One Diagoras, a non believer in the gods, was shown painted tablets bearing the portraits of some worshippers who prayed, then survived, a subsequent shipwreck. The implication was that praying protected you from drowning. Diagoras asked, "Where were those who prayed, then drowned?"The drowned workshippers, being dead, would have a lot of trouble advertising their experiences from the bottom of the sea. This can fool the casual observer into believing in miracles.
Taleb calls this as the problem of silent evidence. We a drawn to success, and we shun failure, so, as Taleb notes, nobody ever writes 'How I failed to Make a Million Dollars on Wall Street'.
Suppose that one of our Wall Street success stories claims that in order to succeed in Wall Street, you need to be a Harvard graduate; this author cites himself and and his college roommate who have both made a million dollars on Wall Street. If you are in high school (or have a child in school), you need to choose a collage. You are wanting to know if a Harvard education is worth its rather considerable cost. If the author is right, then the decision is easy. You will simply replay your student loans with the millions you make on Wall Street.
What information do you need to assess this claim? We could begin by looking for particular cases. Finding one black swan may prove that not all swans are white, likewise finding one millionaire from a state university will disprove a claim that a Harvard education is a requirement for success on Wall Street. Similarly, finding an unsuccessful Wall Street trader from Harvard proves that a Harvard education is not sufficient for success on Wall Street. Likewise, we can find examples of Harvard graduates that failed on Wall Street. So being a Harvard Alumni is not a guarantee of success of Wall Street.
Life is not certain, so we need to make a gamble. How can we to play the odds odds intelligently? The sort of data that would be helpful would include the resumes of both successful and failed Wall Street investors. As a practical matter, getting the list of unsuccessful investors would be something of a trick. Taleb is right about silent evidence: we remember the winners and forget the losers. The winners advertise and the losers go on to something else.
In the age of data mining and databases, it should be possible to build such as list.
If we can gather the data, what framework should we use to decide if going to Harvard would be a rational risk? The correct framework to assess the validity of a claim is to use Bayes' Theorem from statistics. If you haven't heard about Bayesian statistics, a good starting point is An Intuitive Explanation of Bayes' Theorem by Eliezer S. Yudkowsky. In fact, Yudkowsky's explanation is so good that there really isn't any point in me writing more on this subject. There are also on-line course materials. Currently, I'm trying to work through Jeff Grynaviski's (at the University of Chicago) has provided his course materials. In order to learn more, there is David McKay's Information Theory, Inference and Learning Algorithms. I especially like McKay's book because it is able to unite Bayesian methods with Claude Shanon's Information Theory and even to include elements of AI.
If you still have some time left after reading about Bayesian methods, read Yudkowsky's article Cognitive biases potentially affecting judgement of global risk. His assessment seems to be in general agreement with Taleb. Since I live next to Cedar Rapids, which has just had a major flood, Yudkowsky's observation about flood damage was particularly revealing:
Wow, this is an extraordinary claim. If true, the much of the work done to protect people in the Mississippi/Missouri River basins is not just useless but is actually counterproductive. I will remain skeptical on this claim, but it does seem worthy of investigation before Cedar Rapids, Linn County, the State of Iowa and Federal agencies sink more money into flood control on the Cedar River. We need to find a more rational way to understand and manage risk. If we can improve that, we will have learned an important lesson from the catastrophic failures of 9/11, the Indian Ocean Tsunami of 2004, the flooding of New Orleans following Hurricane Katrina, and the current financial collapse. We need Taleb's empiricism. When we have enough data to analyze, we should be using Bayesian methods for that analysis.
Burton et. al. (1978) report that when dams and levees are built, they reduce the frequency of floods, and thus apparently create a false sense of security, leading to reduced precautions. While building dams decreases the frequency of floods, damage per flood is so much greater afterward that the average yearly damage increases.
Wow, this is an extraordinary claim. If true, the much of the work done to protect people in the Mississippi/Missouri River basins is not just useless but is actually counterproductive. I will remain skeptical on this claim, but it does seem worthy of investigation before Cedar Rapids, Linn County, the State of Iowa and Federal agencies sink more money into flood control on the Cedar River. We need to find a more rational way to understand and manage risk. If we can improve that, we will have learned an important lesson from the catastrophic failures of 9/11, the Indian Ocean Tsunami of 2004, the flooding of New Orleans following Hurricane Katrina, and the current financial collapse. We need Taleb's empiricism. When we have enough data to analyze, we should be using Bayesian methods for that analysis.
No comments:
Post a Comment