aaronde

Karma: 309

aaronde 13 May 2015 18:24 UTC
2 points
on: Looking to restart Madison LW meetups, in need of regulars
I would definitely make it every other week, if it’s weekly.

aaronde 10 Feb 2013 21:49 UTC
7 points
in reply to: Athrelon’s comment on: Politics Discussion Thread February 2013
I agree that something unusual is going on. Humans, unlike any other species I’m aware of, are voluntarily restricting our own population growth. But I don’t know why you say that there’s “no reason” to believe that this strange behavior might benefit us. Surely you can think of at least one reason? After all, all those other species that don’t voluntarily limit their own reproduction eventually see their populations crash, or level off in the face of fierce competition over resources, when they meet or exceed their environment’s carrying capacity. The laws of physics as we currently understand them dictate that exponential growth cannot continue forever.

I’m not saying that there are no foreseeable downsides to population leveling off. And I’m not saying that there’s no risk of unforeseeable consequences of the social changes underlying this demographic shift. But I am saying that (amid all the pros and cons) there is one obvious, important reason why human population leveling off might be a good thing. The downsides are neither so obvious nor so potentially dramatic. To illustrate this, lets look at Last’s (awful) WSJ article quoted in the Marginal Revolutions post.

Last does his best to paint declining fertility as a nightmare scenario. But the data he presents simply don’t support his tone. For example:

Low-fertility societies don’t innovate because their incentives for consumption tilt overwhelmingly toward health care. They don’t invest aggressively because, with the average age skewing higher, capital shifts to preserving and extending life and then begins drawing down.

In other words, low-fertility societies do have an incentive to innovate—in medicine and life extension. And not just for the benefit of the old—they also have an incentive to keep the young healthy and productive as long as possible, to maintain their shrinking workforce (which may go some ways toward explaining Japan’s excellent school nutrition program, and low, declining childhood obesity rates). They also have an incentive to develop automation to replace aging workers, which I know is a major reason that Japan is a leader in robotics. Let’s take a closer look at Japan:

From 1950 to 1973, Japan’s total-factor productivity—a good measure of economic dynamism—increased by an average of 5.4% per year. From 1990 to 2006, it increased by just 0.63% per year. Since 1991, Japan’s rate of GDP growth has exceeded 2.5% in only four years; its annual rate of growth has averaged 1.03%.

Wait, did he just admit that Japan’s economy is still growing? Yep, both GDP and GDP/capita have continued to grow, albeit more slowly, since the 1990s. Let that sink in a moment. The Japanese are, on average, working less than they used to. They’re older and more likely to be retired. And yet they still get to enjoy having more stuff. (Largely thanks to innovations in automation driven, in part, by aging demographics.) And thanks to medical innovations, driven in part by aging demographics, they will continue enjoying that stuff longer than any generation before. So where’s the grim cautionary tale? Last has none, just this:

At the current fertility rate, by 2100 Japan’s population will be less than half what it is now.

Which would still be more than it was in 1900. So, where’s the problem? Why is it preferable to keep taxing the earth’s resources with more and more people with no foreseeable prospects at space colonization? On overpopulation, Last says,

First, global population growth is slowing to a halt and will begin to shrink within 60 years.

This is just unforgivably bad logic: ‘Overpopulation isn’t a problem, because population is leveling off, because fertility is declining. Therefore we must act immediately to put a stop to declining fertility!’ If we ever do face a shrinking population, I’d rather deal with it by increasing healthy lifespans than by increasing birthrates.

aaronde 26 Jan 2013 8:53 UTC
5 points
in reply to: iDante’s comment on: Infinitesimals: Another argument against actual infinite sets
I downvoted common_law’s post, because of some clear-cut math errors, which I pointed out. I’m downvoting your comment because it’s not saying anything constructive.

There’s nothing wrong with what common_law was trying to do here, which is to show that infinite sets shouldn’t be part of our ontology. Experience can’t be the sole arbiter of which model of reality is best; there is also parsimony. Whether infinite quantities are actually real, is no less worthy of discussion than whether MWI is actually real, or merely a computational convenience. I only agree with you that the math lacked rigor. This is discussion, so I don’t see a problem with posting things that need to be corrected, but I had to downvote the post because it might have confused someone who didn’t notice the errors.

aaronde 26 Jan 2013 6:06 UTC
6 points
on: Infinitesimals: Another argument against actual infinite sets
I agree with premise (1), that there is no reason to think of infinitesimal quantities as actually part of the universe. I don’t agree with premise (2), that actual infinities imply actual infinitesimals. If you could convince me of (2), I would probably reject (1) rather than accept (3). Since an argument for (2) would be a good argument against (1), given that our universe does seem to have actual infinities.

the points on a line are of infinitesimal dimension … yet compose lines finite in extent.

No. Points have zero dimension. “Infinitesimal” is something else. There are no infinitesimal numbers on the real line (or in the complex plane, for that matter), and no subinterval of the real line has infinitesimal length, so we would have to extend the number system if we wanted to think of infinitesimals as numbers.

When I raise the same argument about an infinite set, you can’t reply that you can always make the set bigger; if I say add an element, you reply that the sets are still the same size (cardinality).

But there is a way to use an infinite set to construct a larger infinite set: - the power set. I don’t understand the rest of this paragraph.

Consider again the points on a 3-inch line segement. If there are infinitely many, then each must be infinitesimal.

Again, single points have zero length, not infinitesimal length. Note, though, that there are ways to partition a finite line segment into infinitely many finite line segmets, including the partition that Zeno proposed: ¹⁄₂ + ¹⁄₄ + ¹⁄₈ + … In an integration, we (conceptually) break up the domain into infinitely many infinitesimally wide intervals, but this is just an intuition. None of the formal definitions of integrals I’ve seen actually say anything about an infinitesimally wide interval.

The series comes infinitesimally close to the limit, and in this context, we treat the infinitesimal as if it were zero.

Actually, we don’t have to treat an infinitesimal as zero, we just have to treat zero as zero. If I move along a meter stick at one meter per second, then according to Zeno’s construction, I traverse half the distance in ¹⁄₂ second, ³⁄₄ of the distance in ³⁄₄ of a second, and so on. As you say, after one second, I have traversed every point on the meter stick except the very last point, because the union of the closed intervals [0,1/2], [1/2,3/4], [3/4,7/8], … is the half-open interval [0,1). So how much longer does it take me to traverse that last point? Zero seconds, because a single point has zero length. There is no contradiction, and no need to use infinitesimals.

aaronde 15 Jan 2013 23:39 UTC
4 points
in reply to: Scott Alexander’s comment on: [LINK] Cholesterol and mortality
Fascinating. But note that these are still very old people with declining cholesterol as they age. The study is more relevant to physicians deciding whether to prescribe statins to their elderly patients, and less relevant to young people deciding whether to keep cholesterol low throughout life with diet.

I’d need to read the whole study, but what I see so far doesn’t even contradict the hypothesis I outlined. The abstract says that people who had low cholesterol at the last two examinations did worse than people who had low cholesterol at only the last examination. But most of these old people had declining cholesterol. So maybe this just means that the earlier your cholesterol starts to decline from aging, the sooner you die.

Anyway, I put more stock in the cross-cultural epidemiology and intervention trials, than in these observational studies trying to parse small differences within relatively homogeneous, free-living populations. We know that the longest-lived, healthiest populations in the world ate a low saturated-fat diet that induces low cholesterol. And we know that Dean Ornish was able to reverse heart disease with a lifestyle intervention including a cholesterol-lowering diet. Show me a population as healthy as the Okinawans with high cholesterol, or an intervention as effective as Ornish’s without lowering cholesterol, and I’ll reconsider. Otherwise, I do consider the issue settled from a pragmatic perspective, even if some of the academic questions remain to be answered. That is, it may be possible to have a healthy lifestyle that raises cholesterol, but we don’t have any proven examples of such a lifestyle to emulate, do we? Mike Darwin gave a good explanation of this idea in “Interventive Gerontology”.

aaronde 15 Jan 2013 21:56 UTC
8 points
on: [LINK] Cholesterol and mortality
http://www.youtube.com/watch?v=xiNvQ-g1XGs&list=PLDBBB98ACA18EF67C&index=19

This (admittedly biased) youtuber has a pretty thorough criticism of the study. The bottom line is that cholesterol tends to drop off before death (6:26 in the video), not just because cholesterol-lowering medications are administered to those at highest risk of heart attack (as Kawoomba points out), but also because of other diseases. When you correct for this, or follow people throughout their lives, this reverse causation effect disappears, and you find exactly the association you would expect: higher cholesterol associates with higher cardiovascular and total mortality (10:21).

I think that studies like this one are like studies showing that overweight is “protective” against mortality—when the obvious alternative explanation is that smoking, cancer, and other diseases can prevent weight gain, or cause weight loss, just before they kill you! Obviously, this would mask or even reverse the association between overweight (high cholesterol) and death, even if overweight (high cholesterol) causes death.

aaronde 23 Dec 2012 16:43 UTC
0 points
in reply to: ShardPhoenix’s comment on: So you think you understand Quantum Mechanics
Agreed. The multiverse idea is older than, and independent of quantum theory. Actually, a single infinitely large classical universe will do, since statistically, every possibility should play out. Nietzsche even had a version of immortality based on an infinitely old universe. Though it’s not clear whether he ever meant it literally, he very well could have, because it was consistent with the scientific understanding of the time.

That said, I like the idea of sminux’s post. I try to steer clear of quantum language myself, and think others should too, if all they mean by “quantum” is “random”.

aaronde 5 Dec 2012 7:50 UTC
5 points
on: How to Avoid the Conflict Between Feminism and Evolutionary Psychology?
All the possible reasons for the conflict you listed suggest that the solution is to help feminists understand evolutionary psychology better, so they won’t have a knee-jerk defensive reaction against it. This could come off as a little condescending, but more importantly, it misses the other side of the issue. In order to leave itself less open to criticism, evolutionary psychology could be more rigorous, just as other “soft” sciences like medicine and nutrition could be more rigorous. This would make it harder for critics to find things to object to, increasing trust in the field over time, and would probably be a good thing in itself anyway.

So I would add to your list: 8) Concerns about lack of rigor in the field of evolutionary psychology.

aaronde 4 Dec 2012 0:13 UTC
4 points
in reply to: [deleted]’s comment on: [LINK] AmA by computational neuroscientists behind ‘the world’s largest functional brain model’
Actually I’m not sure if any of that is a problem. Spaun is quite literally “anthropomorphic”—modeled after a human brain. So it’s not much of a stretch to say that it learns and understands the way a human does. I was just pointing out that the more progress we make on human-like AIs, without progress on brain scanning, the less likely a Hansonian singularity (dominated by ems of former humans) becomes. If Spaun as it is now really does work “just like a human”, then building a human-level AI is just a matter of speeding it up. So by the time we have computers capable of supporting a human mind upload, we’ll already have computer programs at least as smart as humans, which learn their knowledge on their own, with no need for a knowledge transplant from a human.

aaronde 3 Dec 2012 21:29 UTC
6 points
on: [LINK] AmA by computational neuroscientists behind ‘the world’s largest functional brain model’
I think we need to separate the concept of whole brain emulation, from that of biology-inspired human-like AI. This actually looks pretty bad for Robin Hanson’s singularity hypothesis, where the first emulations to perfectly emulate existing humans suddenly make the cost of labor drop dramatically. If this research pans out, then we could have a “soft takeoff”, where AI slowly catches up to us, and slowly overtakes us.

CNRG_UWaterloo, regarding mind uploads:

Being able to simulate a particular person’s brain is incredibly far away. There aren’t any particularly good ideas as to how we might be able to reasonably read out that sort of information from a person’s brain. That said, there are also lots of uses that a repressive state would have for any intelligent system (think of automatically scanning all surveillence camera footage). But, you don’t want a realistic model of the brain to do that—it’s get bored exactly as fast as people do.

So we should expect machine labor to gradually replace human labor, exactly as it has since the beginning of the industrial revolution, as more and more capabilities are added, with “whole brain emulation” being one of the last features needed to make machines with all the capabilities of humans (if this step is even necessary). It’s possible, of course, that we could wind up in a situation where the “last piece of the puzzle” turns out to be hugely important, but I don’t see any particular reason to think that will happen.

aaronde 3 Dec 2012 19:38 UTC
0 points
on: [SEQ RERUN] I Heart CYC
From the article:

AIs learn slowly now mainly because they know so little.

This seems implausible, because humans learn almost everything we know over our lifetimes, from a starting instruction set smaller than existing pieces of software. If architecture really is overrated (meaning that existing architectures are already good enough) isn’t it more likely that AIs learn so slowly now, simply because the computers aren’t powerful enough yet?

aaronde 3 Dec 2012 19:11 UTC
0 points
on: A solvable Newcomb-like problem—part 1 of 3
If Omega gets it right more than 99% of the time, then why would Alpha take 10-to-1 odds against Omega messing up?

aaronde 2 Dec 2012 5:51 UTC
8 points
in reply to: JoshuaZ’s comment on: Open Thread, December 1-15, 2012
Yeah, that was my impression. One of the things that’s interesting about the article is that many of the technologies Taleb disparages already exist. He lists space colonies and flying motorcycles right along side mundane tennis shoes and video chat. So it’s hard to tell when he’s criticizing futurists for expecting certain new technologies, and when he’s criticizing them for wanting those new technologies. When he says that he’s going to take a cab driven by an immigrant, is he saying that robot cars won’t arrive any time soon? Or that it wouldn’t make a difference if they did? Or that it would be bad if they did? I think his point is a bit muddled.

One thing he gets right is that cool new technologies need not be revolutionary. Don’t get me wrong; I take the possibility of truly transformative tech seriously, but futurists do overestimate technology for a simple reason. When imagining what life will be like with a given gadget, you focus on those parts of your life when you could use the gadget, and thus overestimate the positive effect of the gadget (This is also why people’s kitchens get cluttered over time). For myself, I think that robot cars will be commonplace in ten years, and that will be friggin’ awesome. But it won’t transform our lives—it will be an incremental change. The flip side is that Taleb may underestimate the cumulative effect of many incremental changes.

aaronde 2 Dec 2012 2:56 UTC
19 points
in reply to: advancedatheist’s comment on: Open Thread, December 1-15, 2012
I think the real difference between people like Taleb and the techno-optimists is that we think the present is cool. He brags about going to dinner in minimalist shoes, and eating food cooked over a fire, whereas I think it’s awesome that I can heat things up instantly in a microwave oven, and do just about anything in meticulously engineered and perfectly fitted, yet cheaply mass-produced, running shoes without worrying about damaging my feet. I also like keyboards, and access to the accumulated knowledge of humanity from anywhere, and contact lenses. And I thought it was funny when he said that condoms were one of the most important new technologies, but aren’t talked about much, as if to imply that condoms aren’t cool. I think that condoms are cool! I remember when I first got condoms, and took one out to play with. After testing it a couple different ways, I thought: *how does anyone manage to break one of these!?” It’s easy to extrapolate that no “cool” technology will exist in the future, if you don’t acknowledge that any cool technology currently exists.

But I think Taleb’s piece is valuable, because it illustrates what we are up against, as people trying to get others to take seriously the risks, and opportunities, presented by future technologies. Taleb seems very serious and respectable, precisely because he is so curmudgeonly and conservative, whereas we seem excitable and silly. And he’s right that singularitarian types tend to overemphasize changes relative to everything that remains the same, and often conflate their predictions of the future with their desires for the future. I think that lesswrong is better than most in this regard, with spokespeople for SI taking care to point out that their singularity hypothesis does not predict accelerating change, and that the consequences of disruptive technology need not be good. Still, I wonder if there’s any way to present a more respectable face to the public, without pretending that we don’t believe what we do.

aaronde 27 Nov 2012 7:16 UTC
2 points
in reply to: Alex_Altair’s comment on: Mathematical Measures of Optimization Power
Oops, looks like I was wrong about what you meant (ignore the edit). But yes, if you give a stupid thing lots of power you should expect bad outcomes. A car directed with zero intelligence is not a car sitting still, but precisely what you said was dangerous: a car having its controls blindly fiddled with. But if you just run a stupid program on a computer, it will never acquire power in the first place. Most decisions are neutral, unless they just happen to be plugged into something that has already been optimized to have large physical effects (like a bulldozer). Of those decisions that do have large effects, most will be destructive, but that’s exactly what we should expect from a stupid optimization process acting on something that has already been finely honed by a smart optimization process.

what does “could” mean?

Good question. I think it has something to do with simply defining some set of actions to be your “options”, and temporarily putting all your options on an equal footing, so that you end up with the one with the best consequences, rather than the one that seemed like the one you’d be most likely to choose. I don’t think it even has much to do with probabilities, because then you run into self-fulfilling prophesies—doing what you predicted you’d do, thereby justifying the prediction.

In this case, we want to measure how good an agent did, relative to how it could have done. That is, how good were the consequences of the option it chose, relative to its other options. I don’t see any reason to weight those options according to a probability distribution, unless you know what “half an option” means. And choosing a distribution poses huge problems. After all, we know the agent chose one of the options with probability 1.0, and all the others with probability 0.0.

Forest fires are definitely OPs under my intuitive concept. They consistently select a subset of possible future (burnt forests).

Well, you could just compare the rate of oxidation under a flame, to the average rate of oxidation of all surfaces (including those that happen to be on fire) within whichever reference class you prefer. (I think choosing a reference class (set of options) is just part of how you define the OP. And you just define the OP whichever way helps you understand the world best.)

Thanks for all your comments!

Is this actually helpful? I try to read up on the background for this stuff, but I never know if I’m just rehashing what’s already been discussed, and if so, whether reviewing that here would be useful to anyone.

aaronde 27 Nov 2012 4:15 UTC
1 point
in reply to: Alex_Altair’s comment on: Mathematical Measures of Optimization Power

1) You’d need a way to even specify the set of “output” of any possible OP. This seems hard to me because many OPs do not have clear boundaries or enumerable output channels, like forest fires or natural selection or car factories.

How do you define an optimization process without defining its output? If you want to think of natural selection as a force that organizes matter into self-replicators, then compare the reproductive ability of an organism to the reproductive ability of a random clump of matter, to find out how much natural selection has acted on it. If you want to think of it as a force that produces genomes, then compare an evolved genome to a random strand of DNA (up to some maximum length).

I can’t think of a way of fitting a forest fire into this model either, which suggests it isn’t useful to think of forest fires under this paradigm. But isn’t that a good sign? If anything could be usefully modeled as an optimizer, wouldn’t that hint that the concept is overly broad?

2) This is equal to a flat prior over your OPs outputs. You need some kind of specification for what possibilities are equally likely, and a justification thereof.

Why? Isn’t the crux of the decision-making process pretending that you could choose any of your options, even though, as a matter of fact, you will choose one? I can see how you would run into some fuzziness if you tried to apply it to natural selection or even brains. But for the mathematical model, where the process selects from some abstract set of options, equal weighting seems appropriate. And this maps fairly straightforwardly onto an AI acting over a physical wire.

3) Even if we consider an AGI with well-defined output channels, it seems to me that random outputs are potentially very very very destructive, and therefore not the “default” or “status quo” against which we should measure.

(EDIT: D’oh! I just realized what you meant by random outputs being “destructive”. You mean that if an AGI were to take its options to be “configurations of matter in the universe”, then its baseline would be a randomly shuffled universe that was almost completely destroyed from our perspective. But I don’t think this makes sense. Just because an AGI is smart enough to reorganize all matter in the universe doesn’t mean that it makes sense for it to output decisions in that form. That would basically be a type error, just like if I were to decide “be in New York” instead of “drive to New York”. The options the AGI has to choose from are outputs of a subroutine running inside of itself. So if it has a robot body, then the “default” or unoptimized output is random flailing about, or if it interacts through a text terminal, it would be printing random gibberish, most of which does nothing and leaves the configuration of the universe largely unchanged (and a few of which convince the programmer to give it access to the internet so it can take over the world.)).

Are you saying that an “AI” outputting random noise could do worse than an “AI” with optimization power measured at zero (i.e. zero intelligence)? Seems to me that, to reliably do worse than random, you would have to be trying to do badly. And you would have to be doing so with a strictly positive level of skill.

(Note: for a model of natural selection that might actually be usable in practice, suppose that we know a set X of mutations have occurred in a population over a given time, and that a subset of these X have become fixed in the population (the rest have been weeded out). To calculate how “optimized” X is, compare the reproductive fitness of the actual population to the average fitness of hypothetical populations which, instead of X*, had retained some random subset of the mutations from X (that is, selected with uniform probability from the power set of X). The measure of “reproductive fitness” could be as simple as population size.)

aaronde 25 Nov 2012 23:44 UTC
0 points
in reply to: Vaniver’s comment on: Open Thread, November 16–30, 2012
Good questions. I don’t know the answers. But like you say, UDT especially is basically defined circularly—where the agent’s decision is a function of itself. Making this coherent is still an unsolved problem. So I was wondering if we could get around some of the paradoxes by giving up on certainty.

aaronde 25 Nov 2012 6:05 UTC
1 point
in reply to: AlexMennen’s comment on: Mathematical Measures of Optimization Power

Caveat: if someone is paralyzed because of damage to their brain, rather than to their peripheral nerves or muscles, then this is not true,

That’s why I specified that the you don’t get penalized for disabilities that have nothing to do with the signals leaving your brain.

which creates and undesirable dependency of the measured optimization power on the location of the cause of the disability.

I disagree. I think that’s kind of the point of defining “optimization power” as distinct from “power”. A man in a prison cell isn’t less intelligent just because he has less freedom.

No, that clearly makes no sense if EU[av] ⇐ 0. If you want to divide by something to normalize the measured optimization power (so that multiplying the utility function by a constant doesn’t change the optimization power), the standard deviation of the expected utilities of the counterfactual probability distributions over world states associated with each of the agent’s options would be a better choice.

Great idea! I was really sloppy about that, realized at the last minute that taking a ratio was clearly wrong, and just wanted to make sure that you couldn’t get different answers by scaling the utility function. I guess |EU[av]| does that, but now we can get different answers by shifting the utility function, which shouldn’t matter either. Standard deviation is infinitely better.

aaronde 25 Nov 2012 5:12 UTC
0 points
in reply to: Vaniver’s comment on: Open Thread, November 16–30, 2012
What I am saying is that I don’t assume that I maximize expected utility. I take the five-and-ten problem as a proof that an agent cannot be certain that it will make the optimal choice, while it is choosing, because this leads to a contradiction. But this doesn’t mean that I can’t use the evidence that a choice would represent, while choosing. In this case, I can tell that U($10) > U($5) directly, so conditioning on A=$10 or A=$5 is redundant. The point is that it doesn’t cause the algorithm to blow up, as long I don’t think my probability of maximizing utility is 0 or 1.

It’s true that A=$5 could be stronger evidence for U($5)>U($10) than A=$10 is for U($10)>U($5). But there’s no particular reason to think it would be. And as long as P(U($10)>U($5)) is large enough a priori, it will swamp out the difference. As long as making a choice is evidence for that being the optimal choice, only insofar as I am confident that I make the optimal choice in general, it will provide equally strong evidence for every choice, and cancel itself out. But in cases where a particular choice is evidence of good things for other reasons (like Newcomb’s problem), taking this evidence into consideration can affect my decision.

So why can’t I just use the knowledge that I’ll go through this line of reasoning to prove that I will choose $10 and yield a contradiction? Because I can’t prove that I’ll go through this line of reasoning. Simulating my decision process as part of my decision would result in infinite recursion. Now, there may be a shortcut I could use to prove what my choice will be, but the very fact that this would yield a contradiction means that no such proof exists in a consistent formal system.

(BTW, I agree that CDT is the only decision theory that works in practice, as is. I’m only addressing one issue with the various timeless decision theories)

aaronde 25 Nov 2012 1:48 UTC
10 points
on: Mathematical Measures of Optimization Power

What I can’t figure out is how to specify possible worldstates “in the absence of an OP”.

Can we just replace the optimizer’s output with random noise? For example, if we have an AI running in a black box, that only acts on the rest of the universe through a 1-gigabit network connection, then we can assign a uniform probability distribution over every signal that could be transmitted over the connection over a given time (all 2^(10^9) possibilities per second), and the probability distribution of futures that yields is our distribution over worlds that “could have been”. We could do the same thing with a human brain and, say, all combinations of action potentials that could be sent down the spinal cord over a given time. This is desirable, because it separates optimization power from physical power. So paralyzed people aren’t less intelligent just because “raise arm” isn’t an option for them (That is, no combination of action potentials in their head will cause their arm to move).

More formally, an agent is a function or program that has a range or datatype. The range/datatype is the set of what we would call the agent’s options. So assume we can generate counterfactual outcomes for each option in the range, the same way your favorite decision theory does. Then we can take optimization power to be the difference between EU given what the agent actually does, and the average EU over all the counterfactuals.*

If the OP is some kind of black-box AI agent, it’s easier to imagine this. But if the OP is evolution, or a forest fire, it’s harder to imagine.

I’m not so sure. Choosing to talk about natural selection as an agent means defining an agent which values self-replication and outputs a replicator. So if you have a way of measuring how good a genome is at replicating, you could just subtract from that how good a random sequence of base-pairs is, on average, at replicating, to get a measure of how much natural selection has optimized that genome. Of course, you could do the same thing with an entire animal versus a random clump of matter, because the range of the agent is just part of the definition.

EDIT: * AlexMennen had a much better idea for normalizing this than I did ;)