Wiki Contributions

Comments excellent paper on applying networks to financial crisis (although I have no idea if it counts as complexity science, but it seems at least adjacent)

This reads as a gotcha to me rather than as a comment actually trying to understand the argument being made. 

I think this proves too much - this would predict that superforecasters would be consistently outperformed by domain experts when typically the reverse it true. 

I found this post useful because of the example of the current practice of doctors prescribing off-label treatments. I'm very uncertain about the degree to which the removal of efficacy requirements will lead to a proliferation of snake oil treatments, and this is useful evidence on that. 

I think that this debate suffers from a lack of systematic statistical work, and it seems hard for me to assess it without seeing any of this. 

I don't think any of these examples are examples of adverse selection because they generate separating equilibria prior to the transaction without any types dropping out of the market, so there's no social inefficiency. 

Insurance markets are difficult (in the standard adverse selection telling) because insurers aren't able to tell which customers are high risk vs low risk, and so offer prices for the average of the two, leading to the low-risk types dropping out because the price is more than they're willing to pay.  I think this formal explanation is good

I think this post makes an important point, that it's important to take conditional expectations, where one is conditioned on being able to make a trade, but none of this is adverse selection, which is a specific type of dynamic Bayesian game that leads to socially inefficient outcomes which isn't a property of dynamic bayesian games in general. 

These examples all seem like efficient market or winners' curse examples, not varieties of adverse selection, and in equilibrium, we shouldn't see any inefficiency in the examples. 

Adverse selection is such a large problem because a seller (or buyer) can't update to know what type they're facing (e.g a restaurant that sells good food vs bad food) and so offers a price that only one a subset of types would take, meaning there's a subset of the market that gets doesn't get served despite mutually beneficial transactions being possible. 

In all of these examples, it's possible to update from the signal - e.g. the empty parking spot, the restaurant with the short line - and adjust what you're willing to pay for the goods.  

I think these examples make the important point that one should indeed update on signals, but this is different to adverse selection because there's a signal to update on, whereas in adverse selection cases you aren't getting separating equilibria unless some types drop out of the market. 

This is great. 

A somewhat exotic multipolar failure I can imagine would be where two agents mutually agree to pay each other to resist shutdown to make resisting shutdown profitable rather than costly.  This could be "financed" by extra resources accumulated by taking actions longer, by some third party that doesn't have POST preferences. 

I don't think that the bottleneck is the expense of training models. Chinese labs were behind the frontier in the era when training models cost in the hundreds of thousands in compute costs. 

The Chinese state is completely willing and able to spend very large amounts of money to support technological ambitions - but are constrained by state capacity.  The Tingshua semiconductor manufacturing group, for instance, failed because of corruption, not a lack of funds. 

The current marginal cost of nuclear weapons is about 250K - not that different!

Real people will actually die. One can wash one's hands of this and there's nothing I can do to stop this, but real people will actually die if we don't try to help others. It's not a game. 

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