I Spent $2,995 on Nassim Taleb’s Risk Taking Course — Here’s what I learned

🌈 Abstract

The article discusses key takeaways from Nassim Taleb's Real World Risk Institute (RWRI) seminar, focusing on unconventional features of reality such as complexity, emergence, irreducibility, and fat tails that are often ignored in traditional risk management frameworks. The author shares insights on the Lindy effect, the dangers of single-metric optimization, balancing passion and profitability, computational irreducibility, and the challenges of fat-tailed distributions.

🙋 Q&A

[01] The Lindy Effect

1. What is the Lindy Effect, and how does it apply to business survival? The Lindy Effect is a phenomenon where the longer a thing has survived, the longer it is expected to survive. In the context of businesses, the article shows that the older a business gets, the higher its marginal survival rate. For example, the probability of a 10-year-old business surviving an additional 4 years is 80%, compared to 54% for a new business.

2. What is the key takeaway for entrepreneurs from the Lindy Effect? The key takeaway for entrepreneurs is to "keep going." Even though it may feel like a business is not going anywhere, every day the business stays in operation, its chances of success increase incrementally.

[02] Optimizing for a Single Metric

1. What is the danger of optimizing for a single metric? The article highlights that optimizing for a single metric can lead to unintended consequences and vulnerabilities. The world is complex, with interconnected elements interacting in nonlinear ways, so focusing solely on maximizing one aspect can negatively impact other important factors.

2. What example does the author provide of the pitfalls of single-metric optimization? The author shares an example of optimizing his YouTube channel solely for the number of videos posted. While this led to a doubling of video output, other important metrics like leads and paid calls went down, as the author spent less time on video quality and customer engagement.

[03] Balancing Passion and Profitability

1. What advice did Stephen Wolfram provide the author on balancing what he loves and what makes money? The author does not provide the full details of Wolfram's advice, but states that the key takeaways were that some things don't have shortcuts, and that there is a need to balance one's passion and what is profitable.

[04] Computational Irreducibility

1. What is the concept of computational irreducibility, and what are its implications? Computational irreducibility states that some computational processes don't have shortcuts - the only way to predict the outcome is to run the full process step-by-step. This means that some phenomena, like predicting the death toll of a pandemic, are inherently unpredictable and cannot be reduced to a simple equation.

2. How does the bias for computationally reducible things get us into trouble? The article notes that we have a bias for computationally reducible things because they fit neatly into our mental models. However, this can lead us to incorrectly assume that irreducible processes are reducible, such as in the case of financial markets, consumer behavior, and audience interests.

[05] Fat-Tailed Distributions

1. What are fat-tailed distributions, and what are the challenges they present? Fat-tailed distributions describe quantities where rare events drive the aggregate statistics. The key challenges are that 1) we need a large volume of data to accurately estimate their statistical properties, and 2) they can appear thin-tailed, leading to the "Masquerade Problem" where we mistakenly think they are predictable.

2. What is the "Lucretius problem" and how does it relate to fat-tailed distributions? The Lucretius problem is the tendency to think that the largest event we've observed is the worst-case scenario. However, with fat-tailed distributions, much larger events are statistically consistent, so we should not assume the worst has already happened.

[06] Thriving in an Unpredictable World

1. What is the key approach the author recommends for navigating an unpredictable world? The author recommends leveraging trial and error, similar to the process of cooking - experiment, evaluate the results, and iterate. The key is to have a high volume of experiments combined with high-quality judgment to eventually find success.

2. How does the author characterize highly productive people like Elon Musk in this context? The author suggests that highly productive people like Elon Musk seem to have a "superhuman ability" to find success in incredibly unpredictable enterprises, which the author attributes to their ability to combine high-volume experimentation with high-quality judgment.