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The burden of proof of safety are on those introducing the novelty. When unsure, defaulting to Nature may be the safer bet. Kruse's narrative does not need to be correct for people to take precaution. http://fooledbyrandomness.com/...

Backing down to a claim of the novelty and unnaturalness of "light", as if there was no research or empirical evidence to build from is not really a convincing proposition.

A minimal amount of research should make you highly suspicious of any claims made by dr jack https://jackkruse.com/store/

The burden of proof of safety are on those introducing the novelty. When unsure, defaulting to Nature may be the safer bet. Kruse's narrative does not need to be correct for people to take precaution. http://fooledbyrandomness.com/...

The Precautionary Principle(with Application to the Genetic Modification ofOrganisms)Nassim Nicholas Taleb, Rupert Read, Raphael Douady, Joseph Norman,Yaneer Bar-YamSchool of Engineering, New York UniversityNew England Complex Systems InstituteInstitute of Mathematics and Theoretical Physics, C.N.R.S., ParisSchool of Philosophy, University of East Anglia

Just because some species can coexist compatibly with human agriculture doesn't mean that it's "great for ecosystems". Quite the opposite in most cases.

The beautiful thing about nature; that which does survive the change in ecosystems, becomes the new ecosystem. Good, Bad, indifferent... doesn't really matter.

A few ways to think about that statement...

"Annual agriculture is all about living through our concepts... our idea we've imposed on reality & when reality doesn't behave according to our idea, what do we do? We input... we can never input enough to make our false concept correct." http://bit.ly/1GnbtAA

"The middle east today is what annual ag does." http://bit.ly/1K3otw2

"Ecology... Nature is only model we have that has survived climate change with sheer, total, utter neglect..." http://bit.ly/1ohVqpE

http://fooledbyrandomness.com/...

We have only one planet. This fact radically constrains the kinds of risks that are appropriate to take at a large scale. Even a risk with a very low probability becomes unacceptable when it affects all of us – there is no reversing mistakes of that magnitude. Without any precise models, we can still reason that polluting or altering our environment significantly could put us in uncharted territory, with no statistical track- record and potentially large consequences. It is at the core of both scientific decision making and ancestral wisdom to take seriously absence of evidence when the consequences of an action can be large. And it is standard textbook decision theory that a policy should depend at least as much on uncertainty concerning the adverse consequences as it does on the known effects. http://fooledbyrandomness.com/...

Just because some species can coexist compatibly with human agriculture doesn't mean that it's "great for ecosystems". Quite the opposite in most cases.

The beautiful thing about nature; that which does survive the change in ecosystems, becomes the new ecosystem. Good, Bad, indifferent... doesn't really matter.

A few ways to think about that statement...

"Annual agriculture is all about living through our concepts... our idea we've imposed on reality & when reality doesn't behave according to our idea, what do we do? We input... we can never input enough to make our false concept correct." http://bit.ly/1GnbtAA

"The middle east today is what annual ag does." http://bit.ly/1K3otw2

"Ecology... Nature is only model we have that has survived climate change with sheer, total, utter neglect..." http://bit.ly/1ohVqpE

http://fooledbyrandomness.com/...

We have only one planet. This fact radically constrains the kinds of risks that are appropriate to take at a large scale. Even a risk with a very low probability becomes unacceptable when it affects all of us – there is no reversing mistakes of that magnitude. Without any precise models, we can still reason that polluting or altering our environment significantly could put us in uncharted territory, with no statistical track- record and potentially large consequences. It is at the core of both scientific decision making and ancestral wisdom to take seriously absence of evidence when the consequences of an action can be large. And it is standard textbook decision theory that a policy should depend at least as much on uncertainty concerning the adverse consequences as it does on the known effects. http://fooledbyrandomness.com/...

"what would the correct policy be if we had no reliable models?" http://fooledbyrandomness.com/...

The Precautionary Principle (with Application to the Genetic Modification of Organisms)

Pinker claims have been debunked as available histirical data is compatible with a hypothesis that nothing has changed regarding violence for the last 3000 years, http://www.fooledbyrandomness....

I agree whole heartedly. Here is a recent, rigorous and relevant paper advocating a non-naive precautionary principle (much like you are): http://www.fooledbyrandomness....

Nassim Nicholas Taleb, the author of The Black Swan and Fooled by Randomness, has a book chapter coming out that addresses this danger. Prof. Teleb's draft chapter on Medicine, Convexity, and Opacity from his upcoming book, Antifragile: Things That Gain from Disorder, can be found at:

http://www.fooledbyrandomness.com/medicine.pdf

While the entire chapter is worth a read, at page 389 he observes:

The “do you have evidence” fallacy, mistaking evidence of no harm for no evidence of harm, is similar to the one of misinterpreting NED (no evidence of disease) for evidence of no disease. This is the same error as mistaking absence of evidence for evidence of absence, the one that tends to affect smart and educated people, as if education made people more confirmatory in their responses and more liable to fall into simple logical errors.

That may have been the case here. That is, for years no evidence of harm was mistaken for evidence of no harm.

More generally, Prof. Taleb argues at page 376:

Simple, quite simple decision rules and heuristics emerge from this chapter. Via negativa, of course (by removal of the unnatural): resort to medical techniques when the health payoff is very large (say, saving a life) and visibly exceeds its potential harm, such as incontrovertibly needed surgery or lifesaving medicine (penicillin). It is the same as with government intervention. This is squarely Thalesian, not Aristotelian (that is, decision making based on payoffs, not knowledge). For in these cases medicine has positive asymmetries —convexity effects— and the outcome will be less likely to produce fragility. Otherwise, in situations in which the benefits of a particular medicine, procedure, or nutritional or lifestyle modification appear small—say, those aiming for comfort—we have a large potential sucker problem (hence putting us on the wrong side of convexity effects).

Nassim Nicholas Taleb, the author of The Black Swan and Fooled by Randomness, has a book chapter coming out that addresses this danger. Prof. Teleb's draft chapter on Medicine, Convexity, and Opacity from his upcoming book, Antifragile: Things That Gain from Disorder, can be found at:

http://www.fooledbyrandomness.com/medicine.pdf

While the entire chapter is worth a read, at page 389 he observes:

The “do you have evidence” fallacy, mistaking evidence of no harm for no evidence of harm, is similar to the one of misinterpreting NED (no evidence of disease) for evidence of no disease. This is the same error as mistaking absence of evidence for evidence of absence, the one that tends to affect smart and educated people, as if education made people more confirmatory in their responses and more liable to fall into simple logical errors.

That may have been the case here. That is, for years no evidence of harm was mistaken for evidence of no harm.

More generally, Prof. Taleb argues at page 376:

Simple, quite simple decision rules and heuristics emerge from this chapter. Via negativa, of course (by removal of the unnatural): resort to medical techniques when the health payoff is very large (say, saving a life) and visibly exceeds its potential harm, such as incontrovertibly needed surgery or lifesaving medicine (penicillin). It is the same as with government intervention. This is squarely Thalesian, not Aristotelian (that is, decision making based on payoffs, not knowledge). For in these cases medicine has positive asymmetries —convexity effects— and the outcome will be less likely to produce fragility. Otherwise, in situations in which the benefits of a particular medicine, procedure, or nutritional or lifestyle modification appear small—say, those aiming for comfort—we have a large potential sucker problem (hence putting us on the wrong side of convexity effects).

This dialog is a bit of a mess, but makes some good points: Taleb on Antifragility

These talks come with very loose transcripts. Here's the key passage at length as I shamelessly promote Taleb's upcoming book Antifragility , through I'm already certain I only agree with two-thirds of what he is putting forth (emphasis mine):

It's because of convexity effects, because small probability is very convex to error. [] Take the Gaussian distribution. And actually in a separate paper I finally proved something that has taken me three years. Take a very thin-tailed distribution such as the Gaussian. Thin-tailed, the normal distribution. You have two inputs, one of which is standard deviation. Standard deviation is very much your error. Now, if you take a remote event, say, 6, 7, 8 sigmas, you increase the standard deviation away from the mean; you increase the sigma by 10%, the probability of that is multiplied by several thousand, several million, several billion, several trillions. So, what you have, you have nonlinearity of remote events to sigma, to the standard deviation of the distribution. And that, in fact if you have uncertainty, the smallest uncertainty you have in the estimation of the standard deviation, the higher the small probability becomes and at the same time, the bigger the mistake you are going to have about the small probability. So, in other words, most of the uncertainty in parameterizing the model, most of the tails. So, you take an event like Fukushima, you see, where they said it should happen every million years; you perturbate probabilities a little bit and one in a million becomes one in thirty. Or the financial crisis. Or anything.

Some of those sigmas are model guards, not actual certainty.

Do not confuse popularity with quality. After all, Twilight probably gets read more than Solzhenitsyn's works.

To quote Taleb:

"Hard work will get you a professorship or a BMW. You need both work and luck for a Booker, a Nobel or a private jet."

Now, that is not to say that Tolkien's work was not good. But from a literary perspective, it was (and is) indeed quite mediocre.

For a few temporary small benefits, people are willing to accept enormous potential damage. That is my personal definition of evil.

For me that is definition of sucker.

I'm a long way from New York, so someone correct me if I'm wrong[*], but I've always understood the problem to lie more with the people feeding data into the equations, rather than with the equations themselves.

Now, I accept that risk calculations consisted of a great deal of voodoo because, as Taleb tells us, they tended to ignore 'Black Swan' events (where the 1 in a million catastrophe wasn't going to happen just yet) and saw patterns where only chaos existed, but as I understand it, the core of the problem was simple greed: money-hungry mortgage and securities dealers deliberately feeding bad data into the system.

So-called quants may be decidedly imperfect, but if someone's willing to game the system to make a buck, nothing the quant does can stop it.

If Wilmott doesn't have an answer to that, I fear that his efforts will only obscure the real problem.

Greed is a motivator. Greedy people will work hard to acquire money. Capitalism & free enterprise allow a society to harness greedy people for positive ends like the creation of jobs to produce valuable goods and services. This is a good thing. Unfortunately, greedy people are not necessarily *smart*. And even the smart greedy people are not necessarily *correct* when they do things a particular way.

The story sums it up nicely - this formula oversimplifies a complex market creating a classic bursting bubble. There's an economist named Taleb http://www.fooledbyrandomness.com/ lecturing about how the market will basically always be more complex than you think.

The best part about his message is in not trusting your data too much. I think of this every time people start talking confidently about geoengineering. We don't know as much as we think we do.

...every economist that I've read says that ironically, that massive layoffs are the beginning of the end of an economic downturn, and that it appears as though things will be back into shape around the end of 2009 or the beginning of 2010, and none of their arguments are contingent upon a stimulus package. In fact, none mention it.

I don't know where you're getting your news, but most economists I've been reading (Krugman, Reich, Roubini, Galbraith, Taleb, etc.) say we're at the beginning, not the end, of a massive downturn. That the stimulus is not only necessary but nowhere near big enough to fill the demand gap created by this crisis. We need about 2 trillion in direct, massive government spending. We're getting only 800 billion (so far), of which a huge proportion is political garbage like tax cuts which are not very effective, AMT stuff which will not create jobs, etc.

Note that these are the same economists who long warned this crisis was coming. We ignore them at our peril.

that so many claim it to be. e.g., Nassim Nicholas Taleb heaps scorn upon several recent winners of the Nobel Prize in economics and demonstrates how their work is completely invalid and incorrect and has contributed greatly to the current economic collapse.

You are absolutely right. And that's what my point was (shit numbers).

I remember asking the business guys that time about this (historical VaR and not scenario based VaR) - asking specifically the practicability of such a number because events like 9/11 would not have any say in those numbers.

Now at this particular bank, they used to do scenario based number crunching as well in parallel (with a grid of over 10000 machines), but it was not used for reporting. WTF? All that meant was they were just discarding some of the 'risks' from the calculations.

Of course, you can not blame the army of quants here, but it was their bosses and bosses of bosses who decided to ignore inconvenience and go with whatever worked for them.

BTW, Here is another interesting (but old) read on the flaws of VaR approach.

Add Fooled by Randomness to that reading list. In one particular example, the author shows that a dentist looking at hour-by-hour market data sees mostly noise, wheres a dentist looking at quarterly reports sees very little noise.

A better approximation can be achieved by modeling the level of rationality of the individual and assigning probabilities based on that.

And yet even this approach has serious problems; normal probability models fail miserably when applied to numerous areas where humans have an influence. Check out Dr. Taleb's The Black Swan: The Impact of the Highly Improbable or at a minimum, check out Dr. Taleb's website for more details.

Taleb's conjecture (which is supported by many others in the fields of behavioral finance & economics) is that models based on normal distributions (the primary model in Taleb's "mediocristan") fail when they encounter conditions often found with human behavior that seriously violate the prerequisites for such a distribution. For example, a normal distribution requires that each flip of a coin be an independent event and have no outcome bias from previous heads or tails. Unfortunately, many human events tend to violate this precondition. Winners tend to win more, and losers lose more, in human situations possibly due to herding behavior (e.g. people feel they will be safer from harm by being around a winner, thereby assigning more resources to the winner and allowing them to win even more). This condition alone damages normal distribution models and tends to yield what is called a fractal distribution (also known as a "power law" distribution).

Other conditions, such as bimodal and multimodal models, further distort the use of normal distribution approaches in forecasting events. Some suggest the economy is bimodal, having a "good economy" ruleset and a "bad economy" ruleset based on the majority perception. A friend who manages a multi-billion dollar mutual fund has conjectured that U.S. financial markets are multi-modal, with its behavior corresponding to the financial belief system of the dominant participants. E.g. the dot-com boom was driven by non-technical investors who believed in the rule of capital appreciation (and totally ignored models like dividend discount model and such for equity valuation). Following that bust and their departure from the market, those who remained in the market were mostly those educated in classical financial analysis methods, and subsequently the market tended to behave following their rules for a few years (mostly 2002-2004). When technical analysis investors tend to get active, you'll find equities of interest to the TAs tend to start following the TA rules - mostly because those who are interacting with it daily believe in them. This goes on until a different dominant force influences the mode, often due to opportunities being discovered by users of other models that are missed in the current one. This approach was also heavily practiced by George Soros, who would raid an investment target when the current mode carried it way out of line, creating unique risks (such as liquidity risks) which the current model didn't recognize. Multi-modal distributions cause normal distribution approaches to fail miserably as the modes tend to be organic and have considerable influence from exogenous events.

Then again, it's not terribly surprising that grants such as these are given. Most of our financial and risk analysts are trained in classical models and are constantly shocked when the real world doesn't behave as such. If you're interested in this kind of stuff, or find amusement in the failure of supposedly smart people to predict stuff, check out "Why Most Things Fail" by Paul Ormerod,

Who paid your minimum wage as a subway wage slave? The subway customers... most of whom weren't minimum wage workers themselves. Take away the minimum wage, and the employers go on a "race to the bottom" of what the employment market will bear.

In the absence of a social benefit, in a place where all the resources are controlled by the rich, and in the presence of a desperate need to eat and feed your family.... the bottom can be very very very low indeed.

So it is clear who pays for a lack of social benefit, the very poor, and the lower middle class exposed to increased crimes of desperation.

Taxes being generally redistributive, means the upper middle class pays for the social benefit. (The super rich generally, having the most options, generally escape quite a lot of taxation.) (My current favourite economics related Author is Nicolas Nassim Taleb, author of "The Black Swan" http://www.fooledbyrandomness.com/) As he points out, wealth, unlike weight, is from Extremistan.... the wealth of the richest person in a randomly selected group significantly affects the average.

The fact you made it from India to Chicago means you are rich. Rich in strength, talent and drive. So you probably would have done quite well (relatively speaking) in India or anywhere. ie. You are intrinsically fortunate, but spare a thought for your children, they may perhaps not be...

The (Mis)Behavior of Markets, by Benoit Mandelbrot and Richard L. Hudson.

Fooled by Randomness, by Nassim Nicholas Taleb.

A Random Walk Down Wall Street, by Burton G. Malkiel.

All of these are fascinating reading, and highly recommended. Certainly anyone who pays attention to CNNfn and the rest should read Taleb; anyone who's trading options (and relying on an option pricing model) should read Taleb and Mandelbrot; and anyone who still thinks that investing in mutual funds is a good idea should read all three. The parent's remark about survivorship bias is right on target, and that's by no means the only pitfall out there.

Kessler is a fun and lively narrator. He strikes me as the kind of guy that would be tons of fun at a party.

For a more serious look at markets, check out Nassim Taleb.

Yep I immediated thought of the book Fooled By Randomness by Nassim Nicholas Taleb.
He has some very good insights about the markets and human behavior.