Fooled by randomness
"[Taleb is] Wall Street's principal dissident.... [Fooled By Randomness] is to conventional Wall Street wisdom approximately what Martin Luther's ninety-nine theses were to the Catholic Church."--Malcolm Gladwell, The New Yorker Finally in paperback, the word-of-mouth sensation that will change the way you think about the markets and the world. This book is about luck: more precisely how we perceive luck in our personal and professional experiences. Set against the backdrop of the most conspicuous forum in which luck is mistaken for skill--the world of business--Fooled by Randomness is an irreverent, iconoclastic, eye-opening, and endlessly entertaining exploration of one of the least understood forces in all of our lives. — From the Trade Paperback edition.
Stats for nerds
195recommend
403mentions
307people
2007–2026years
63% recommend34% neutral3% critical
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What Hacker News says
- There are a number of good books to explain the main concepts of statistics without getting bogged down in the technical details. As a grad student at a public university in the USA, I worked as a teaching assistant for the undergraduate course Introduction to Statistics. The course used SPSS software to enable the students to perform statistical analysis on data sets. Although we offered a good textbook (listed below), many of the students struggled with the main concepts, so my colleagues and I added a optional supplemental reading list to the syllabus. The following books are the ones we recommended to the students. I have read all these books, and they are pretty good at explaining statistics to anyone interested in learning the material at a conceptual level; anyone can read and understand these books, regardless of their educational background.
Ian Ayres (2007) Super Crunchers: Why Thinking by Numbers is the New Way to Be Smart.
Joseph Healey (2020) Statistics: A Tool for Social Research [This is the textbook used in the class, which I highly recommend].
John Allen Paulos (1988) Innumeracy: Mathematical Illiteracy and Its Consequences.
Nate Silver (2012) The Signal and the Noise: Why So Many Predictions Fail, but Some Don't.
Nassim Nicholas Taleb (2005) Fooled by Randomness, second edition.
Nassim Nicholas Taleb (2010) The Black Swan: The Impact of the Highly Improbable, second edition.
- As a grad student at a public university in the USA, I worked as a teaching assistant for the sociology class "Introduction to Statistics." The course used SPSS software to enable the students to perform statistical analysis on data sets. Although we offered an excellent textbook (listed below), most of the undergraduate students struggled with the material, so my colleagues and I added a supplemental reading list to the syllabus for those students interested in studying the main concepts. The following books are the ones we recommended to the students. I have read all these texts, and they are pretty good at explaining statistics to anyone interested in learning the material at a conceptual level; anyone can read and understand these books, regardless of their educational background.
Ian Ayres (2007) Super Crunchers: Why Thinking by Numbers is the New Way to Be Smart
Joseph Healey (2020) Statistics: A Tool for Social Research [This is the textbook used in the class, which I highly recommend]
Catherine Marsh and Jane Elliott (2008) Exploring Data: An Introduction to Data Analysis for Social Scientists, second edition
John Allen Paulos (1988) Innumeracy: Mathematical Illiteracy and Its Consequences
Nate Silver (2012) The Signal and the Noise: Why So Many Predictions Fail, but Some Don't
Nassim Nicholas Taleb (2005) Fooled by Randomness, second edition;
_______ (2010) The Black Swan: The Impact of the Highly Improbable, second edition
- In investing it's critical to think about things probabilistically, rather than trying to just guess the future. "Fooled by Randomness" is a great book on this. In a scenario like this it's especially critical, because in the case where Starship fully delivers and the next great frontier of humanity opens up, the upside potentially is practically infinite.
E.g. imagine in a bet you think there's a 99% chance of something being worth $1 and a 1% chance of it being worth $1 million. What's a fair price? It's a simple calculation - 0.99 * 1 + 0.01 * 1,000,000 = ~$10,000. That's you setting a price of $10k on something that you fully expect to be worth $1 99% of the time, and still coming out just fine.
So the actual debate should not be on 'guess the future' but rather on the odds of Starship delivering and estimating the impact this would have. And I think to many the answers are fairly easy - 'very nonzero' and 'very big.' It's akin to the NVidia/Apple stories, except in this case it's being somewhat priced into the market to begin with because it's somewhat more probable, and easier to foresee.
- Empire of AI by Karen Hao. The whole world is going crazy for AI. This book brings the story of what actually goes on within those companies.
Fooled by Randomness by Nassim Nicholas Taleb
- lol, someone needs to go read Fooled By Randomness
1. currency predictions are all more affected by randomness than anything else - this is why only people who want to lose their money invest it in, say, pesos or roubles (many have done it, many have "blown up" as per the author) 2. LLMs "predict" by following past trends, which are going to be fallible to randomness.
So, it may come to pass, or likely will not, as the odds against currency valuation predictions are enormous and random.
- If you like anti-fragile, I really recommend Fooled by Randomness and Black Swan. The Black Swan is a good example of a book with a good original idea that gets repeated so often its almost conventional wisdom, except that half the people saying it are misusing the term because they didn’t read the book.
