1 edition of Forecasting zero found in the catalog.
Forecasting zero
Jonathan Pearl
Published
2011
by Strategic Studies Institute, U.S. Army War College in Carlisle, PA
.
Written in English
A vigorous debate is occurring among American elites with respect to whether and when the United States should relinquish its nuclear weapons. Bolstering hopes for tangible results is that a U.S. President is again publicly and forcefully supporting disarmament. While this debate, which addresses both technical and political factors related to abolition, may be the most serious one of its kind since the dawn of the nuclear age, the future of U.S. nuclear weapons policy remains uncertain. The general approach advanced today in U.S. policy circles largely hews, after all, to the logic of the past 65 years: arms control and nonproliferation now, disarmament at an undetermined time in the future. Moreover, several conceptual and strategic barriers continue to block serious progress toward U.S. disarmament. By situating the current pro-disarmament rhetoric in this larger historical and strategic context, this monograph argues that there is reason to doubt whether the current push for disarmament will produce meaningful and lasting results.
Edition Notes
Other titles | US nuclear history and the low probability of disarmament, United States nuclear history and the low probability of disarmament |
Statement | Jonathan Pearl |
Contributions | Army War College (U.S.). Strategic Studies Institute |
Classifications | |
---|---|
LC Classifications | JZ5665 .P45 2011 |
The Physical Object | |
Pagination | x, 57 p. ; |
Number of Pages | 57 |
ID Numbers | |
Open Library | OL25330218M |
ISBN 10 | 158487516X |
ISBN 10 | 9781584875161 |
LC Control Number | 2011506830 |
OCLC/WorldCa | 768823905 |
Get this from a library! Forecasting zero: U.S. nuclear history and the low probability of disarmament. [Jonathan Pearl, (Writer on nuclear disarmament); Army War College (U.S.). Strategic Studies Institute.] -- A vigorous debate is occurring among American elites with respect to whether and when the United States should relinquish its nuclear weapons. This book is perfectly timed as Maintenance professionals navigate through the advent of the 4th Industrial Revolution – powered by the Internet of Things, AI and intelligent machines. It applies Industry specifically to the Maintenance context, and is laser-focused on driving success in your first implementation in Maintenance
Superforecasting—predicting events that will occur in the future—is not only possible; it accounts for an entire industry. World-renowned behavioral scientist Tetlock (Expert Political Judgment: How Good Is It?How Can We Know, , etc.) explains why some people are so good at it and how others can cultivate the skill.. Global forecasting is hardly limited to predicting the weather. Get this from a library! Forecasting zero: U.S. nuclear history and the low probability of disarmament. [Jonathan Pearl; Army War College (U.S.). Strategic Studies Institute.].
Forecasting of demand using ARIMA model Article (PDF Available) in International Journal of Engineering Business Management 10(2) October . Forecasting returns is as important as forecasting volatility in multiple areas of finance. This topic, essential to practitioners, is also studied by academics. In this new book, Dr Stephen Satchell brings together a collection of leading thinkers and practitioners from around the world who address this complex problem using the latest.
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Philip E. Tetlock is the Annenberg University Professor at the University of Pennsylvania and holds appointments in the psychology and political science departments and the Wharton School of Business.
He and his wife, Barbara Mellers, are the co-leaders of the Good Judgment Project, a multi-year forecasting study. He is also the author of Expert Political Judgment and (with Aaron Belkin Cited by: Discover the best Business Planning & Forecasting in Best Sellers. Find the top most popular items in Amazon Books Best Sellers.
The TRB National Cooperative Highway Research Program's NCHRP Web-Only Document Zero Emission Vehicles: Forecasting Fleet Scenarios and their Emissions Implications analyzes a set of scenarios of infrastructure development, policy changes, and cost parameters, with a suite of 49 simulations across those scenarios conducted to assess their.
This book describes what was learned as Tetlock set out to improve forecasting accuracy with the Good Judgement Project. Largely in response to colossal US intelligence errors, Summarizing 20 years of research on forecasting accuracy conducted from throughPhilip Tetlock concluded “the average expert was roughly as accurate as a /5.
Stationarity and differencing. A stationary time series is one whose properties do not depend on the time at which the series is observed.
14 Forecasting zero book, time series with trends, or with seasonality, are not stationary — the trend and seasonality will affect the value of the time series at different Forecasting zero book. On the other hand, a white noise series is stationary — it does not matter when you. Forecasting is the process of making predictions of the future based on past and present data and most commonly by analysis of trends.
A commonplace example might be estimation of some variable of interest at some specified future date. Prediction is a similar, but more general term. Both might refer to formal statistical methods employing time series, cross-sectional or longitudinal data, or.
Superforecasting: The Art and Science of Prediction is a book by Philip E. Tetlock and Dan Gardner released in It details findings from The Good Judgment Project. 4 External links. The Economist reports that superforecasters are clever (with a good mental attitude), but not necessarily geniuses.
It reports on the treasure trove of data Author: Philip E. Tetlock. tional forecasting, and providing an overview of forecasting methodology.
Session 2: Time series approaches to forecasting (Se ctions 4/2 – 4/3) Exponential smoothing methods and ARIMA mo dels. Forecasting time series using R Time series in R 2 Outline 1 Time series in R 2 Some simple forecasting methods 3 Measuring forecast accuracy 4 Exponential smoothing 5 Box-Cox transformations 6 ARIMA forecasting 7 Difficult seasonality 8 forecast() function 9 Time series cross-validation.
With time series forecasting, one-step forecasts may not be as relevant as multi-step forecasts. In this case, the cross-validation procedure based on a rolling forecasting origin can be modified to allow multi-step errors to be used. Suppose that we are interested in models that produce good \(4\)-step-ahead forecasts.
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Perform financial forecasting, reporting, and operational.•Editor-in-Chief, International Journal of Forecasting How my forecasting methodology is used: •Pharmaceutical Benefits Scheme •Cancer incidence and mortality •Electricity demand •Ageing population •Fertilizer sales Poll: How experienced are you in forecasting?
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