Astral Codex Ten

Southeast Asia

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Southeast Asia

Article

Southeast Asia is a recurring place in the Astral Codex Ten archive, appearing 5 times across 5 issues between May 21, 2021 and July 21, 2023. The archive places it in contexts such as “a free-trade network in Southeast Asia”; “He runs through the trade of Southeast Asia and suggests that American cooperation in the area”; “He runs through the trade of Southeast Asia”. It most often appears alongside Britain, America, Europe.

Metadata

  • Category: Places
  • Mention count: 5
  • Issue count: 5
  • First seen: May 21, 2021
  • Last seen: July 21, 2023

Appears In

Source Context

Recovered passages from the original issue text. When the raw archive preserved outbound links inside the source passage, they are listed directly under the quote.

Your Book Review: The Accidental Superpower
May 21, 2021 · Original source
While Bretton Woods was America’s plan, presumably to benefit America, it had some nice side effects for the world. Zeihan credits it with leading to peace between former enemies, a new era of cooperation (France and Germany forming the EU; Sweden and the Netherlands focusing on trade; a free-trade network in Southeast Asia), and ending colonialism:
These predictions include some persuasive analysis of many countries and plenty of speculation to go with it. Zeihan spends a chapter highlighting America’s partners in the chaos to come. At the top of the list are its North American neighbors, and a prediction that Cuba will be pulled back into the American orbit (because a larger power that supported it could cut off trade with the greater Mississippi system – Zeihan’s summary of exactly why America was willing to risk nuclear war in the Cuban Missile Crisis). He also gives some analysis of the geography of South America and how it affects their trading patterns, and of the best European allies for various purposes (Denmark and the Netherlands control access to the Rhine and Baltic Sea, making them valuable allies). He runs through the trade of Southeast Asia and suggests that American cooperation in the area could have a strategic benefit of helping to “keep China and India apart.”
6 Apparently Germany and Japan would have found it to be unbelievable. “The primary reason Germany and Japan had launched World War II in the first place was to gain greater access to resources and markets. Germany wanted the agricultural output of Poland, the capital of the Low Countries, the coal of Central Europe, and the markets of France. Japan coveted the manpower and markets of China and the resources of Southeast Asia. Now that they had been thoroughly defeated, the Americans were offering them economic access far beyond their wildest prewar longings: risk-free access to ample resources and bottomless markets a half a world away. And “all” it would cost them was accepting a security guarantee that was better than anything they could ever have achieved by themselves.”
Inline links: 6
Contra Smith On Jewish Selective Immigration
June 14, 2021 · Original source
On the one hand, I agree that this question has lots of interesting context - though I would have chosen the history of other "market minorities" like the Lebanese or the Chinese in Southeast Asia. On the other, I'm not sure that the Scottish Golden Age is really appropriate. I'm not an expert in this period, but it sounds like the kind of thing that had something to do with increased economic growth, trade, and an improving intellectual climate in Scotland. Just to randomly speculate, Scotland had just joined in a Union with England, right as England was inventing industrialization - surely a good climate for a Golden Age to start in. It's much harder to explain Jewish achievement through similar means, because Jews are so intermixed with other populations. Whatever political and economic currents were affecting Albert Einstein or Noam Chomsky or whoever else, ought to also be affecting their Gentile friends and neighbors too. How come they didn't? To me that's a much bigger mystery than whatever happened in Scotland.
Book Review: Crazy Like Us
July 15, 2021 · Original source
In 2004, a magnitude 9 earthquake struck the Indian Ocean. The resulting tsunami devastated Southeast Asia. One of the worst-affected countries was Sri Lanka, where 30,000 people died and millions were left homeless.
Your Book Review: The Dawn Of Everything
June 10, 2022 · Original source
Carefully working through ethnographic accounts of existing egalitarian foraging bands in Africa, South America and Southeast Asia, Boehm identifies a whole panoply of tactics collectively employed to bring would-be braggarts and bullies down to earth—ridicule, shaming, shunning. . .
Your Book Review: The Laws of Trading
July 21, 2023 · Original source
“Residential real estate has historically returned significantly below equity markets over long time horizons” But I’m not so sure that these lessons are directly applicable to other areas of life. Some of the best things in life come from lashing yourself to the mast, burning the boats behind you, willingly giving up liquidity. The deepest monogamous relationships are built from an irrational investment in one other person, saying “In sickness and in health, until death do us part.” How many scientific problems were solved because one person had an irrational willingness to: Just. Keep. Going. Sometimes it’s powerful to use the sunk cost fallacy to your advantage. Investing in relationships, subject matter expertise, even putting down roots via *gulp* homeownership reduces your liquidity, but also leads to some of the best (if intangible) things in life. 5: Edge If you can’t explain your edge in five minutes, you don’t have a very good one. OR The long-term profitability of an edge is inversely proportional to how long it takes to explain it. The Efficient Market Hypothesis is one of the core concepts taught in Finance 101. The Efficient Market Hypothesis is a lie. The person that better understands the nature of a small sliver of the world (e.g. Apple’s share price) will make more money than others. Modern financial markets are exceedingly competitive. This means that the bigger you think your edge is, the more likely it is that you’re wrong. “Evolutionary thinking applies quite directly when thinking about the evolution of markets. Having an edge in a mature market means understanding the world better than other traders, even ones who are already highly skilled. In fact, the marginal trader in modern financial markets is quite sophisticated and skilled indeed.” Lebron here warns us of getting too cute with data, of changing variables. Enough randomness will produce an “edge” that is likely to break down the second a trading strategy hits the real world. You can always find a statistical correlation if you change enough variables. But this is fundamentally the same problem facing the replication crisis in social sciences. Lebron argues that we need stories here. Edge is expressed in stories: an edge does not exist without a clear mental representation of that edge. Pure linear algebra does not suffice. I’m not so sure. It seems like AI companies are pushing forward technology in a way that suggests that mental representations are not the only path to intelligence. Lebron discounts “black box” trading strategies without much discussion of their potential merits. Are all of RenTech’s models explainable by a story? The firm is notoriously secretive, so I don’t know, but I’d guess not. “Frequently a good trade appears, has a seemingly insurmountable difficulty, and it is mere persistence that knocks down the final barrier. There may have been many others who looked at the idea, wanted to do it, but couldn’t get past that last hurdle.” Before Sam Bankman-Fried was the face of Why Effective Altruism is Bad, before he even founded FTX, he made money arbitraging the difference between Bitcoin prices on Japanese and American exchanges. I’m reminded of that trade here. It isn’t a particularly elegant trade, it doesn’t require deep technical knowledge or any models. It was a schlep. It was all operational work: figuring out how to open a Japanese bank account, transferring money between the US and Japan, standing in line for hours every day at both US and Japanese banks (presumably this wasn’t the same person). In as technical a field as trading, sheer willpower is often what gets things done in the end. 6: Models The model expresses the edge. Lebron drills into us that a model is the tool for expressing an edge. The model is not the edge. The model does not give us unique knowledge about the world. The map is not the territory. He dives into the difference between generative (G) and phenomenological (P) models. G models express a worldview and fit data into that way of thinking, whereas P models solely look at the empirical data to build a worldview. Models of the world differ from models of markets, though. Markets have quick feedback loops, are explicit in terms of what they measure, and are easy to quantify at a specific point in time. Most of our models for the world, though, are ill-defined and explicit. Models are only as good as our assumptions. As an aside, this is a common criticism of rationality or Effective Altruism – you can justify any worldview if you assign your model input weights in just the right way5. I also tend to think that “traditional” EA is overly dependent on P models, and doesn’t embrace the G models that led to economic reforms in India in the 1990s or the economic policies that led to rapid economic development in Southeast Asia in the second half of the 20th Century. Interestingly, I think a lot of longtermist EA, specifically AI alignment, leans the other way, relying on G models which explicitly assume a certain P(doom) and work backwards from there. (Though I won’t pretend to be an expert here or to understand everything, so take this with a grain of salt.) Overall, startups and tech seem to take heed to Lebron’s lesson much better than the folks hanging out on this part of the internet: “Even if a model makes good predictions about some future value or event, that knowledge is useless without also knowing how to take advantage of that prediction.” Now we get a bit philosophical. By acting, you change the nature of the market. Your model predicts things that might not be true as soon as you start trading (and changing the environment) based on it. When you’re right, everyone else sees the same trades that your model does and will beat you to them. When your model is wrong, others don’t act, meaning adverse selection rears its ugly head once again. So your model shows you with an edge, but in practice you only make the trades where you don’t have an edge. Lebron closes by arguing that G models are best for understanding other people, and are good in and of themselves: “You can also see connections to traditional moral philosophy in thinking about modeling the behavior of others. To have a good G model about someone else is to have some measure of empathy and compassion for that person: what they’re like, what they think and feel, putting yourself in their shoes. Pragmatically, developing the skill of empathy and compassion for others is, aside from a moral good in itself, an excellent way to understand better the people who surround you. More people working to develop good G models of others is surely a small step to a better world.” 7: Costs and Capacity If you think your costs are negligible relative to your edge, you’re wrong about at least one of them. This section of the book displayed a good amount of epistemic humility, words that I didn’t expect to be typing in the context of a book about trading. Lebron tells us that trades don’t exist independently in the universe — in the n-dimensional space of all possible trades seeking to optimize profitability, if you have a gigantic mountain of profitability, someone else has probably at least discovered the base. So you probably don’t have a profitable trade; rather, you are misunderstanding something about your trade. You’ve either overestimated profitability or underestimated cost. Lebron highlights four types of trading costs: [graph that didn’t show up correctly here: two axes and four quadrants, with the axes being visible ←→ invisible costs and linear ←→ nonlinear costs] Here, we’ll focus on Quadrant 4, where he highlights a few interesting phenomena. Herding. It’s likely that if you have a profitable trading strategy, either: Other firms discovered a similar strategy independently and/or
Inline links: replication crisis, RenTech’s, arbitraging the difference between Bitcoin prices on Japanese and American exchanges, 5