When Eliezer Yudkowsky divides by zero, he gets a singularity.
Meni_Rosenfeld
Karma: 219
Mixed strategy Nash equilibrium
It means that the model used per item doesn’t have enough parameters to encode what we know about the specific domain (where domain is “Reddit comments”, “Urban dictionary definitions”, etc.)
The formulas discussed define a certain mapping between pairs (positive votes, negative votes) to a quality score. In Miller’s model, the same mapping is used everywhere without consideration of the characteristics of the specific domain. In my model, there are parameters a and b (or alternatively, a/(a+b) and a+b) that we first train per-domain, and then apply per item.
For example, let’s say you want to decide the order of a (5, 0) item and a (40, 10) item. Miller’s model just gives one answer. My model gives different answers depending on:
The average quality—if the overall item quality is high (say, most items have 100% positive votes), the (5,0) item should be higher because it’s likely one of those 100% items, while (40,10) has proven itself to be of lower quality. If, however, most items have low quality, (40,10) will be higher because it has proven itself to be one of the rare high-quality items, while (5,0) is more likely to be a low-quality item which lucked out.
The variance in quality—say the average quality is 50%. If the variance in quality is low, (5,0) will be lower because it is likely to be an average item which lucked out, while (40, 10) has proven to be of high quality. If the variance is high (with most items being either 100% or 0%), (5,0) will be higher because in all likelihood it is one of the 100% items, while (40, 10) has proven to be only 80%.
In short, using a cookie-cutter model without any domain-specific parameters doesn’t make the most efficient use of the data possible.
Eliezer Yudkowski can solve EXPTIME-complete problems in polynomial time.
I don’t suppose it’s possible to view the version history of the post, so can you state for posterity what “DOCI” used to stand for?
In the notation of that post, I’d say I am interested mostly in the argument over “Whether a Bayesian or frequentist algorithm is better suited to solving a particular problem”, generalized over a wide range of problems. And the sort of frequentism I have in mind seems to be “frequentist guarantee”—the process of taking data and making inferences from it on some quantity of interest, and the importance to be given to guarantees on the process.
Hi.
I intend to become more active in the future, at which point I will introduce myself.
The web game greatly suffers from the network effect. There’s just very little chance you’ll get >=3 people to log on simultaneously, and of course, because of this people will give up on trying, worsening the effect.
Maybe we can designate, say, 12:00 AM and PM, UTC, as hours at which people should log on? This will make it easier to reach critical mass.
(Link to How Not To Sort By Average Rating.)
I forgot to link in the OP. Then remembered, and forgot again.
Something of interest: Jeffery’s interval. Using the lower bound of a credible interval based on that distribution (which is the same as yours) will probably give better results than just using the mean: it handles small sample sizes more gracefully. (I think, but I’m certainly willing to be corrected.)
This seems to use specific parameters for the beta distribution. In the model I describe, the parameters are tailored per domain. This is actually an important distinction.
I think using the lower bound of an interval makes every item “guilty until proven innocent”—with no data we assume the item is of low quality. In my method we give the mean quality of all items (and it is important we calibrate the parameters for the domain). Which is better is debatable.
At the risk of stating the obvious: The information content of a datum is its surprisal, the logarithm of the prior probability that it is true. If I currently give 1% chance that the cat in the box is dead, discovering that it is dead gives me 6.64 bits of information.
It’s worth mentioning that EFF has resumed accepting Bitcoin donations a while ago.
Would it? Maybe the question (in its current form) isn’t good, but I think there are good answers for it. Those answers should be prominently searchable.
Thanks. I’ll move it as soon as I have the required karma.
Tel Aviv Self-Improvement Meetup Group
How not to sort by a complicated frequentist formula
I think it’s reasonable to model this as a Poisson process. There are many people who could in theory vote, only few of them do, at random times.
This is interesting, especially considering that it favors low-data items, as opposed to both the confidence-interval-lower-bound and the notability adjustment factor, which penalize low-data items.
You can try to optimize it in an explore-vs-exploit framework, but there would be a lot of modeling parameters, and additional kinds of data will need to be considered. Specifically, a measure of how many of those who viewed the item bothered to vote at all. Some comments will not get any votes simply because they are not that interesting; so if you keep placing them on top hoping to learn more about them, you’ll end up with very few total votes because you show people things they don’t care about.
Proving a program to be secure down from applying Schrödinger’s equation on the quarks and electrons the computer is made of is way beyond our current abilities, and will remain so for a very long time.
Challenge accepted! We can do this, we just need some help from a provably friendly artificial superintelligence! Oh wait...
I’ve written a post on my blog covering some aspects of AGI and FAI.
It probably has nothing new for most people here, but could still be interesting.
I’ll be happy for feedback—in particular, I can’t remember if my analogy with flight is something I came up with or heard here long ago. Will be happy to hear if it’s novel, and if it’s any good.
How many hardware engineers does it take to develop an artificial general intelligence?
Short answer: I already addressed this. Is your point that I didn’t emphasize it enough?
Long answer:
Correct. Do you know what the distribution is? Can you gather statistics? Do you understand the mentality of your opponents so well that you can predict their actions? Can you put the game in some reference class and generalize from that? If any of the above, knock yourself out. Otherwise there’s no justification to use anything but the equilibrium.
If you assume I will continue to use the NE, even after collecting sufficient statistics to show that the players follow some specific distribution (as opposed to “not Nash equilibrium”), then this is a strawman. If you’re just saying you’re very good at understanding the mind of the MMOer then that’s likely, but doesn’t have much to do with the post.
I did not criticize the rare swords&armor posts that actually tried to profile their opponents and predict their actions. I criticized the posts that tried to do some fancy math to arrive at the “optimal” solution inherent to the game, and then failed to either acknowledge or reconcile the fact that their solution is unstable.
One should first learn how to do Nash equilibrium, then learn how to not do Nash equilibrium. NE is the baseline default choice. If someone doesn’t understand the problem well enough to realize that any suggested deterministic strategy will be unstable and that the NE is the answer to that, what hope does he have with the harder problem of reasoning about the distribution of opponents?
Even under the strong assumption that there is some clear reference class about which you can collect these statistics, this is true only if one of the following conditions holds:
The attacker hasn’t heard about those statistics.
The attacker is stupid.
The attacker is plagued with the same inside-view biases that made all his predecessors attack west.
The plausibility of each depends on the exact setting. If one holds, and we know it, and we really do expect to be attacked west with 90% probability, then this is no longer a two-player game. It’s a one-player decision theory problem where we should take the action that maximizes our expected utility, based on the probability of each contingency.
But if all fail, we have the same problem all over again. The attacker will think, “paulfchristiano will look at the statistics and defend west! I’ll go east”. How many levels of recursion are “correct”?