Sentences with phrase «when modeling assumptions»

The authors find that when modeling assumptions closely mirror the actual requirements and flexibilities of the final MATS rule, the predicted impact on the electricity sector is less severe.

Not exact matches

Even when two valuators do agree on the methodology, they may vary on the assumptions used in that model and then arrive at very different values for the firm.

When founders discover their assumptions are wrong, as they inevitably will, the result isn't a crisis, it's a learning event called a pivot — and an opportunity to change the business model.

And it would not be until they questioned their own most basic assumptions about «the way Magellan's was managed» — the company's dependence on them, the heroic - leader model they'd brought into the company from day one — that they would have a shot at doing what few founders can when the wall confronts them: getting through it.

The problem is when investors adopt theories and models that embed the most optimistic assumptions possible, run contrary to historical evidence, or embed subtle peculiarities that actually drive the results (see, for example, the «novel valuation measures» section of The Diva is Already Singing).

When you say «Copernicanism», are you referring to the heliocentric model of the solar system — which David's post discussed — or are you referring more broadly to the assumption that we aren't in an especially privileged position in the universe?

But for my purposes it is enough to say that the assumptions I have listed have many practical consequences when governments adopt policies oriented to economic growth on the basis of advice from neoliberal economists, or, indeed, many other economists who share this model.

When Henry Ford merchandized his Model T shortly after World War I, advertising was based on the assumption that the seller and buyer were equally accessible, equally knowledgeable, equally powerful.

Because 2 - D models make simplified assumptions about some aspects of the flow, they can't account for changes in the flow, such as when the flood wave moves around large obstacles, changes rapidly in direction, or fully immerses bridge decks.

How do you judge a model for the origin of life when its underlying assumptions are so uncertain?

The core assumption that eHarmony makes when using models based on data from married couples is that you can generalize their «fit» to data collected from singles.

When we look to test - based evidence — and look no further — to decide whether choice «works,» we are making two rather extraordinary, unquestioned assumptions: that the sole purpose of schooling is to raise test scores, and that district schools have a place of privilege against which all other models must justify themselves.

We believe that this model will allow educators to question their own assumptions and see opportunities, rather than obstacles, when shaping their own students» learning experiences.

This double standard re assumptions works to the benefit of the Passive Indexing model when it is tested statistically.

Also covered in this section are costs and business models including when games have collectible elements or whether they work on the assumption of expansion

I agree, they should evaluate something... I am not as sanguine as Ray though about how a mismatch «insight» would be employed in the science when the belief remains that the underlying assumptions remain robust enough coupled with the scientist as advocate model you support.

I would argue that if we use a simple radiative model with a variety of assumptions, no upper atmosphere cooling but only warming will occur with increased CO2 (see # 333), based on the radiative transfer equations and the Second Law of thermodynamics, but when other complexities are introduced, this might change.

The problem arises, I believe, when strong feedbacks, «masking» effects of aerosols and volcanoes and other uncertain assumptions are fed into computer models to generate catastrophic scenarios for the near - medium future.

The assumption of independence leads to increased confidence in the «robustness» of model results when multiple models agree.

There is, however, a point to be made about exercising caution when evaluating the forward - looking output of a computer model, particularly when those models are used to advocate policy changes on the assumption that the computer model accurately simulates the earth's climate, and more particularly when there is no demonstrable track record of the predictive accuracy of the model.

So, when you wrote, «the same mistake that seems to appear in a lot of climate models, a false assumption of linearity...» you in fact made the mistake; one of supposing an assumption where there is instead a demonstration.

Don't be surprised when they don't question the computer model assumptions.

Specifically, when he looked at the climate models used by the IPCC, Kiehl found they all used very different assumptions for aerosol cooling and, most significantly, he found that each of these varying assumptions were exactly what was required to combine with that model's unique sensitivity assumptions to reproduce historical temperatures.

When energy modelling is carried out, the models contain assumptions and default values that don't reflect the design.

To be explicit, when I said «linear model» I meant the assumption of validity of linear regression between temperature and independent variables.

When Alarmists say that ENSO's short term effects must balance to zero over the long run they are doing a priori science using the assumptions of the «radiation - only» model.

The assumptions of the global warming models must be publicly, repeatedly, and systematically critiqued, and when they do not stand up to scrutiny, these assumptions and policies must be rejected by the United States government outright.

My personal concern: I was taught that models are only valid when their assumptions hold.

However, its long been apperent that while climate models and econ models have similar levels of scientific validity, economists are far more willing to talk about assumptions their models make, when and why those assumptions might or might not hold, etc., than climate scientists.

Once such an IPCC exposition of the assumptions, complications and uncertainties of climate models was constructed and made public, it would immediately have to lead, in my view, to more questions from the informed public such as what does calculating a mean global temperature change mean to individuals who have to deal with local conditions and not a global average and what are the assumptions, complications and uncertainties that the models contain when it comes to determining the detrimental and beneficial effects of a «global» warming in localized areas of the globe.

Quite a remarkable statement when you consider the myriad assumptions and uncertainties built into the modeling scenarios that are needed to favor renewables.

I find it rather disturbing when models are created based on assumptions, then tweaked and modified and finally used to run «experiments» which amazingly provide proof of the original belief.

it is well established that warming since 1950 is predominantly anthropogenic» This is is only a model - derived assertion based on an assumption of declining natural variability, that was utterly refuted when the models were found to have too little natural variation.

When a hypothesis / model fails the next step is to relook at the hypothesis / model to see which assumptions in the modeling / hypothesis are incorrect.

Regarding the reports on renewable energy standards, Frank Ackerman, a Harvard PhD and Senior Economist with Synapse Energy Economics, Inc. said the Beacon Hill Institute models contained «wild overstatement (s) of the cost of wind energy, assumed that expensive backup capacity was always needed and running when wind energy was used, inflated the price of new transmission capacity, and overestimated job losses due to assumption of «hypersensitivity to tax rates.

A few years ago, Pierce and Adams modeled the potential cloud forming effect of cosmic rays and found it wanting by more than an order of magnitude, even when the most favourable assumptions possible were made.

«The point of the paper is to show a broad range of hypotheticals and use the model to demonstrate the economic impact, not to read tea leaves and try to precisely forecast future REC prices,» Divounguy said when asked about the basis for those assumptions.

When you are this wrong in such a short period something is seriously wrong with your model and assumptions.

Simple climate models are perfect in answering many questions, but they require too many unphysical assumptions when they are used to answer other questions, and they can not tell anything about some further ones.

Between this shortcut / mistake (which violates the Stephan - Boltzmann equations and was copied by all the following climate scientists) and through the climate model's assumption of a constant linear lapse rate of 6C / kilometre when it is probably not constant), they have changed all the logarithmic radiation equations into linear ones.

The attribution of the warming is made from assumptions [G and Curry] from models and the models are all programmed to input 0.2 degrees rise a decade [the rise that «must «occur when CO2 is going up at this rate»].»

I personally published what was wrong (with) my own original 1971 cooling hypothesis a few years later when more data and better models came along and further analysis showed [anthropogenic global warming] as the much more likely... In fact, for me that is a very proud event — to have discovered with colleagues why our initial assumptions were unlikely and better ones reversed the conclusions — an early example of scientific skepticism in action in climatology.»

The approach Müller considers «most practical» for adaptation studies is to simply make assumptions that fall somewhere within the range of model projections — for example, a 2 - degree temperature rise and 20 % less rain — when estimating consequences such as shifting crop yields.

The no - brainer part is that when the models fail, that is, are unable accurately to predict anything of significance, the investigator should realize that the small signal assumption has failed.

Matching available past and present observations is a necessary condition, but never can validate a model because incorrect assumptions also could fit past data, particularly when there are many adjustable parameters.

As is typical when models go wrong, early problems in the model did not cause users to revisit their assumptions:

As there are multiple periods in the geological record of tens of millions years in duration when CO2 levels were high and the planet was cold and when CO2 levels were low and the planet was warm, it appears there is a basic fundamental assumption in the model of atmospheric radiation that is incorrect or there is an omission of another mechanism from the standard models of atmosphere radiation.

There are numerous models that show a relationship, but the models omit more elements of atmospheric complexity than they include (and exclude any solar forcing with the exception of irradiance in some models), they can not backcast past ca 1900, they can not forecast next month, and they do generate greatly dissimilar forecasts when fed with the same assumptions of future conditions.

The former modeled short - term, natural cooling, not AGW warming, and the latter was very obvious circular reasoning which became redundant when Hadley later admitted their natural variability assumptions were wrong anyway.

Some of us simply can not accept as remotely credible a claim to 95 % confidence in distant - future forecasts generated by models which are inherently incapable of faithfully reproducing the full complexity of the real world climate, particularly when they rest on elementary assumptions which are neither proven nor provable.