Building a Crystal Ball Model
Monte Carlo simulation is a tool for modeling uncertainty. Typically, we begin with a deterministic spreadsheet model of the situation we are analyzing, then use Crystal Ball to add stochastic assumptions to represent the most important sources of uncertainty.
Information about climate1 is used to make decisions every day. From farmers deciding
which crops to plant next season to mayors in large cities deciding how to prepare for future heat
waves, and from an insurance company assessing future flood risks to a national security planner
assessing future conflict risks from the impacts of drought, users of climate information span a
vast array of sectors in both the public and private spheres. Each of these communities has
different needs for climate data, with different time horizons (see Box 1) and different tolerances
This book is dedicated to the many students we have taught over the years,
whose thought-provoking questions led us to rethink what we had learned as
graduate students. For all such questioning minds, we offer the research efforts
of scholars around the world who have come to the conclusion that uncertainty
can be decomposed into a risk component and a reward component; that all
uncertainty is not bad.
This book was conceived as a result of many years research with students
and postdocs in molecular simulation, and shaped over several courses on
the subject given at the University of Groningen, the Eidgen¨ossische Technische
Hochschule (ETH) in Z¨urich, the University of Cambridge, UK, the
University of Rome (La Sapienza), and the University of North Carolina
at Chapel Hill, NC, USA.
The market for lemons quality uncertainty and mechanism present about the model with automobiles as an example; example and application; counteracting institutions; conclusion of The market for lemons quality uncertainty and mechanism.
THE EFFECTS OF MACROECONOMIC UNCERTAINTY ON IRREVERSIBLE INVESTMENT Peer group and, by extension, average student
performance are endogenous to unobserved determinants of housing prices. One
estimation strategy that accommodates this endogeneity is that taken by Bayer, McMillan,
and Reuben (2002), who estimate a structural model for housing prices and community
composition in San Francisco.
Cross-document coreference, the task of grouping all the mentions of each entity in a document collection, arises in information extraction and automated knowledge base construction. For large collections, it is clearly impractical to consider all possible groupings of mentions into distinct entities.
In spoken dialogue systems, Partially Observable Markov Decision Processes (POMDPs) provide a formal framework for making dialogue management decisions under uncertainty, but efﬁciency and interpretability considerations mean that most current statistical dialogue managers are only MDPs. These MDP systems encode uncertainty explicitly in a single state representation.
We present and evaluate a new model for Natural Language Generation (NLG) in Spoken Dialogue Systems, based on statistical planning, given noisy feedback from the current generation context (e.g. a user and a surface realiser). We study its use in a standard NLG problem: how to present information (in this case a set of search results) to users, given the complex tradeoffs between utterance length, amount of information conveyed, and cognitive load. We set these trade-offs by analysing existing MATCH data.
In this paper the functional uncertainty machinery in L F G is compared with the treatment of long distance dependencies in TAG. It is shown that the functional uncertainty machinery is redundant in TAG, i.e.,what functional uncertainty accomplishes for L F G follows f~om the T A G formalism itself and some aspects of the linguistic theory instantiated in TAG. It is also shown that the analyses provided by the functional uncertainty machinery can be obtained without requiring power beyond mildly context-sensitive grammars.
Volume 102, Number 6, November–December 1997
Journal of Research of the National Institute of Standards and Technology
[J. Res. Natl. Inst. Stand. Technol. 102, 647 (1997)]
Uncertainty and Dimensional Calibrations
Volume 102 Ted Doiron and John Stoup National Institute of Standards and Technology, Gaithersburg, MD 20899-0001
The calculation of uncertainty for a measurement is an effort to set reasonable bounds for the measurement result according to standardized rules.
Knowing that the plant parameters can vary within their lower and upper bounds, this
parametric uncertainty is formulated as an additive perturbation in the transfer function
matrix. It is important to note that the controller be designed with respect to worst case
uncertainty for each λij. This can be achieved by performing an optimization procedure
given by (61) for 200 frequencies. Here an element by element uncertainty bound model is
used for the characterization of upper bound of the uncertainty matrix. Then wij , which
satisfies (62) for each λij is given in matrix form as,...
Having lived with the World Wide Web for twenty years, surfing the Web becomes a way of our life that cannot be separated. From latest news, photo sharing, social activities, to research collaborations and even commercial activities and government affairs, almost all kinds of information are available and processible via the Web. While people are appreciating the great invention, the father of the Web, Sir Tim Berners-Lee, has started the plan for the next generation of the Web, the Semantic Web....
objective or subjective, when making decisions under uncertainty. This is especially true
when the consequences of the decisions can have a significant impact, financial or
otherwise. Most of us make everyday personal decisions this way, using an intuitive process
based on our experience and subjective judgments.
Mainstream statistical analysis, however, seeks objectivity by generally restricting the
information used in an analysis to that obtained from a current set of clearly relevant data.
The most discussed effect of global warming is the increase of temperatures,
although this increase will not be homogeneous through the seasons, with the winters
expected to warm up significantly more than the summers. In addition, changes in
precipitation are also expected that could lead to increase or decrease of rainfall, snowfall
and other water-related events. Finally, a change in the frequency and intensity of storm
events could be possible, although this is probably the most uncertain of the effects of
For new information to be valuable it must, of course, actually be new information. If
new cost estimation or accounting procedures simply confirm existing beliefs, they have little
likelihood of contributing to changes in decision-making, and thus little likelihood of adding
value. Environmental accounting techniques will be most valuable when they correct beliefs
that are biased or when they focus on issues subject to high degrees of uncertainty.
A focus in much of the environmental accounting literature is on the failure to quantify
environmental benefits and costs.
Because time-series studies focus on a given geographic location over a number of years,
factors that are often thought to influence the health of the population, such as percentage of
smokers, income level, occupational exposure to pollutants, access to medical care and age
distribution, do not need to be incorporated into the analysis as they are considered to remain
relatively constant within the study area over time. Typically, the only other factors aside from
pollution included in these models are weather variables and seasonal controls.
Several times already in this chapter we have made statements about the standard errors, or uncertainties, in a set of M estimated parameters a. We have given some formulas for computing standard deviations or variances of individual parameters