“Nullius in Verba,” which means, “Take no one’s word for it.”

TRANSCRIPT

Last weeks episode, #3, titled Drift was quite short because much of what we continue to discuss and learn about here on A New Order of Things are about dealing with drift.

The Right Questions is an oversimplified title for this episode. But to find drift, figure out how it occurs in your organization and why. Or understand and preempt your competitions actions. Or understanding why an employee made a mistake, we must be identifying and asking the right questions. I bet you have heard that time and again. There is a BIG BUT to that idea though. To best serve your organization, its people, and its customers, you have to find the correct answers to the questions.

I believe we answer questions often quite easily and quickly, it is human nature to take the easy road. Daniel Kahneman solidified that in his book, Thinking Fast and Slow (Kahneman, 2013) or we can work a little harder and find explanations to the answers.

Stick with me here…

Here is an example. You manage a branch of the Fidelity Fiduciary Bank. A teller, Mrs. Dawes gives a customer more money than they had in their account. Mrs. Dawes has been with the bank for a very long time and is fully trusted. We will, for the sake of this conversation, accept that fragile Mrs. Dawes was not acting maliciously.

When questioned about why the excessive mistake occurred the teller says something like, “I don’t know why I did that. I know the process, I’ve done it thousands of times; I just made a mistake.”

The general answer found in this circumstance is “worker error”. Right?

Thousands of folks have been fired, dismissed, injured, or killed. Airplanes have crashed, and ships have collided. All with the answer of: “Worker error” as causation.

Tell me, is “worker error” a deeply researched answer? Does it explain why the incident occurred? Does it help us mitigate the problem in the future? This is a BAD EXPLANATION.

No matter the industry you are in science has a hold:

Psychology, the way people’s brains work and why they make decisions they make is a science.

Physiology, the capabilities of the human bodies make-up, functionality, and mobility is a science.

Physics, science.

Chemistry, also science.

Learning method, ooh, a science.

Mathematics, science.

If we are going to find the best explanations to questions, I say we should take some hints from science as we look for them.

David Deutsch, scientist, physicist, and philosopher says in his 2011 book The Beginning of Infinity; Explanations that transform the world, that we should be looking for explanations to our questions, knowing that we can only find the best answer for the moment (Deutsch, The Beginning of Infinity, 2011). Because, at some point humans will gain some new piece of knowledge that falsifies the current explanation and replaces it with the next eventually falsifiable explanation. Hence the title of the book.

So, What is a good explanation?

In a recently released episode of the Naval Ravikant’s podcast, David Deutsch: Knowledge Creation and the Human Race, Naval interviews David Deutsch (Deutsch, Davd Deutsch: Knowledge Creation and The Human Race, 2023). I find David’s answers to his ideas about explanations to be the best, easiest to understand so I will read them now.

Naval: Let’s talk about what is a good explanation. I literally want to bullet point this for the masses. I know it’s a difficult thing to pin down because it’s highly contextual. But knowing that we are always fallible and always subject to improvement, what is your current thinking of a good explanation?

David: In The Fabric of Reality, I completely avoided saying what an explanation is. I just said it’s hard to define and it keeps changing and we can keep improving our conception of what it is.

But what makes an explanation good is that it meets all the criticisms that we have at the moment. If you have that, then you’ve got the best explanation. That automatically implies that it already doesn’t have any rivals by then—because if it has any rivals that have anything going for them, then the existence of two different explanations for the same thing means that neither of them is the best explanation.

You only have the best explanation when you’ve found reasons to reject the rivals. Of course, not all possible rivals, because all possible rivals include the one that’s going to supersede the current best explanation.

If I want to explain something like, “How come the stars don’t fall down?” I can easily generate 60 explanations an hour and not stop, and say that the angels are holding them up, or they are really just holes in the firmament. Or I can say, “They are falling down and we better take cover soon.” Whereas, coming up with an explanation that contains knowledge—an explanation that’s better than just making stuff up—requires creativity and experimentation and interpretation, and so on. As (Karl) Popper says, knowledge is hard to come by (Popper, 1983). Because it’s hard to come by, it’s also hard to change once we’ve got it.

Once we have an explanation, it’s going to explain several different things. After we’ve done that for a while and been successful in this hard thing, it’s going to be difficult to switch to one of those easy explanations. The angel thing is no longer going to be any good for explaining why some of those stars don’t move in the same way. They used to call planet stars because they didn’t know the drastic difference between them. The overwhelming majority of them move from day to day and from year to year in a rigid way, but the planets don’t.

Once you have a good explanation that tells you about the planets as well, it’s no good going back to the angels or any of those easy-to-come-by explanations. Not only do you not have a viable rival, but you can’t make one either. You can’t say, “Ah, OK, so we got a good explanation there, but it would work just as well if we replace this by this, or if we try to extend its range to cover this other thing as well.”

Therefore, the good explanation is hard to vary. It’s hard to vary because it was hard to come by. It is hard to come by because the easy ones don’t explain much. (WORKER ERROR!!)

Good explanations are hard to find, hard to vary, and falsifiable

Naval: Let me throw out a list of things that might be part of a good explanation. You tell me where I’m wrong. It’s better than all the explanations that came before. It’s hard-fought knowledge and it’s hard to vary. So, we’ve got those pieces. Falsifiability—I know that sounds like a very basic criterion. If it’s not falsifiable, then it’s not an explanation worth taking seriously. 

David: So, falsifiability is very much part of what makes a good explanation in science. I’m trying to find my way into constructor theory at the moment. Chiara and I and some other people are trying to build the theory. It’s very hard to come by. The parts of it that we’ve got are very hard to change. That’s alright. But we are still far away from having any experimental tests of it. That’s what we are working towards. We want a theory that is experimentally testable.

The things that will be testable are the things that we haven’t yet discovered about it. And we can’t fix that deficiency just by adding a testable thing to it. We can’t say, “We take constructor theory as it is now and add the prediction that the stock market is going to go wildly up next year.” That’s a testable prediction, but the whole thing doesn’t make an explanation at all, let alone a good one.

Naval: So testability can’t be arbitrary testability. It has to be within the context of the explanation and has to arise from the explanation. And while you’re in the process of coming up with the explanation, you don’t know if testability is necessarily going to be available in any reasonable timeframe. You hope eventually that will happen, and we can use this amazing oracle that we call reality to help test the outcome. But it’s not a given at the beginning and it’s highly contextual.

David: And all that is within science. As soon as you get outside science, for example, in mathematics or in philosophy, then testability is not really available, not in the same sense that testing is used in science.

So there are many other methods of criticism and criticize-ability. You could say, “If a theory, even the philosophical theory, immunizes itself against criticism—like the theory that anyone who would contradict me isn’t worth listening to—that’s a theory that tries to immunize itself from criticism and can therefore be rejected.”

Naval: For example, saying that an all-knowing but mysterious god did it and, “God works in mysterious ways” is immunizing from criticism. Or “the great programmer created the simulation, and it’s incomprehensible to us because the laws of physics used to generate it are outside of our simulation.” That’s also immunizing itself to criticism.

We have narrowed down on a new point here that has not been explicitly made before, which is that it’s the criticize-ability that is important, not necessarily the testability—although the closer you get to classic science, the more you look for experiments that can test it.

A hallmark of a good explanation is narrow and risky predictions

Let me move on to the next one. I was reading one of your books, scribbling notes to myself. I don’t think you used this phrase but I summarize it as, “One of the hallmarks of a good explanation is that it often makes narrow and risky predictions.” Of course, the classic example is relativity bending light around the star and the Eddington experiment. Is that a piece of it, making narrow and risky predictions?

David: It is. But that kind of formulation is Popper’s, not mine. I’m a little bit uncomfortable expressing it like that because I can just hear the opponent saying, “Narrow by what criterion? Risky by what criterion? Hard to vary by what criterion?”

Naval: Wouldn’t risky be unexpected and narrow be within the range of possibilities? The more precise and unexpected that prediction was, the more testable I’m making it, the better adapted my explanation is.

David: Those are criteria that come up when trying to think more precisely what testable means. I think the important thing is that you’re testing an explanation, not just a prediction. It’s also true that hard to vary means you are sticking your neck out when you try to vary it, and the few variants that survive were hard to come by.

So it’s perfectly true that narrowness and sticking your neck out are indeed components of a good explanation—and not just within science. If you say, like Popper did, that scientific knowledge is not derived from observations, he’s really sticking his neck out. He’s really got to make a good case for that for it to be taken seriously by any thinker about knowledge. And he does that. It can’t be denied that he was sticking his neck out.

The more reach something has, the better an explanation it is, as long as it does account for what it’s trying to account for. But the converse is not true. Most good explanations don’t have much reach or don’t have any. We’re trying to solve the problem of how to get the delivery person to deliver it to the right door. You might have a great solution that’s totally hard to vary, but it may not have any reach at all. It may not even reach your neighbor. The neighbor might have a different problem with delivery. Often we succeed in making good explanations, but rarely do they have much reach. When they do, that’s great because that makes them of a different order of goodness.

I think we have beat what a good explanation is into submission, but the part about them having reach needs a little more.

Remember our little end-of-day shortage of Mrs. Dawes’ at the Fidelity Fiduciary Bank? I think we can all agree, that “worker error” is not a good explanation.

If we take the time to look for the good explanation that has reach we will, for the sake of this discussion find that Mrs. Dawes was the only teller on duty at the drive thru during the lunch rush. Her till was running low and she needed to leave her window to get more cash.

In the rush, confusion, frustrated customers in a hurry, Mrs. Dawes lost sight of the details. She miss counted the cash from the drawer and the refill from the safe.

Not a bad good explanation, right? Still no reach…

It is hard to beat Mr. Deutsch’s own words, so I will reference his anecdote describing reach:

In The Beginning of Infinity is the explanation for why we have seasons. David gives the old Greek explanation that it’s driven by Persephone, the goddess of spring, and when she can leave Hades. Hades had kidnapped and raped Persephone. Demeter, her mother, goddess of the earth and agriculture negotiated with Hades for her Persephone’s release. They agreed that Persephone marry Hades and eat a magic seed. This would driver her to visit him once a year from that time forward. While she was away on her conjugal visit, Demeter would become sad and command the world to become cold and stark and nothing would grow.

The sure Greeks gave a lot of credit and theories involving gods and goddesses.

Not only was that idea not easily testable, but it is also easy to vary. Persephone could have been Nike, and Hades could have been Jupiter or Zeus. There is even a like Norse explanation for seasons. It’s easy to vary that explanation without the predictions changing.

But a good explanation for seasons that is seen when we look at the axis tilt theory—which says Earth is angled at 23 degrees relative to the sun and therefore we’d expect the sun to rise over here in the winter and over there in the summer—the facts of that are very hard to vary. It makes risky and narrow predictions. The axis tilt theory can predict the exact length of summer and winter at different latitudes, and you can test with perfection.

See, a good explanation. It has reach too.

Any planetary body in the universe, if tilted on its axis will have a “seasonal” variation of temperature dependent on its relevant star.

Okay, what questions do we need to be asking when an incident like that at the Fidelity Fiduciary Bank occurs se we can get a good explanation with the reach to be able to make modifications to systems and process that will mitigate the hazard of an incident like Mrs. Dawes’ throughout the organization?

Join me next Tuesday as we continue to travel the path of what is difficult, perilous, and uncertain as we explore introducing A New Order of Things.

I am your host, Eddie Killian. And this concludes Episode Four.

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References

Deutsch, D. (2011). The Beginning of Infinity. New York: Viking.

Deutsch, D. (2023, February 11). Davd Deutsch: Knowledge Creation and The Human Race. (N. Ravikant, Interviewer)

Kahneman, D. (2013). Thinking, Fast and Slow. New York: Farrar, Straus and Giroux.

Popper, K. (1983). The Aim of Science. New York: Routledge.