A new proof by SFI Professor David Wolpert sends a humbling message to would-be super intelligences: you can't know everything all the time.

The proof starts by mathematically formalizing the way an "inference device," say, a scientist armed with a supercomputer, fabulous experimental equipment, etc., can have knowledge about the state of the universe around them. Whether that scientist's knowledge is acquired by observing their universe, controlling it, predicting what will happen next, or inferring what happened in the past, there's a mathematical structure that restricts that knowledge. The key is that the inference device, their knowledge, and the physical variable that they (may) know something about, are all subsystems of the same universe. That coupling restricts what the device can know. In particular, Wolpert proves that there is always something that the inference device cannot predict, and something that they cannot remember, and something that they cannot observe.

"In some ways this formalism can be viewed as many different extensions of [Donald MacKay's] statement that 'a prediction concerning the narrator's future cannot account for the effect of the narrator's learning that prediction,'" Wolpert explains. "Perhaps the simplest extension is that, when we formalize [inference devices] mathematically, we notice that the same impossibility results that hold for predictions of the future—MacKay's concern—also hold for memories of the past. Time is an arbitrary variable—it plays no role in terms of differing states of the universe."

**Not everyone can be right**

What happens if we don't require that an inference device know everything about their universe, but only that it knows the most that could be known? Wolpert's mathematical framework shows that no two inference devices who both have free will (appropriately defined) and have maximal knowledge about the universe can co-exist in that universe. There may (or not) be one such "super inference device" in some given universe—but no more than one. Wolpert jokingly refers to this result as "the monotheism theorem," since while it does not forbid there being a deity in our universe, it forbids there being more than one.

As an example, suppose that Bob and Alice are both scientists with unlimited computational abilities. Moreover, suppose that they both have "free will," in that the question Bob asks himself does not restrict the possible questions Alice could ask herself, and vice-versa. (This turns out to be crucial.) Then it is impossible for Bob to predict (or retrodict) what Alice thinks at another time if Alice is also asked to predict what Bob is not thinking at that time.

Wolpert compares this proposition to the Cretan liar's paradox, in which Epimenides of Knossos, a Cretan, famously stated "all Cretans are liars." Unlike Epimenides' statement though, which exposes the problem of systems that have the capability of self-reference, Wolpert's reasoning also applies to inference devices without that capability.

In addition, in Wolpert's formalism, the same scientist, considered at two different moments in time, is two different inference devices. So while it could be that some inference device is a "super inference device" at one moment, they could not be so more than once. Again tongue in cheek, he refers to this as the "deism" theorem, since it allows there to be a deity that knows the most that could be known at the beginning of the universe—but forbids their ever being so knowledgeable again.

Because it does not rely on specific theories of physical reality like quantum mechanics or relativity, the new proof presents a broad set of limits for exploring the nature of scientific knowledge.

"None of these results limiting knowledge acquired by prediction relies on there being chaotic processes in the universe… it doesn't matter what the laws of physics are or if Alice is more computationally powerful than a Turing machine," says Wolpert. "All of this is independent of that and it's much broader ranging."

This research is progressing in many different directions, ranging from epistemic logic to a theory of Turing machines. In particular, Wolpert and his colleagues are creating a more nuanced, probabilistic framework that will allow them to explore not only the limits of absolutely correct knowledge, but also what happens when the inference devices are not required to know with 100% accuracy.

"What if Epimenides had said 'the probability that a Cretan is a liar is greater than x percent?'" Moving from impossibility to probability could tell us whether knowing one thing with greater certainty inherently limits the ability to know another thing. According to Wolpert, "we are getting some very intriguing results."

**Explore further:**
Can scientists know that they do not know?

**More information:**
Read David Wolpert's "Theories of Knowledge and Theories of Everything" chapter in the *The Map and the Territory*: www.springer.com/us/book/9783319724775

Constraints on physical reality arising from a formalization of knowledge: arXiv:1711.03499 [physics.hist-ph] arxiv.org/abs/1711.03499

## rgeContextomics

No subsystem can mirror all the incident info propagated by a larger system that it is a subsystem of.

Is there a proof proving that the obvious is obvious?

## mackita

## Spaced out Engineer

Monism has its problems too. Such a super inference device's field generation of knowledge, would be ambiguous.

An implicit hierarchy does not eliminate transfinite induction or pluralistic aggregation.

I still don't get how mutual information could not permit sharing the same space. What if all there was to know was the transaction protocol, or the noise from which is was derived?

Seems silly to say assume Laplace's demon because, we can't handle Cantor's diagonalization with the constraints of consistency.

Not being an expert however, I do agree that if you are going to have causal relations, and impose consistency, super determinism is the way to go.

It would be fascinating is logic alone said conditional probability was the wiser choice. Though unitarity and the Gaussian distribution form a seeming axiomatic basis for classical statistics.

## Whydening Gyre

SOE - I'll have some of whatever you're smokin'....

## Spaced out Engineer

Ain't been shit... At least for a while now.

Sorry for the typos though. What I said still stands.

## TheGhostofOtto1923

## mackita

Because the volume of information waiting for its reveal is infinite whereas the volume of money is always finite, we should think about ways, how to prioritize the research and to research primarily the stuff, which has immediate practical usage (like the overunity or cold fusion). This would give us enough of resources for research of this less usefull one.

Whereas the rules of contemporary science lead to quite opposite approach: the most abstract and least important things are researched first.

## antialias_physorg

seems to indicate otherwise.

Though the proof still seems to hinge on there being something like a 100% correct fact (or "capital-T Truth" if you so will) - which I'm not at all sure can be argued in a watertight way.

(Note also that the 'monotheism' statement doesn't mean that there can be only one such super inference device...just not two at the same time. There could be one that later gets replaced by another)

## knowphiself

## mackita

1) "inappropriate study given study data;"

2) "inappropriate extrapolations or recommendations for clinical practice;"

3) "selective reporting;"

4) "more favorable data presentation"

## knowphiself

May 2, 2018 by Tasnim Elmamoun, Public Library of Science

Read more at: https://phys.org/...html#jCp

## neiorah

## Da Schneib

I am unimpressed.

## TheGhostofOtto1923

It was meant to generate nonsense like this

"Philo of Alexandria in his first-century book "On the creation" mentions perfect numbers, claiming that the world was created in 6 days and the moon orbits in 28 days because 6 and 28 are perfect..."

-Philo - the first philo (probably not)

## torbjorn_b_g_larsson

That said, Wolpert's work was more interesting than I thought. It does not attempt to describe measurements or learning in general but assumes given knowledge. So primary finds is that "you cannot know what you cannot know" and that you can set up different instruments that conflicts in "what is to be known". When probability is introduced Wolpert's ideas becomes more realistic and the latter conflict disappears and consistent science knowledge is allowed. I do not understand Wolpert's claim that a finite universe cannot be fully known however, I think his proof then tries to infer knowledge that is not of the universe.

## torbjorn_b_g_larsson

Finally the model allows an analog to Hilbert's uncertainty principle (HIP) of statistical inferences; HIP is a deep result indeed! Though notably we now know that entanglement serves to undercut HIP due to the added system information so we can know more, Wolpert's model cannot describe that. Good luck with a formal axiomatic model describing science and its methods, re Gödel's results on what happens with sufficiently complex axiomatic models (inconsistencies)...

## InquiringMind

Human comprehension may be similarly limited, though Wolpert is also concerned with non-human comprehension.

From this synopsis it's hard to be sure if Wolpert's method really does not appeal to self-reference. If not that would be a big achievement. However at least the Alice/Bob argument sounds to me suspiciously like self-reference generated in a new way. I look forward to learning more about it.

## Da Schneib

## Whydening Gyre

I suppose it would depend on how much of a perfectionist you are....

## Whydening Gyre

Well, he DID get a whole sophy named after him...

## Whydening Gyre

'A new proof by SFI Professor David Wolpert sends a humbling message to would-be super intelligences: you can't know everything all the time."

By knowing this, you complete the circle..:-)

Shit changes too fast to be up on it all, all the time.

What's important is - you can learn and know about it if you have the time and inclination to keep up on - everything...

## mackita

In praxis the information follows the principle of least action and once the size of system across it is spreading grows too large, then it undergoes spontaneous symmetry breaking and it gets fragmented both is space but also both in time (it simply gets forgotten). The overall speed of progress will be thus limited by the speed in which individual members will be able to process new information - which will be the lower, the higher volume of info they already process.

## mackita

In this context the reading of articles The era of expert failure by Arnold Kling, Why experts are usually wrong by David H. Freeman and Why the experts missed the crash by Phill Tetlock may be useful.

## mackita

Another way, even more effective one is the prioritization of levels, at which information will be processed in a fractal way. It has again analogy in natural high dimensional systems, which evolved to handle energy very effectively.

It basically means, that some layer of experts will only deal with information at higher level (in form of reviews) and they will be authorized to say, what experts at lower levels will research and how. This approach is already anticipated in articles like this one.

## mackita

## mackita

Such an insight reflects common experience, that getting liberals to agree is like herding cats - they also like cats more for their independence than the conservatives. You cannot keep fully free will and to follow some consensus at the same moment - no matter how universally valid it actually is.

## InquiringMind

For the avoidance of doubt, in my comment above, "non-trivial theory." = any theory ìncluding basic arithmetic (Peano axioms or equivalent))