For over a decade, mathematician Jeffrey Shallit has been an outspoken critic of intelligent design. Recently, in a series of blog posts, he has attacked Stephen Meyer’s book Signature in the Cell (SITC) for what he sees as a variety of shortcomings. Some of Shallit’s criticisms merit careful attention, which we’ll present here in weeks to come.
Other criticisms, however, are fluffy confections, failing to achieve even the slightness of what Hume called “mere cavils and sophisms.” Let’s look at one such bonbon of sophistry, Shallit’s claim that weather forecasting represents a devastating counterexample to SITC’s argument that complex specified information is, universally in human experience, produced by a mind or intelligence.
Even if we accept Meyer’s informal definition of information with all its flaws, his claims about information are simply wrong. For example, he repeats the following bogus claim over and over:
p. 16: “What humans recognize as information certainly originates from thought – from conscious or intelligent human activity… Our experience of the world shows that what we recognize as information invariably reflects the prior activity of conscious and intelligent persons.” […]
I have a simple counterexample to all these claims: weather prediction. Meteorologists collect huge amounts of data from the natural world: temperature, pressure, wind speed, wind direction, etc., and process this data to produce accurate weather forecasts. So the information they collect is “specified” (in that it tells us whether to bring an umbrella in the morning), and clearly hundreds, if not thousands, of these bits of information are needed to make an accurate prediction. But these bits of information do not come from a mind – unless Meyer wants to claim that some intelligent being (let’s say Zeus) is controlling the weather. Perhaps intelligent design creationism is just Greek polytheism in disguise!
Poor Zeus: stand-in deity for yet another counterexample. And he only gets union scale.
1. Collecting Meteorological Data
To see what’s wrong with this putative counterexample, begin by asking yourself if you know — without peeking online at a weather page, or looking at a barometer — what the atmospheric (barometric) pressure happens to be in your immediate neighborhood, right now.
Any guesses? Well, how about the temperature, or the wind speed and direction? Again, no peeking allowed. Give yourself a moment or two to write down the correct values. Okay, stop.
The fact is, unless you cheated, you don’t know the relevant measurements for your immediate surroundings (and nor do I, as I write this, for my neighborhood). So what would we need to obtain those data?
Measuring instruments, such as (a) a barometer, (b) a thermometer, (c) a wind speed indicator (an anemometer), (d) a wind direction indicator, and so on. So let’s suppose we have these instruments, and we retrieve data from all of them.
Can we now predict tomorrow’s weather? Do we have, as Shallit argues, complex specified information?
2. Turning Data into Predictions
No. We have a few data points. To create an accurate weather prediction, we’re going to need data retrieved from hundreds or thousands of instruments, distributed or coordinated across a wide geographic area, and taken over a range of time intervals.
We’re going to need something more, however, without which all those measurements will tell us nothing. We need an analytical model — an algorithm — and a computer to run that model.
Shallit glides over this essential step in how data become predictions with his innocent, almost blushingly naïve verb, “process”:
Meteorologists collect huge amounts of data from the natural world: temperature, pressure, wind speed, wind direction, etc., and process this data to produce accurate weather forecasts. (emphasis added)
Now, “process” can mean many things. What “process” manifestly does not mean in the case of weather forecasting, however, is the mechanical transmission of thousands of bits of data directly from measuring instruments to end-users. That would look like this:
And we’d be none the wiser. There is a reason we don’t receive our weather predictions this way: raw data aren’t predictions at all. Collecting measurements from instruments, and mechanically transmitting those data, without any interpretation or analysis, does not (indeed, cannot) make any specified predictions.
To be sure, there is complexity aplenty in the data, but, as SITC explains, that complexity is unspecified. Unspecified complexity is what natural causes produce. And thus, because that “information” lacks specification, it is useless (by itself) for yielding genuine predictions. No specificity; no forecast.
By contrast, in real weather forecasting, data only become complex specified information — that is, genuine predictions — by passing through an intelligently-designed algorithm: a computer model, in most instances. But the measurements themselves don’t give us the model. They can’t.
3. The Intelligence in the Story
Meteorologists construct models, using their minds (their analytical intelligence). The useful, complex specified information that emerges from a computer model comes to us via the action of intelligent agents, and not otherwise. The true “process” therefore looks like this:
Shallit would succeed if he could show how raw meteorological data yield testable weather forecasts, without those data ever passing through the analytical filter of an intelligently designed model or algorithm.
Good luck with that.
One final point. In other writings, Shallit has indicated his hostility to the notion of human agency. In light of this, it’s perhaps not surprising that Shallit reduces the creative intellectual activity of meteorologists, who can improve their predictions by designing better and more powerful algorithms, to the bland and seemingly agency-free verb, “process.”
But unpacking that verb shows clearly that intelligent causation is actually indispensable, whether Shallit sees it or not.
Support your local meteorologist.