Monday Morning Links

• You can see that when gold peaked out in 1980, it peaked out, at least from a yearly price point of view at 2.5x production cost. We know gold actually spiked up to $850/oz or so. But that was a short term spike that didn’t last long. We can see gold peaked out again in 2012 or so at 1.7x cost of production. With gold at $4,100/oz, it is trading at 2.7x production cost, higher than it was in 1980. But if we use the peak price of $850/oz, that was 3.4x the cost of production, so for us to get near that level, gold would have to spike up to $5,100/oz… another $1,000! But last time gold got that high, it went down for the next 20 years, so who wants to get on that boat?! The title of this post is referring to the stock market, but having written this about gold, I now see that gold is clearly in bubble territory. [The Brookyln Investor]
• What we see with bulk population mortality curves is exactly what we would expect to see if we were monitoring the convergence of thousands of similar simulations, or the same simulation run thousands of times with slightly different initial conditions (such as in weather forecasting). Over time the state quantities gradually diverge from their initial harmony. Integrated homeostatic systems consistently restore equilibrium, but there is hysteresis and loss of information. Homeostatic mechanisms are themselves perturbed by the steadily degrading state, and the resulting feedback is a slow (or fast, depending on perspective) slide into an ever less convergent state. The process is deterministic. In numerical simulations, there are plenty of hacks to try. One could speed up feedback loops, decrease timesteps, reformulate the underlying equations, attempt to add dissipation, filtering, or systemic decoupling. Perhaps the reason exercise and caloric restriction improves life expectancy a bit is because it tempers state excursions relative to the capacity of feedback systems to recenter them? [Casey Handmer]
• People often ask what they can do to generate original contributions or comparative advantages. Usually they vastly overestimate how common it is to have gone through the basic intellectual background in a field. If you've actually read the book (actually checked the proof, actually implemented the algorithm), you're probably way ahead of the field. Much expertise is simply doing this over and over. [Nate Meyvis]
• But now you need to figure out: what should it be that you consume a lot of? For me, it's largely newsletter writers. The economics of this aren't fully clear to me (or anyone else, I think), but the people I'm most willing to bet chunks of my cognitive life on are, disproportionately, writing newsletters or newsletter-adjacent things. Why? It makes them prolific, which fuels componding returns for a good reader (see above). There are massive intellectual benefits to writing on the cadence a newsletter encourages, so the writers are improving most rapidly. [Nate Meyvis]
• Cigarette advertising used to be a huge deal. Tobacco ads were banned from broadcast TV and radio way back in 1971, and the practical upshot was that cigarette companies became some of the biggest boosters of print periodicals. Much later, the 1998 Tobacco Master Settlement Agreement between the four largest tobacco companies and 52 state and territory attorneys involved an agreement to stop running cigarette ads in publications that had very large youth-readership shares. Still, when I was an intern at Rolling Stone in 2000, the magazine’s business model relied in large part on the fact that its readership demographics were young-skewing without tripping the T.M.S.A. threshold. [Matt Yglesias] 
• When you observe an extreme outlier, you should usually vastly reduce your credence in the model with respect to which the event counts as an outlier. In crude terms: suppose that you're 95% sure that some model of the situation is right. If you observe an event that is a four-sigma outlier according to the model but much less likely if the model is wrong, then (by Bayes' theorem) your model is almost certainly wrong. So if you were making commitments on the basis of that model, you should stop doing so. [Nate Meyvis]
• Whenever possible, ask: "should I do this same thing again and again?" Getting compound returns is great. Getting diminishing returns is bad. Doing more and more of the same thing tends to get you one or the other, and figuring out which situation you’re in is often very tricky. Work at it. [Nate Meyvis]