Dragon Black

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It has been proposed that the more homogeneous the system, and the stronger its interactions, the more predictable it will be. For instance, in non-linear systems with phase transitions at a critical point, it is well known that a window of predictability occurs in the neighborhood of the critical point due to precursory signs: the system recovers more slowly from perturbations, autocorrelation changes, variance increases, spatial coherence increases, etc.

For the phenomena of unsustainable growth e. In systems that are discrete scale invariant such a model is power law growth, decorated with a log-periodic function.

This has been applied to many problems, [3] for instance: rupture in materials, [24] [28] earthquakes, [29] and the growth and burst of bubbles in financial markets [12] [30] [31] [32] [33].

An interesting dynamic to consider, that may reveal the development of a block-buster success, is Epidemic phenomena : e.

Given a model and data, one can obtain a statistical model estimate. This model estimate can then be used to compute interesting quantities such as the conditional probability of the occurrence of a dragon king event in a future time interval, and the most probable occurrence time.

When doing statistical modeling of extremes, and using complex or nonlinear dynamic models, there is bound to be substantial uncertainty.

Thus, one should be diligent in uncertainty quantification: not only considering the randomness present in the fitted stochastic model, but also the uncertainty of its estimated parameters e.

One can then use the estimated probabilities and their associated uncertainties to inform decisions. In the simplest case, one performs a binary classification : predicting that a dragon king will occur in a future interval if its probability of occurrence is high enough, with sufficient certainty.

For instance, one may take a specific action if a dragon king is predicted to occur. For instance, if the cost of a miss is very large relative to the cost of a false alarm, the optimal decision will detect dragon kings more frequently than they occur.

One should also study the true positive rate of the prediction. The smaller this value is, the weaker the test, and the closer one is to black swan territory.

In practice the selection of the optimal decision, and the computation of its properties must be done by cross validation with historical data if available , or on simulated data if one knows how to simulate the dragon kings.

In a dynamic setting the dataset will grow over time, and the model estimate, and its estimated probabilities will evolve.

In this dynamic setting, the test will likely be weak most of the time e. Dragon kings form special kinds of events leading to extreme risks which can also be opportunities.

That extreme risks are important should be self-evident. Natural disasters provide many examples e.

In general such statistics arrive in the presence of heavy-tailed distributions , and the presence of dragon kings will augment the already oversized impact of extreme events.

Despite the importance of extreme events, due to ignorance, misaligned incentives, and cognitive biases, we often fail to adequately anticipate them.

Technically speaking, this leads to poorly specified models where distributions that are not heavy tailed enough, and under-appreciating both serial and multivariate dependence of extreme events.

Some examples of such failures in risk assessment include the use of Gaussian models in finance Black—Scholes , the Gaussian copula , LTCM , the use of Gaussian processes and linear wave theory failing to predict the occurrence of rogue waves , the failure of economic models in general to predict the financial crisis of — , and the under-appreciation of external events, cascades, and nonlinear effects in probabilistic risk assessment , leading to not anticipating the Fukushima Daiichi nuclear disaster in Such high impact failures emphasize the importance of the study of extremes.

The dragon king concept raises many questions about how one can deal with risk. Of course, if possible, one should avoid exposure to large risks often referred to as the black swan approach.

However, in many developments, exposure to risk is a necessity, and a trade-off between risk and return needs to be navigated.

This nontrivial case is the one that must be considered. In an adaptive system, where prediction of dragon kings is successful, one can act to defend the system or even profit.

How to design such resilient systems , as well as their real time risk monitoring systems, [39] is an important and interdisciplinary problem where dragon kings must be considered.

On another note, when it comes to the quantification of risk in a given system whether it be a bank, an insurance company, a dike, a bridge, or a socio-economic system , risk needs to be accounted for over a period, such as annually.

Typically one is interested in statistics such as the annual probability of loss or damage in excess of some value value at risk , other tail risk measures , and return periods.

To provide such risk characterizations, the dynamic dragon kings must be reasoned about in terms of annual frequency and severity statistics.

These frequency and severity statistics can then be brought together in a model such as a compound Poisson process. If not, one may only construct scenarios.

However, in any case, given the uncertainty present, a range of scenarios should be considered. Due to the shortage of data for extreme events, the principle of parsimony , and theoretical results from extreme value theory about universal tail models, one typically relies on a generalized Pareto distribution GPD tail model.

However such a model excludes DK. Thus, when one has sufficient reason to believe that DK are present, or if one simply wants to consider a scenario, one may e.

From Wikipedia, the free encyclopedia. Redirected from Dragon King Theory. Event that is both extremely large in impact and of unique origins.

Bc4 is to prevent Black from playing the freeing move The variations resulting from this move are notorious for having been heavily analysed.

In addition to covering d5, White's light-squared bishop helps cover White's queenside and controls the a2—g8 diagonal leading to Black's king.

However, the bishop is exposed on c4 to an attack by a rook on c8, and usually has to retreat to b3, giving Black more time to organize his attack.

Common in this line is an exchange sacrifice on c3 by Black to break up White's queenside pawns, and sacrifices to open up the long diagonal for Black's bishop on g7 are also common.

An example of both ideas is the line 9. Bc4 Bd7 Bb3 Ne5 Bxc4 Rxc4 Bh6 Nxe4! Qe3 Rxc3! The Soltis Variation was the main line of the Dragon up until the late s.

Garry Kasparov played the move three times in the World Championship against Viswanathan Anand, scoring two wins and a draw. The line goes 9.

Other important deviations for Black are Qa5 and More recently, White players have often avoided the Soltis by playing Kb1 , which has proven so effective that Black players have in turn tried to dodge this with Rb8 , known as the Chinese Dragon.

The Classical Variation, 6. Be2 , is the oldest White response to the Dragon. It is the second most common White response behind the Yugoslav Attack.

After Bg7 , White has two main continuations:. The Levenfish Attack, 6. It is not currently very common in the highest levels in chess, though Vassily Ivanchuk scored a nice win with it recently.

White prepares 7. Nc6 or Nbd7 were considered mandatory to meet the Levenfish Attack; however, it has transpired that after Bg7 7.

The Harrington—Glek Variation is another option for White. Named after Grandmaster Igor Glek who has devoted considerable effort evaluating the resulting positions for White.

Be3 Bg7 7. Be2 8. GM John Emms wrote, "Although it's difficult to beat the Yugoslav in terms of sharp, aggressive play, 7.

White's plans include queenside castling and a kingside attack. And there's a major plus point in that it's much, much less theoretical!

When Black adopts the Dragon formation without Music Dragon. Meteor Dragon. Lava Dragon. Hydra Dragon. Justice Dragon. Burning Dragon.

Quetzal Dragon. Soccer Dragon. Frozen Wind Dragon. Chimera Dragon. Master Dragon. One of these will be shown in the list and appear when you open the final box: Burning Dragon.

Blue Fire Dragon. Clover Dragon. Eclipse Dragon. Dark Elf Dragon. Blind Dragon. Giant Wings Dragon. Sylvan Dragon.

Celtic Dragon Black Market! White what? What witch?

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Sign In Don't have an account? Start a Wiki. Black Market - Dragon History Status. File:Rock Star Dragon m3.

One of these will be shown in the list and appear in the first box you choose: Storm Dragon. Hot Metal Dragon.

Medieval Dragon. Star Dragon. Ice Cream Dragon. Icecube Dragon. Neon Dragon. Battery Dragon. Mojito Dragon. Petroleum Dragon. Two of these will be shown in the list and appear in the 2nd and 3rd boxes you choose: Rattlesnake Dragon.

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Moose Dragon. Vampire Dragon. Gummy Dragon. Carnivore Plant Dragon. Penguin Dragon. One of these will be shown in the list and appear in the 4th box you choose: Thief Dragon.

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