Followup to:  Surprised by Brains

Five sources of discontinuity:  1, 2, and 3...

Cascades are when one thing leads to another.  Human brains are effectively discontinuous with chimpanzee brains due to a whole bag of design improvements, even though they and we share 95% genetic material and only a few million years have elapsed since the branch.  Why this whole series of improvements in us, relative to chimpanzees?  Why haven't some of the same improvements occurred in other primates?

Well, this is not a question on which one may speak with authority (so far as I know).  But I would venture an unoriginal guess that, in the hominid line, one thing led to another.

The chimp-level task of modeling others, in the hominid line, led to improved self-modeling which supported recursion which enabled language which birthed politics that increased the selection pressure for outwitting which led to sexual selection on wittiness...

...or something.  It's hard to tell by looking at the fossil record what happened in what order and why.  The point being that it wasn't one optimization that pushed humans ahead of chimps, but rather a cascade of optimizations that, in Pan, never got started.

We fell up the stairs, you might say.  It's not that the first stair ends the world, but if you fall up one stair, you're more likely to fall up the second, the third, the fourth...

Followup to:  The First World Takeover

As last we looked at the planet, Life's long search in organism-space had only just gotten started.

When I try to structure my understanding of the unfolding process of Life, it seems to me that, to understand the optimization velocity at any given point, I want to break down that velocity using the following abstractions:

• The searchability of the neighborhood of the current location, and the availability of good/better alternatives in that rough region. Maybe call this the optimization slope.  Are the fruit low-hanging or high-hanging, and how large are the fruit?
• The optimization resources, like the amount of computing power available to a fixed program, or the number of individuals in a population pool.
• The optimization efficiency, a curve that gives the amount of searchpower generated by a given investiture of resources, which is presumably a function of the optimizer's structure at that point in time.

Followup to: Excluding the Supernatural

Yesterday, I wrote:

If the "boring view" of reality is correct, then you can never predict anything irreducible because you are reducible.  You can never get Bayesian confirmation for a hypothesis of irreducibility, because any prediction you can make is, therefore, something that could also be predicted by a reducible thing, namely your brain.

Benja Fallenstein commented:

I think that while you can in this case never devise an empirical test whose outcome could logically prove irreducibility, there is no clear reason to believe that you cannot devise a test whose counterfactual outcome in an irreducible world would make irreducibility subjectively much more probable (given an Occamian prior).

Without getting into reducibility/irreducibility, consider the scenario that the physical universe makes it possible to build a hypercomputer —that performs operations on arbitrary real numbers, for example —but that our brains do not actually make use of this: they can be simulated perfectly well by an ordinary Turing machine, thank you very much...

Well, that's a very intelligent argument, Benja Fallenstein.  But I have a crushing reply to your argument, such that, once I deliver it, you will at once give up further debate with me on this particular point:

Followup to:  Is Morality Given?, Is Morality Preference?, Moral Complexities, Could Anything Be Right?, The Bedrock of Fairness, ...

Intuitions about morality seem to split up into two broad camps: morality-as-given and morality-as-preference.

Some perceive morality as a fixed given, independent of our whims, about which we form changeable beliefs.  This view's great advantage is that it seems more normal up at the level of everyday moral conversations: it is the intuition underlying our everyday notions of "moral error", "moral progress", "moral argument", or "just because you want to murder someone doesn't make it right".

Others choose to describe morality as a preference—as a desire in some particular person; nowhere else is it written.  This view's great advantage is that it has an easier time living with reductionism—fitting the notion of "morality" into a universe of mere physics.  It has an easier time at the meta level, answering questions like "What is morality?" and "Where does morality come from?"

Both intuitions must contend with seemingly impossible questions.  For example, Moore's Open Question:  Even if you come up with some simple answer that fits on T-Shirt, like "Happiness is the sum total of goodness!", you would need to argue the identity.  It isn't instantly obvious to everyone that goodness is happiness, which seems to indicate that happiness and rightness were different concepts to start with.  What was that second concept, then, originally?

Or if "Morality is mere preference!" then why care about human preferences?  How is it possible to establish any "ought" at all, in a universe seemingly of mere "is"?

So what we should want, ideally, is a metaethic that:

1. Adds up to moral normality, including moral errors, moral progress, and things you should do whether you want to or not;
2. Fits naturally into a non-mysterious universe, postulating no exception to reductionism;
3. Does not oversimplify humanity's complicated moral arguments and many terminal values;
4. Answers all the impossible questions.

Followup to:  Probability is Subjectively Objective

The classic explanation of counterfactuals begins with this distinction:

1. If Lee Harvey Oswald didn't shoot John F. Kennedy, then someone else did.
2. If Lee Harvey Oswald hadn't shot John F. Kennedy, someone else would have.

In ordinary usage we would agree with the first statement, but not the second (I hope).

If, somehow, we learn the definite fact that Oswald did not shoot Kennedy, then someone else must have done so, since Kennedy was in fact shot.

But if we went back in time and removed Oswald, while leaving everything else the same, then—unless you believe there was a conspiracy—there's no particular reason to believe Kennedy would be shot:

We start by imagining the same historical situation that existed in 1963—by a further act of imagination, we remove Oswald from our vision—we run forward the laws that we think govern the world—visualize Kennedy parading through in his limousine—and find that, in our imagination, no one shoots Kennedy.

It's an interesting question whether counterfactuals can be true or false.  We never get to experience them directly.

Followup to:  When Science Can't Help

Once upon a time, a younger Eliezer had a stupid theory.  Eliezer₁₈ was careful to follow the precepts of Traditional Rationality that he had been taught; he made sure his stupid theory had experimental consequences.  Eliezer₁₈ professed, in accordance with the virtues of a scientist he had been taught, that he wished to test his stupid theory.

This was all that was required to be virtuous, according to what Eliezer₁₈  had been taught was virtue in the way of science.

It was not even remotely the order of effort that would have been required to get it right.

The traditional ideals of Science too readily give out gold stars. Negative experimental results are also knowledge, so everyone who plays gets an award.  So long as you can think of some kind of experiment that tests your theory, and you do the experiment, and you accept the results, you've played by the rules; you're a good scientist.

You didn't necessarily get it right, but you're a nice science-abiding citizen.

Previously in series:  Decoherence

As touched upon earlier, Heisenberg's "Uncertainty Principle" is horribly misnamed.

Amplitude distributions in configuration space evolve over time.  When you specify an amplitude distribution over joint positions, you are also necessarily specifying how the distribution will evolve.  If there are blobs of position, you know where the blobs are going.

In classical physics, where a particle is, is a separate fact from how fast it is going.  In quantum physics this is not true.  If you perfectly know the amplitude distribution on position, you necessarily know the evolution of any blobs of position over time.

So there is a theorem which should have been called the Heisenberg Certainty Principle, or the Heisenberg Necessary Determination Principle; but what does this theorem actually say?

My intended next OB post will, in passing, distinguish between moral judgments and factual beliefs.  Several times before, this has sparked a debate about the nature of morality.  (E.g., Believing in Todd.) Such debates often repeat themselves, reinvent the wheel each time, start all over from previous arguments.  To avoid this, I suggest consolidating the debate.  Whenever someone feels tempted to start a debate about the nature of morality in the comments thread of another post, the comment should be made to this post, instead, with an appropriate link to the article commented upon.  Otherwise it does tend to take over discussions like kudzu.  (This isn't the first blog/list where I've seen it happen.)

I'll start the ball rolling with ten points to ponder about the nature of morality...

I've always been annoyed at the notion that the bias-variance decomposition tells us something about modesty or Philosophical Majoritarianism.  For example, Scott Page rearranges the equation to get what he calls the Diversity Prediction Theorem:

Collective Error = Average Individual Error - Prediction Diversity

I think I've finally come up with a nice, mathematical way to drive a stake through the heart of that concept and bury it beneath a crossroads at midnight, though I fully expect that it shall someday rise again and shamble forth to eat the brains of the living.

(Part one in a series on "statistical bias", "inductive bias", and "cognitive bias".)

"Bias" as used in the field of statistics refers to directional error in an estimator.  Statistical bias is error you cannot correct by repeating the experiment many times and averaging together the results.

The famous bias-variance decomposition states that the expected squared error is equal to the squared directional error, or bias, plus the squared random error, or variance.  The law of large numbers says that you can reduce variance, not bias, by repeating the experiment many times and averaging the results.