IDBS Data Management Blog

Say what you’re not saying, don’t say it, say what you didn’t say

Last time I blogged that modelling is not limited to software engineering, play and simulation; but is universal in human endeavour. I mentioned that considering accuracy is important but not sufficient in assessing a model. What other considerations are there?

My favourite lens for looking at a model is abstraction. In philosophical terminology, abstraction is about grouping concepts together at decreasing levels of detail. So, a duck is a duck and no other thing is a duck (no matter how it looks or walks or sounds); but applying abstraction allows us to talk about birds and say useful things, which might be rather exasperating if we had to list every bird in the world to say them. This kind of classification is a particular feature of object-oriented programming languages (which may or may not be a good thing).

Leaving it out

A modeller, not saying

However another way of considering abstraction is to pause before asking what a model is saying, and ask: what is this model not saying?

The model of biological change that we call evolution has incredible empirical support, so that its application has great explanatory and predictive power (some would even say that we don’t apply it enough). Strangely, though, it seems to cause an awful lot of consternation to those who subscribe to another model called creationism.

Why strange? At first sight, both of these models deal with how the world came to be the way it is. But evolution models a process, and has nothing whatsoever to say about how that process began, or why it began, or who began it. Conversely, creationism says nothing about how its proposed agent went about his craft (well, usually). He just did it. Apples and oranges.

Putting it back in

Any critical analysis or use of a model has to carefully stick to assessing or building upon what it actually models. This might sound simple, but humans find it remarkably tricky. We are fond of making cultural and doctrinal assumptions and applying intuitions without knowing about it. (In a black alley, a black cat spies a black rat. How?*)  Unfortunately this is not only inevitable, it’s usually necessary.

Why so? Models almost always rely on background information. Of particular interest in computer science and artificial intelligence is the notion of semantics: the meaning of symbols. Tell a robot to fetch you a cuppa, and it may suffer the same semantic confusion as is now affecting US readers: a cuppa what?

With the pace of technological change it is more relevant than ever to question not only how children are taught but also what they are taught. From simple adjustments like replacing cursive handwriting with typing, to deep questions over how learning itself is learned, we can expect (and hope for) seismic upsets in educational practice in the near future as the internet age continues to settle in.

There is no doubt that software systems have transformed the way we work and communicate. But I believe that we can also learn a great deal from the way that they are engineered, especially from the trials and tribulations that have beset our struggle to establish best practices. One aspect of this that is particularly dear to my heart is modelling.

Modelling is everywhere

Skoda cake engine: accurate, but not particularly useful

In exploring the relationship between modelling and program code I have gradually come to the realisation that modelling is a critical skill, not just in software engineering but also in all walks of life. Some of the purest considerations of modelling are actually universally applicable. In this post I’d like to explore why, before taking a closer look in forthcoming posts at some of the key defining characteristics of modelling that are so fundamental that I think they deserve representation on the national curriculum.

So what is modelling? At its heart, it is about creating a representation of something that enables communication or reasoning. For a child, not being able to steer a police boat onto an airport runway does not preclude exploring the consequences in a Lego town. Building a bridge in a CAD package first can be used to cheaply explore stresses and, perhaps in hindsight, the collective behaviour of people on it. A simple relationship between energy, mass and the speed of light can be used to reason about the behaviour of, well, everything.

Little models have lesser models

That covers modelling in play, engineering, science and maths. Let’s go further. What is this blog post? A bunch of symbols intended to communicate an idea in my head. It’s not the idea itself, but a model of it. Actually, neither is it what is in my head: that’s a model too, which I’m using to reason about the idea. I’m clearly getting carried away here and butting up against some deep philosophical notions that I have no right or qualification to comment upon, but bear with me.

What is the use of categorising most of human endeavour and thought as modelling? Let’s go back to play. Children model all the time with toys and role-play, because the many adult situations they want to learn about are not available for them to experience. But as modelling becomes more serious and is used for communication and reasoning with immediate consequences of benefit or harm, we start to worry about value. Is our model actually any good, or are we missing something important?

The real value of modelling

There are several considerations that might give us a picture of the value of our model. Importantly, we are not just looking for accuracy. It is perfectly possible to create a model that is wholly accurate but tells you nothing useful, or actually misleads you. In spoken language this can be achieved very effectively using something Neal Stephenson calls bulshytt: “euphemism, convenient vagueness, numbing repetition, and other such rhetorical subterfuges”. But this applies to all models, not just those expressed in language.

What if children were taught to critically analyse models of every kind, using a universal set of criteria? I think the future for understanding the world around us and innovation of all kinds would be a little bit brighter.

The first and foremost of these considerations is the model’s level of abstraction. But that’s probably enough for now. I’ll make that my main topic in Part II.