In 2005, an Athens-raised medical researcher named John P. Ioannidis published a controversial paper titled "Why Most Published Research Findings Are False." The paper studied positive findings documented in peer-reviewed journals: descriptions of successful predictions of medical hypotheses carried out in laboratory experiments. It concluded that most of these findings were likely to fail when applied in the real world. Bayer laboratories recently confirmed Ioannidis's hypothesis. They could not replicate about two-thirds of the positive findings claimed in medical journals when they attempted the experiments themselves. (查看原文)
The most calamitous failures of prediction usually have a lot in common. We focus on those signals that tell a story about the world as we would like it to be, not how it really is.
Nobody saw it coming. When you can't state your innocence, proclaim your ignorance: this is often the first line of defense when there is a failed forecast.
"The housing crash was not a black swan. The housing crash was the elephant in the room. (Paul Krugman) (查看原文)
They knew how to sniff out a scam-such as the case of the kidnappers who took a hedge-fund billionaire hostage but foiled themselves by charging a pizza to his credit card.
In some cases, the ratings agencies went further still and abetted debt issuers in manipulating the ratings.
"Unknown unknowns"-the risk that we are not even aware of. Perhaps the only greater threat is the risk we think we have a handle on, but don't. In the case of the ratings agencies, it helped to infect the entire financial system. (查看原文)
It might have been fine had the potential for error in their forecasts been linear and arithmetic. But leverage, or investments financed by debt, can make the error in a forecast compound many times over, and introduces the potential of highly geometric and nonlinear mistakes.
Risk: It is not pleasant when you take a "bad beat" in poker, but at least you know the odds of it and can account for it ahead of time.
Uncertainty: Your back-of-the-envelope estimate might be off by a factor of 100 or by a factor of 1,000; there is no good way to know.
Risk greases the wheels of a free-market economy; uncertainty grinds them to a halt.
The alchemy that the ratings agencies performed was to spin uncertainty into what looked and felt like risk. (查看原文)
But if a home was not a profitable investment it had at least been a safe one.
The infamous Japanese real estate bubble of the early 1990s forms a particularly eerie precedent to the recent U.S. housing bubble. (查看原文)
They (The ratings agencies) vouched for mortgage-backed securities with lots of AAA ratings and helped to enable a market for them that might not otherwise have existed. The market was counting on them to be the Debbie Downer of the mortgage party-but they were acting more like Robert Downey, Jr. (查看原文)
It should suffice to remember that when a financial company dies (Lehman Brothers), it can continue to haunt the economy through an afterlife of unmet obligations.
Investors and lenders, hawking at the accident but unsure about who owed what to whom, might become unable to distinguish the solvent companies from the zombies and unwilling to lend money at any price, preventing even healthy companies from functioning effectively.
Larry Summers: the United States might have had a modestly better outcome had it bailed out Lehman Brothers. But with the excess of leverage in the system, some degree of pain was inevitable.
Negative Feedback: a good thing for market V. Positive Feedback.
Some investors have little appetite for risk and some have plenty, but their preferences balance out: if the pr... (查看原文)
The process of disentangling a financial crisis-everyone trying to figure out who owes what to whom-can produce hangovers that persist for a very long time.
Perhaps the White House's more inexcusable error was in making such a precise-seeming forecast-and in failing to prepare the public for the eventuality that it might be wrong.
When you layer the large uncertainty intrinsic to measuring the effects of stimulus atop the large uncertainty intrinsic to making macroeconomic forecasts of any kind, you have the potential for a prediction that goes very bad. (查看原文)
Out-of-sample problem: Your sample size for drunk driving is not 20,000 trips but zero, and you have no way to use your past experience to forecast your accident risk. (查看原文)
We forget-or we willfully ignore-that our models are simplifications of the world. We figure that if we make a mistake, it will be at the margin.
In a complex systems, however, mistakes are not measured in degrees but in whole orders of magnitude.
Even if the amount of knowledge in the world is increasing, the gap between what we know and what we think we know may be widening. This syndrome is often associated with very precise-seeming predictions that are not at all accurate.
Financial crisis-and most other failures of prediction-stem from this false sense of confidence. Precise forecasts masquerade as accurate ones, and some of us get fooled and double-down our bets. (查看原文)
Our current echo chamber era isn't much different from the shouting match era, except that the liberals and conservatives are confined to their own channels, separated in your cable lineup by a demilitarized zone demarcated by the Food Network or the Golf Channel. This arrangement seems to produce higher ratings if not necessarily more reliable analysis. (查看原文)
作者: [美] 纳特•西尔弗 副标题: 大数据时代预测的科学与艺术 isbn: 7508641140 书名: 信号与噪声 页数: 432 译者:胡晓姣, 张新, 朱辰辰 定价: 69.00元 原作名: The Signal and the Noise: Why Most Predictions Fail but Some Don't 出版社: 中信出版社 出版年: 2013-8 装帧: 精装
