If you had been a security policy-maker in the world's greatest power in 1900, you would have been a Brit, looking warily at your age-old enemy, France.I think you could maybe nitpick some holes in it for historical accuracy, but the basic point - that geopolitical tides in the twentieth century varied dramatically at ten year intervals - is a cogent one, and its point is underscored by the fact that five months after it was written, the world's whole geopolitical outlook was upended catastrophically by 9/11.
By 1910, you would be allied with France and your enemy would be Germany.
By 1920, World War I would have been fought and won, and you'd be engaged in a naval arms race with your erstwhile allies, the U.S. and Japan.
By 1930, naval arms limitation treaties were in effect, the Great Depression was underway, and the defense planning standard said "no war for ten years."
Nine years later World War II had begun.
By 1950, Britain no longer was the worlds greatest power, the Atomic Age had dawned, and a "police action" was underway in Korea.
Ten years later the political focus was on the "missile gap," the strategic paradigm was shifting from massive retaliation to flexible response, and few people had heard of Vietnam.
By 1970, the peak of our involvement in Vietnam had come and gone, we were beginning détente with the Soviets, and we were anointing the Shah as our protégé in the Gulf region.
By 1980, the Soviets were in Afghanistan, Iran was in the throes of revolution, there was talk of our "hollow forces" and a "window of vulnerability," and the U.S. was the greatest creditor nation the world had ever seen.
By 1990, the Soviet Union was within a year of dissolution, American forces in the Desert were on the verge of showing they were anything but hollow, the U.S. had become the greatest debtor nation the world had ever known, and almost no one had heard of the internet.
Ten years later, Warsaw was the capital of a NATO nation, asymmetric threats transcended geography, and the parallel revolutions of information, biotechnology, robotics, nanotechnology, and high density energy sources foreshadowed changes almost beyond forecasting.
All of which is to say that I'm not sure what 2010 will look like, but I'm sure that it will be very little like we expect, so we should plan accordingly.
We show that probability dilution is a symptom of a fundamental deficiency in probabilistic representations of statistical inference, in which there are propositions that will consistently be assigned a high degree of belief, regardless of whether or not they are true. We call this deficiency false confidence. [...] We introduce the Martin–Liu validity criterion as a benchmark by which to identify statistical methods that are free from false confidence. Such inferences will necessarily be non-probabilistic.From Section 3(d):
False confidence is the inevitable result of treating epistemic uncertainty as though it were aleatory variability. Any probability distribution assigns high probability values to large sets. This is appropriate when quantifying aleatory variability, because any realization of a random variable has a high probability of falling in any given set that is large relative to its distribution. Statistical inference is different; a parameter with a fixed value is being inferred from random data. Any proposition about the value of that parameter is either true or false. To paraphrase Nancy Reid and David Cox,3 it is a bad inference that treats a false proposition as though it were true, by consistently assigning it high belief values. That is the defect we see in satellite conjunction analysis, and the false confidence theorem establishes that this defect is universal.From Section 5:
This finding opens a new front in the debate between Bayesian and frequentist schools of thought in statistics. Traditional disputes over epistemic probability have focused on seemingly philosophical issues, such as the ontological inappropriateness of epistemic probability distributions [15,17], the unjustified use of prior probabilities [43], and the hypothetical logical consistency of personal belief functions in highly abstract decision-making scenarios [13,44]. Despite these disagreements, the statistics community has long enjoyed a truce sustained by results like the Bernstein–von Mises theorem [45, Ch. 10], which indicate that Bayesian and frequentist inferences usually converge with moderate amounts of data.
The false confidence theorem undermines that truce, by establishing that the mathematical form in which an inference is expressed can have practical consequences. This finding echoes past criticisms of epistemic probability levelled by advocates of Dempster–Shafer theory, but those past criticisms focus on the structural inability of probability theory to accurately represent incomplete prior knowledge, e.g. [19, Ch. 3]. The false confidence theorem is much broader in its implications. It applies to all epistemic probability distributions, even those derived from inferences to which the Bernstein–von Mises theorem would also seem to apply.
Simply put, it is not always sensible, nor even harmless, to try to compute the probability of a non-random event. In satellite conjunction analysis, we have a clear real-world example in which the deleterious effects of false confidence are too large and too important to be overlooked. In other applications, there will be propositions similarly affected by false confidence. The question that one must resolve on a case-by-case basis is whether the affected propositions are of practical interest. For now, we focus on identifying an approach to satellite conjunction analysis that is structurally free from false confidence.
The work presented in this paper has been done from a fundamentally frequentist point of view, in which θ (e.g. the satellite states) is treated as having a fixed but unknown value and the data, x, (e.g. orbital tracking data) used to infer θ are modelled as having been generated by a random process (i.e. a process subject to aleatory variability). Someone fully committed to a subjectivist view of uncertainty [13,44] might contest this framing on philosophical grounds. Nevertheless, what we have established, via the false confidence phenomenon, is that the practical distinction between the Bayesian approach to inference and the frequentist approach to inference is not so small as conventional wisdom in the statistics community currently holds. Even when the data are such that results like the Bernstein-von Mises theorem ought to apply, the mathematical form in which an inference is expressed can have large practical consequences that are easily detectable via a frequentist evaluation of the reliability with which belief assignments are made to a proposition of interest (e.g. ‘Will these two satellites collide?’).[boldface emphasis mine]
[...]
There are other engineers and applied scientists tasked with other risk analysis problems for which they, like us, will have practical reasons to take the frequentist view of uncertainty. For those practitioners, the false confidence phenomenon revealed in our work constitutes a serious practical issue. In most practical inference problems, there are uncountably many propositions to which an epistemic probability distribution will consistently accord a high belief value, regardless of whether or not those propositions are true. Any practitioner who intends to represent the results of a statistical inference using an epistemic probability distribution must at least determine whether their proposition of interest is one of those strongly affected by the false confidence phenomenon. If it is, then the practitioner may, like us, wish to pursue an alternative approach.
5 Aleatory Variability and Epistemic Uncertainty Aleatory variability and epistemic uncertainty are terms used in seismic hazard analysis that are not commonly used in other fields, but the concepts are well known. Aleatory variability is the natural randomness in a process. and propagate epistemic uncertainty: 1. Dempster-Shafer Theory of Evidence Finally, we have one way which is generally used to propagate uncertainty in combined analysis, where we are propagating both aleatory and epistemic uncertainty. This is called “second-order” probability. The second-order While there can be many sources of uncertainty, in the context of modeling, it is convenient to catego-rize the character of uncertainties as either aleatory or epistemic. The word aleatory derives from the Latin alea , which means the rolling of dice. Thus, an aleatoric uncertainty is one that is presumed to be The uncertainty here is aleatoric before I shuffle, but becomes epistemic after shuffling but before looking. In Hacking's An Introduction to Probability and Inductive Logic, the brief chapter on philosophical interpretations of probability concludes with the following comments. Our prototypical examples [of probability] are artificial randomizers. Epistemic uncertainty derives from the lack of knowledge of a parameter, phenomenon or process, while aleatory uncertainty refers to uncertainty caused by probabilistic variations in a random event . Each of these two different types of uncertainty has its own unique set of characteristics that separate it from the other and can be quantified through different methods. Aleatory uncertainty is irreducible except through design modifications. Examples of aleatory uncertainty are component failures or material properties derived from statistically significant testing under conditions relevant to the application. Aleatory uncertainties are characterized by frequency distributions; and aleatory uncertainties ... Finally, epistemic and aleatory uncertainty have distinct markers in natural language. New data (Fox, Ülkümen & Malle, 2011) suggest that epistemic uncertainty tends to be expressed using phrases like “I am 90% sure” or “I’m reasonably confident” whereas aleatory uncertainty tends to be . The Nature of Uncertainty Aleatory and Epistemic Aleatory uncertainty is irreducible. The only way to avoid this uncertainty is to change the nature of the phenomenon under consideration. Philosophically, the presence of this type of uncertainty is debated. One may question whether anything is inherently uncertain. 6 Aleatory Uncertainty and Epistemic Uncertainty • Aleatory uncertainty is an inherent variation associated with the physical system or the environment – Also referred to as variability, irreducible uncertainty, and stochastic uncertainty, random uncertainty • Examples: – Variation in atmospheric conditions and angle of attack for inlet conditions – Variation in fatigue life of ... Since LKNA ’14, I have learned about aleatory and epistemic risk. Epistemic: uncertainty due to gaps in knowledge; Aleatory: uncertainty due to variability or randomness [like throwing dice or flipping a coin] Differentiating between the type of risk is important because they are mitigated in completely different ways. Epistemic risk is the easier type to deal with because it is something that can be overcome.
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In 46 episodes, Hank Green will teach you philosophy! This course is based on an introductory Western philosophy college level curriculum. By the end of the ... http://www.theaudiopedia.com What is AMBIVALENCE? What does AMBIVALENCE mean? AMBIVALENCE meaning - AMBIVALENCE pronunciation - AMBIVALENCE defin... On today’s episode...CATS. Also: Hank talks about some philosophy stuff, like a few of the key concepts philosophers use when discussing belief and knowledge... For tutoring please call 856.777.0840 I am a recently retired registered nurse who helps nursing students pass their NCLEX. I have been a nurse since 1997. I have worked in a lot of nursing fields ... There are many forms of uncertainty which afflict measurements and predictions - this video outlines the main ones.
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