## Posts

## Comments

**Patrick Cruce (patrick-cruce)**on Exorcizing the Speed Prior? · 2018-07-24T15:20:15.603Z · LW · GW

Current AI does stochastic search, but it is still search. Essentially PP complexity class, instead of NP/P. (with a fair amount of domain specific heuristics)

**Patrick Cruce (patrick-cruce)**on Probabilistic decision-making as an anxiety-reduction technique · 2018-07-16T15:34:00.561Z · LW · GW

Never leave the house without your d20 :-P

But I agree with you. This seems a simple way to do something like satisficing. Avoiding the great computational cost of an optimal decision.

In terms of prior art that is probably the field you want to explore: https://en.m.wikipedia.org/wiki/Satisficing

**Patrick Cruce (patrick-cruce)**on Compact vs. Wide Models · 2018-07-16T14:34:55.114Z · LW · GW

Not sure if this is helpful, but since you analogized to chip design. In chip design, you typically verify using a constrained random method when the state space grows too large to verify every input exhaustively. That is, you construct a distribution over the set of plausible strings and then sample it and feed it to your design. Then you compare the result to a model in a higher level language.

Of course, standard techniques like designing for modularity can make the state space more manageable too.

**Patrick Cruce (patrick-cruce)**on The Craft And The Codex · 2018-07-09T14:36:51.101Z · LW · GW

First off, Scott’s blog is awesome.

Second, the example of dieting comes to mind when I think of training rationality. While they’re not much connected to the rationality community, they are a large group of people focused on overcoming one particular aspect of our irrationallity. (but without much success)

**Patrick Cruce (patrick-cruce)**on The Fermi Paradox: What did Sandberg, Drexler and Ord Really Dissolve? · 2018-07-09T14:00:24.518Z · LW · GW

What basis is there to assume that the distribution of these variables is log uniform? Why, in the toy example, limit the variables to the interval [0,0.2]? Why not [0,1]?

These choices drive the result.

The problem is, for many of the probabilities, we don’t even know enough about them to say what distribution they might take. You can’t infer a meaningful distribution over variables where your sample size is 1 or 0

**Patrick Cruce (patrick-cruce)**on Why it took so long to do the Fermi calculation right? · 2018-07-05T14:43:06.467Z · LW · GW

I’m still not seeing a big innovation here. I’m pretty sure most researchers who look at the Drake equation think “huge sensitivity to parameterization.”

If we have a 5 parameter drake equation then number of civilizations scales with X^5, so if X comes in at 0.01, we’ve got a 1e-10 probability of detectable civilization formation. But if we’ve got a 10 parameter Drake equation and X comes in at 0.01 then it implies a 1e-20 probability. (extraordinary smaller)

So yes, it has a a huge sensitivity, but it is primarily a *constructed sensitivity*. All the Drake equation really tells us is that we don’t know very much and it probably won’t be useful until we can get N above one for more of the parameters.

**Patrick Cruce (patrick-cruce)**on Dissolving the Fermi Paradox, and what reflection it provides · 2018-06-30T19:36:23.841Z · LW · GW

I’m not sure I understand why they’re against point estimates. As long as the points match the mean of our estimates for the variables, then the points multiplied should match the expected value of the distribution.

**Patrick Cruce (patrick-cruce)**on The Power of Letting Go Part I: Examples · 2018-06-29T13:40:55.204Z · LW · GW

I think this is an interesting concept and want to see where you go with it. But just devil’s advocating, there are some pretty strong counterexamples for micromanagement. For example, many imperative languages can be ridiculously inefficient. And try solving an NP complete problem with a genetic algorithm and you’ll just get stuck in a local minimum.

Simplicity and emergence are often surprisingly effective but they’re just tools in a large toolbox.

**patrick-cruce**on [deleted post] 2018-06-28T15:00:52.350Z

Somewhat ironic that LW is badly in need of better captcha.

**Patrick Cruce (patrick-cruce)**on Loss aversion is not what you think it is · 2018-06-24T05:30:56.438Z · LW · GW

I read him, he is just incorrect. “People hate losses more than they hate gains” is not explained by DMU. They dislike losses to an extent far greater than predicted by DMU, and more importantly, this dislike is largely scale invariant.

If you go read papers like the original K&T, you’ll see that their data set is just a bunch of statements that are predicted to be equally preferrable under DMU (because marginal utility doesn’t change much for small changes in wealth). What changes the preference is simply whether K&T phrase the question in terms of a loss or a gain.

So...unsurprisingly, Kahneman is accurately describing the theory that won him the Nobel prize.

**Patrick Cruce (patrick-cruce)**on Order from Randomness: Ordering the Universe of Random Numbers · 2018-06-22T05:23:18.582Z · LW · GW

The result you got is pretty close to the fft of f(t) = t

Which is roughly what you got from sorting noise.

**Patrick Cruce (patrick-cruce)**on Physics has laws, the Universe might not · 2018-06-21T16:28:54.325Z · LW · GW

All finite length sequences exist in any infinite random sequence. So, in the same way that all the works of shakespeare exist inside an infinite random sequence, so too does a complete representation of any finite universe.

I suppose one could argue by the anthropic principle that we happen to exist in a well ordered finite subsequence of an infinite random sequence. But it is sort of like multiverse theories where it lacks the explanatory power or verifiability of simpler theories.

**Patrick Cruce (patrick-cruce)**on Order from Randomness: Ordering the Universe of Random Numbers · 2018-06-21T14:32:47.585Z · LW · GW

Maybe I’m being dense, and missing the mystery, but I think this reference might be helpful.

**Patrick Cruce (patrick-cruce)**on Loss aversion is not what you think it is · 2018-06-21T13:46:44.995Z · LW · GW

I mean...he quotes Kahneman; claiming the guy doesn’t know the implications of his own theory.

Losses hurt more than gains even at scales where DMU predicts that they should not. (because your DMU curve is approximately flat for small losses and gains) Loss aversion is the psychological result which explains this effect.

This is the author’s conclusion: “So, please, don’t go around claiming that behavioral economists are incorporating some brilliant newfound insight that people hate losses more than they like gains. We’ve known about this in price theory since Alfred Marshall’s 1890 Principles of Economics.”

Sorry nope. Alfred Marhall’s Principles would have made the wrong prediction.

**Patrick Cruce (patrick-cruce)**on Loss aversion is not what you think it is · 2018-06-21T02:21:21.586Z · LW · GW

That makes a lot of sense to me. Aversion to small losses makes a ton of sense as a blanket rule, when the gamble is: lose: don’t eat today win: eat double today don’t play: eat today

Our ancestors probably faced this gamble since long before humans were even humans. Under those stable conditions, a heuristic accounting for scale would have been needlessly expensive.

**Patrick Cruce (patrick-cruce)**on Loss aversion is not what you think it is · 2018-06-20T21:08:00.901Z · LW · GW

In short, the author is wrong. Diminishing marginal utility only really applies when the stakes are on the order of the agent’s total wealth, whereas the loss aversion asymmetry holds true for relatively small sums.