Oh, you’re right. The net incentive to catch cheaters is actually… 1/(k(k-1)^2), then? The relative incentive story is worse, though still better in total than the positive version, and better still if you assume a constant-size disincentive to be caught cheating.
rossry
Karma: 169
Another (related?) advantage is that the incentives to manipulate and catch manipulation are much better balanced with the negative (“you’re out”) version. Consider:
Perfectly cheating in the positive version improves your chances of winning by (n-1)/n, and stopping you from doing so improves each other person’s chances by 1/n.
Perfectly cheating in a round of the negative version improves your chances of winning by 1/(k(k-1)), where k is the number of people in to start the round. Stopping you from doing so improves each other person’s chances by the same amount.
The total (summed) incentive to manipulate in the negative version is (n-1)/n, the same as in the positive case.
(Disclaimer: I’m a financial professional, but I’m not anyone’s investment advisor, much less yours.)
You mention diversity as an advantage because it reduces your risk, but this framing is missing the crucial point that you can transmute a portfolio that’s 0.5x as risky as your baseline to one that returns twice as much as your baseline. (Wei_Dai mentions this, but obliquely.) The trick is to use leverage, which is not as hard to get (or as expensive, or as complicated) as you think.
To be explicit about it: if you increase the per-dollar riskiness of your portfolio without increasing the per-dollar expected value, then after you leverage it down until it’s at the optimal level of risk for your utility function (which you were going to do, right?), you will have lower expected returns.
The relevant question is “how much lower?” (which is precisely to say “how much does it increase your per-dollar risk?”), which I answer in my response to Wei_Dai (nephew to this). The answer turns out to be “very little”, but in order to get there, you have to be asking the right question first.
I don’t have a good intuition about how costly this actually is in practice, if you only play with 10% of your portfolio.
tl;dr extremely little.
Here’s some numbers I made up:
Let the market’s single common factor explain 90% of the variance of each stock.
Let the remaining 10%s be idiosyncratic and independent.
Let stocks have equal volatility (and let all risk be described by volatility).
Now compare a portfolio that’s $100 of each of a hundred stocks with one that’s $90 of each of a hundred plus $1k of another stock. (I’ll model each stock as 0.75 times the market factor plus 0.25 a same-variance idiosyncratic factor.) Compared to a $10k single-stock portfolio...
the equal-weighted portfolio has like
the shot-caller’s portfolio has like
...for an increase in of 4.5 basis points. So, pretty negligible.
Even if the market’s single factor explains only half of the variance of each stock, the increased risk of the shot-caller’s portfolio is just 40 basis points ( vs ). In the extreme case where stocks are uncorrelated, the increased risk is +34.5%, though I think that that’s unrealistically generous to the diversification strategy.
Since an increase in volatility-per-dollar of basis points means that you give up basis points of your expected returns, I’m going to say that this effect is negligible in the “10% of portfolio” setting.
Can you define “rational/consistent”? The terms are a bit overloaded, especially in this community, and making your definition precise is itself most of the answer to your question.
For example, you give some good examples of nontransitive decider-actors, and if some of them are “rational”, then nontransitive preferences can be rational, as you point out. Alternatively, one definition of “consistent” is that a decider-actor will always reach the same decision when it has the same information, regardless of what other options it has previously rejected, which requires transitive preferences.
Is the idea something like: the actor themself is uncertain about the value of the project, and the kickstarter also helps them find out whether it’s worth doing
Nope! The paper’s model for this result assumes that the value conditioned on success is known to the proposer, so that the proposer’s only incentive is to maximize their own profits by setting the payout odds and threshold. The (non-obvious to me) result that the paper proves is that this coincidentally minimizes the probability of up-cascades:
A higher decision threshold excludes more DOWN cascades while it is less likely to be reached. We show that the concern about potential DOWN cascades dominates the concern about likelihood to reach the target. To maximize the proceeds, the proponent endogenously sets the target to the smallest number that in equilibrium completely excludes DOWN cascades in the same spirit as Welch (1992), with the caveat that the proponent utilizes both price and target to achieve this. Consequently, with endogenous issuance pricing, there is no DOWN cascade which stops private information aggregation, and good projects are always financed while bad projects are never financed, when the crowd base N becomes very large. In other words, financing efficiency and information aggregation efficiency approaches the first best as N grows bigger, despite the presence of information cascades.
My (uninformed) guess is “humor, of the slightly mind-sharpening sort”.
Hypothesis: (formed before running the test below) “clearly too high” will be more of an overestimate than “clearly too low” is an underestimate; most midpoints will be high; this will bias estimates high (especially through repeated self-anchoring to high midpoints).
Anecdata: I tried using this method to estimate the number of gates in Hong Kong International Airport. (I’m currently standing in a line in HKIA, and have flown through it at least a dozen times in my life.)
40 ~ 10,000
40 ~ 5,020
40 ~ 2,530
40 ~ 1285
40 ~ 662 (maybe 351 is neither obviously too high or too low, so I should take it? or maybe it’s slightly high and so...)
40 ~ 351 (195 I don’t have a strong opinion about, so I’ll take it)
So, depending on how I decide to stop, I get estimates around 195 or 351. Apparently the true answer is 90, supporting my hypothesis.
Proposal: Maybe use geometric mean instead of arithmetic mean?
I tried to clear my head and try again with geomean:
40 ~ 10,000
40 ~ 632 (ehhh, 159 is more likely to high than too low)
40 ~ 159 (80 is too low)
80 ~ 159 (113)
There’s one suspect step around 159, but my guess here is that being anchored by one higher number (632) rather than five (5020, 2530, 1285, 662, 351) is enough to actually make me think “probably less than 159” rather than “no strong opinion about 195″. Terminating at 159 also is an outperformance of arithmetic mean, but feels a bit more like luck around where the midpoints fell.
What’s up with “FDIC insurance up to $1,000,000”? Wikipedia claims that FDIC insurance only covers $0.25mln/bank unless I have a joint account, retirement account, or some kind of legal entity which I’m not.
I don’t think that there is off-the-shelf insurance for “I lose my job (prospects) or otherwise choose to have lower lifetime earnings, in some way I did not foresee.”
It would be confusing to me if most EAs had equity investments they could not afford to bear a crash loss in, especially those involved in an emergency fund scheme. Why add market exposure to the portfolio of donations + scheme membership + liquid cash? (Especially, why as market exposure you expect other scheme participants to share?)
That said, it is possible to buy insurance against a market crash. Probably not as a centralized service.
So shouldn’t that inequality apply to 0.AAA… (base eleven) and 0.999… (base ten) as well? (A debatable point maybe).
Not debatable, just false. Formally, the fact that for all does not imply that .
If I were to poke a hole in the (proposed) argument that 0.[k 9s]{base 10} < 0.[k As]{base 11} (0.9<0.A; 0.99<0.AA;...), I’d point out that 0.[2*k 9s]{base 10} > 0.[k As]{base 11} (0.99>0.A; 0.9999>0.AA;...), and that this gives the opposite result when you take (in the standard sense of those terms). I won’t demonstrate it rigorously here, but the faulty link here (under the standard meanings of real numbers and infinities) is that carrying the inequality through the limit just doesn’t create a necessarily-true statement.
0.111...{binary} is 1, basically for the Dedekind cut reason in the OP, which is not base-dependent (or representation-dependent at all) -- you can define and identify real numbers without using Arabic numerals or place value at all, and if you do that, then 0.999...=1 is as clear as not(not(true))=true.
I think your comment is unnecessarily hedged—do you think that you’d find much disagreement among LWers who interact with FHI/GMU-Econ over whether people there sometimes (vs never) fail to do level-one things?
I think I understand the connotation of your statement, but it’d be easier to understand if you strengthened “sometimes” to a stronger statement about academia’s inadequacy. Certainly the rationality community also sometimes fails to do even the obvious, level-one intelligent character things to enable them to achieve their goals—what is the actual claim that distinguishes the communities?
I’m confused; it seems like evidence against the claim that you can get arbitrary amounts of value out of learning generic rationality skills, but I don’t see it as “devastating” to the claim you can get significant value, unless you’re claiming that “spent years learning all that stuff, and now do it as a day job; some of them 16 hours a day” should imply only a less-than-significant improvement. Or am I missing something here?
cf. my comment cousin to this one; I misunderstood what the term was pointing at at first, though I stand by my complaint that that’s a problem with the term.
Thanks; I legitimately misunderstood at first read whether “counterspell” was intended to apply to the invocations thrown out by bad arguers or the concise and specific distillations the OP is presenting for use. On re-read, I agree that it’s supposed to be a set of useful tools.
I remain convinced of the specific claim that “counterspell” is bad jargon (though I don’t think it’s good practice to cite my own confusion too strongly; the incentives there aren’t great). I agree that MtG″s paradigm where more general counterspells are more expensive seems like a good fit for thinking about rhetorical (and perhaps epistemic) tactics, though I reiterate that that’s not how they work in many other settings, and that ambiguous baggage is worse than no baggage for this sort of thing. The question of whether identifying counterspells with magic is supposed to be a positive or negative association is additional gratuitous confusion—I think your claim that the magic metaphor implies they don’t work is wrong, but I’m not 85% sure.
Strong approval of the overall goal of the post, but here’s a semantic criticism:
In accordance with the Rationalist tradition that requires everything to have a nerdy sci-fi or fantasy name
I parse this as an in-joke (and appreciate it as such), but I do think that regularly minting new jargon that’s likely to carry substantial, conflicting(!) contextual baggage (not all of it appropriate[1]) is...a bad norm for an epistemic community to have.
I also think that the deeper tradition of jargon-forging (as Eliezer practiced it) involved names that sounded nerdy, but _not_ sci-fi or fantasy -- _cf._ Knowing About Biases Can Hurt People, which uses “Fully General Counterargument” in much the same way(?) as you’re using “Counterspell”. “Fully General Counterargument” is slightly more unwieldy, but apart from that is a better piece of jargon—it’s less loaded and by leaning even harder into the implicit snark that winks at the fact that the purported counter- isn’t working at all, makes it even more clear that no, this is not a useful thing.
[1] To belabor this point, MtG counterspells are fully general (edit: okay, not **fully** general, and see Slider below), and a reasonable fit for the term as you’re using it, but D&D (3.5e, at least) counterspells are based on negating a spell by casting a copy of it, which is not what you mean at all. I don’t actually know what “counterspells” are in WoD/Mage, but the risk that they’re even further afield from your intention should be another strike against using the already-loaded handle.
preference “my decisions should be mine”—and many people seems to have it
Fair. I’m not sure how to formalize this, though—to my intuition it seems confused in roughly the same way that the concept of free will is confused. Do you have a way to formalize what this means?
(In the absence of a compelling deconfusion of what this goal means, I’d be wary of hacking epistemics in defense of it.)
There are “friends” who claim to have the same goals as me, but later turns out that they have hidden motives.
Agreed and agreed that there’s a benefit to removing their affordance to exploit you. That said, why does this deserve more attention than the inverse case (there are parties you do not trust who later turn out to have benign motives)?
“give the power over my final decisions to small random events around me” seems like a slightly confused concept if your preferences are truly indifferent. Can you say more about why you see that as a problem?
The potential adversary seems like a more straightforward problem, though one exciting possibility is that lightness of decisions lets a potential cooperator manipulate your decisions on favor of your common interests. And presumably you already have some system for filtering acquaintances into adversaries and cooperators. Is the concern that your filtering is faulty, or something else?
[Commitment] eventually often turn to be winning strategy, compared to the flexible strategy of constant updating expected utility.
Some real-world games are reducible to the game of Chicken. Commitment is often a winning strategy in them. Though I’m not certain that it’s a commitment to a particular set of beliefs about utility so much as a more-complex decision theory which sits between utility beliefs and actions.
In summary, if the acquaintances whose info you update on are sufficiently unaligned with you and your decision theory always selects the addition that your posterior assigns the highest utility, then your actions will be “over-updating on the evidence” if your beliefs are properly Bayesian. But I don’t think the best response is to bias yourself towards under-updating.
Do you bite the bullet that this means the set of things you morally value changes discontinuously and predictably as things move out of your light cone? (Or is there some way you value things less as they are less “in” your light cone, in some nonbinary way?)
My guess is that one gets a reasonable start by framing more tasks as self-delegation (adding the middle step of “decide to do” / “describe task on to-do list” / “do task”), then periodically reviewing the tasks-competed list and pondering the benefits and feasibility of outsourcing a chunk of the “do task”s.
Creating a record of task-intentions has a few benefits in making self-reflection possible; reflecting on delegation opportunities is a special case.