into probability at the time, his theorem was mainly seen as relevant to games of chance, like dice and cards. But its implications soon became clear, affecting fields as diverse as medicine, law and artificial intelligence. Bayes' theorem helps explain why highly accurate screening tests can lead to false positives, causing unnecessary anxiety for patients. A failure to account for it in court has put innocent people in jail. But its influence goes far beyond practical applications. Fusing biography, razor-sharp science communication and intellectual history, 'Everything Is Predictable' is a captivating tour of Bayes' theorem and its impact on modern life.

Economic forecasting is a key ingredient of decision making in the public and private sectors. This book provides the necessary tools to solve real-world forecasting problems using time-series methods. It targets undergraduate and graduate students as well as researchers in public and private institutions interested in applied economic forecasting.

\"Risk terrain modeling (RTM) diagnoses the spatial attractors of criminal behavior and makes accurate predictions of where crime will occur at the micro-level. This book presents RTM as part of a larger risk management agenda that defines and measures crime problems; suggests ways in which they can be addressed through interventions; proposes measures for assessing effectiveness of treatment and sustainability of efforts; and offers suggestions for how police organizations can address vulnerabilities and exposures in the communities that they serve through strategies that go beyond specific deterrence of offenders. Technical and conceptual aspects of RTM are considered into the context of past criminological research, leading to a discussion of crime vulnerabilities and exposures, and the Theory of Risky Places. Then best practices for RTM, crime prediction, and risk reduction are set to ACTION. Case studies empirically demonstrate how RTM can be used to analyze the spatial dynamics of crime, allocate resources, and implement customized crime and risk reduction strategies that are transparent, measurable, and effective. Researchers and practitioners will learn how the combined factors that contribute to criminal behavior can be targeted, connections to crime can be monitored, spatial vulnerabilities can be assessed, and actions can be taken to reduce the worst effects\"--Provided by publisher.

The aim of the article is to propose different ways of forecasting labour productivity developments by using different statistical forecasting methods and applying different approaches to the most appropriate statistical forecasting method selection. This article examines labour productivity in the European Union member states, measured per employee and per hour worked, in the period from 1990 to 2016. In the forecasting analysis, seven statistical forecasting methods are used to forecast labour productivity for each European Union member state separately and for the European Union as a whole. Overall, three approaches to determine the forecast values of labour productivity have been used in the analysis. The impact of each statistical forecasting method was determined by using the MSE approach. The results are suggesting that the differences in labour productivity between countries should be smaller. In the future research, the level of labour productivity convergence in the European Union should be investigated.

The unemployment can be considered as one of the main economic problems. The aim of this article is to examine the differences in male and female unemployment rates in selected European countries and to predict their future trends by using different statistical forecasting models. Furthermore, the impact of adding a new data point on the selection of the most appropriate statistical forecasting model and on the overall forecasting errors values is also evaluated. Male and female unemployment rates are observed for twelve European countries in the period from 1991 to 2014. Four statistical forecasting models have been selected and applied and the most appropriate model is considered to be the one with the lowest overall forecasting errors values. The analysis has shown that in the period from 1991 to 2014 the decreasing trend of unemployment rates in the short-run is forecasted for more Eastern Balkan than the EU-28 countries. An additional data point for male and female unemployment rates in 2014 led to somewhat smaller forecasting errors in more than half of the observed countries. However, the additional data point does not necessarily improve forecasting performances of the used statistical forecasting models.

The history of twentieth-century American physical oceanography concentrates on naval patronage, but its significance for British oceanography is largely unknown. This case study analyzes a varied patronage structure, including naval, industrial, academic, and local and central governmental support, for one site of British physical oceanography, the Liverpool Observatory and Tidal Institute and, in particular, its work on storm surges between 1919 and 1959. Storm surges, caused by wind and changes in barometric pressure, can produce dramatic changes in sea levels. The local shipping industry initially funded the Institute’s research on surge forecasting to improve the accuracy of tidal predictions. After a flood in 1928, however, the focus shifted to flood forecasting. Local government then backed their work, during the Second World War support came from the Royal Navy, and since a flood in 1953, from central government. This case study reveals the range of negotiations carried out between patrons and researchers, and demonstrates how researchers managed competing demands from academic interests and those of industry, the navy, and the government. Studying institutions that did not see a dramatic increase in state patronage during the early Cold War enables us to see the impact of patronage more clearly, highlighting how research interests and methods differed (or not) between institutions with different patronage structures.

Hurricane Climatology explains how to analyze and model hurricane data to better understand and predict present and future hurricane activity.