Is String theory true? Has Latin America seen a political polarization in the last ten years? Is a keto-oriented diet dangerous for your heart? The only reasonable answer to these questions – for the absolut majority of us – is that we do not know. If you are a physicist working in String theory, a deep political expert with years in studying Latin-American politics or a medical doctor focused on diets and their adverse effects you will naturally have something to say here – but most of us are not. Yet, it is not hard to find people who have views on all of the subjects above.

What we do when we answer questions like this is not that we disclose knowledge – we make stuff up. We confabulate. Confabulation is, the almighty Wikipedia tells us, “a memory error defined as the production of fabricated, distorted, or misinterpreted memories about oneself or the world. People who confabulate present incorrect memories ranging from “subtle alterations to bizarre fabrications”,^[1] and are generally very confident about their recollections, despite contradictory evidence”.

It is worth studying this more in detail. In her fascinating book Being Wrong, author Kathryn Schulz notes that:

Most of us, however, are noticeably better at generating theories than at registering our own ignorance. Hirstein says that once he began studying confabulation, he started seeing sub-clinical versions of it everywhere he looked, in the form of neurologically normal people “who seem unable to say the words, ‘I don’t know,’ and will quickly produce some sort of plausible-sounding response to whatever they are asked.” Such people, he says, “have a sort of mildly confabulatory personality.”*

Actually, all of us have mildly confabulatory personalities.

Being Wrong: Adventures in the Margin of Error, p 83

So, why is it so hard to say that we do not know? One answer is of course that we want to be helpful, and be seen as insightful. And not knowing is generally perceived as a social flaw, but on the other hand confabulation often means that you end up being wrong, which is a worse social flaw. But the reality is that very few of our opinions are tested or submitted to more rigorous review, so we do get away with it.

There is significant power in not knowing, however. And what is more important: it may help others admit the same thing and then we can start making some real progress in finding things out. What we need then, is a mental model of what it means not to know. It is not that we “come up empty” and lack value and contribution – it is more that we have found a reason to search for the right answer, and in that search we engage others.

“I don’t know” really should translate into “We should find that out”.

Not knowing is agreeing to explore rather than giving up.

When we are on this subject we may also want to think about what some of the consequences of the tendency to confabulate is. A correlate to the observation that there is a lot of things we do not know is that there are a lot of things other people do not know either, and we tend to forget that. A case in point is what Michael Crichton called the Gell Mann effect:

Media carries with it a credibility that is totally undeserved. You have all experienced this, in what I call the Murray Gell-Mann Amnesia effect. (I refer to it by this name because I once discussed it with Murray Gell-Mann, and by dropping a famous name I imply greater importance to myself, and to the effect, than it would otherwise have.)

Briefly stated, the Gell-Mann Amnesia effect is as follows. You open the newspaper to an article on some subject you know well. In Murray’s case, physics. In mine, show business. You read the article and see the journalist has absolutely no understanding of either the facts or the issues. Often, the article is so wrong it actually presents the story backward — reversing cause and effect. I call these the “wet streets cause rain” stories. Paper’s full of them.

In any case, you read with exasperation or amusement the multiple errors in a story, and then turn the page to national or international affairs, and read as if the rest of the newspaper was somehow more accurate about Palestine than the baloney you just read. You turn the page, and forget what you know.

That is the Gell-Mann Amnesia effect.

See http://geer.tinho.net/crichton.why.speculate.txt

Once we realize that confabulation makes up a significant piece of what we read in the news, we may want to seek to change the things we read and seek out more substantive reading – read research and books instead of news, for example — but more importantly, constantly reminding us that many of the articles in the news probably should be translated to a quiet “I don’t know”.

There is a certain liberty in that! When we do not know we are free to start learning.

This article discusses a subject that increasingly has caught my interest: what is a good explanation? This is an old question in philosophy – and deciding that something is an explanation of some fact or phenomenon is not as straightforward as it seems. Explanations can operate at different levels and we may decide that different explanations are more or less relevant for different kinds of systems.

An example of this is the use of the concept of “stances” as applied by philosopher DC Dennett. Dennett suggests that any phenomenon or system can predicted – which is not the same as explained, but stay with me – from (at least) three different stances: the physical, design and intentional stance. The kind of predictions you apply will start from very different positions, and these positions are, in a sense explanations of the systems themselves. The physcial stance explains what we are studying as governed by physical laws and phenomena, the design stance looks to the purpose of the system’s design and the intentional stance explains a system as having intentions and emotions.

The way we approach predicting a system is related to how we explain it, albeit not in an entirely straightforward way – and Dennett’s stances are powerful mental models. They apply, among other things, to the analysis of computer systems. Someone who takes an algorithmic stance is doing something that is very close to someone taking the physical stance, whereas someone who takes an architectural stance is much more akin to someone who takes a design stance. The question of AI, then, is essentially a question of when we take an intentional stance to software – very crudely put.

When policy makers demand that tech companies explain what their systems do, it would be helpful if they could agree on what stance it is that they are taking – since algorithmic stances may very well be as useless as the physical stance is when explaining how a car works or why an ant hill behaves in a certain way.

Now, explanations are not just interesting because they allow us to predict systems in different ways. They are also interesting because we can optimize them in different ways.

In a recent paper, two researchers looked at what it is that we are valuing in explanations and found that there are (at least) two different dimensions along which we can evaluate an explanation. The parsimony of the explanation and the co-explanatory value that an explanation may have. The first dimension looks at how compressed the explanation is – what is the shortest program I can write that produces this system / pattern – but co-explanation looks at how many other things an explanation can order in one and the same explanatory pattern.

In an interesting application, the authors suggest that conspiracy theories are examples of over-optimizations on the co-explanatory axis, where we seek one underlying cause that explains everything that is happening. Conspiracy theories are attempts – as I have suggested elsewhere (in Swedish) – to compress the program you need to explain the world in a way that generates everything from a single set of initial conditions.

So what kind of explainer are you? Which stance do you prefer and do you seek parsimony or inclusive co-explanations? And more importantly – can you use these different mental models to shift between stances and value dimensions in explanations, and learn something new about what you study?