Murder of a Chemistry prof (The Square root of Murder, 2011)

 

    Ada Madison (Camille Minichino). The Square Root of Murder (2011) We are in summer school at Henley College, one of the last remaining universities for women, which is facing momentous changes when co-education begins with the Fall semester. The most detested professor on campus, Dr Appleton of the Chemistry Department, is murdered. Dr Sophie Knowles of the Mathematics Department solves the case, mostly by handing useful clues to the cops after sussing out their relevance and thereby figuring out what other clues she needs and perhaps where to find them. The puzzle is quite good, the resolution involves the now-mandatory near-death experience of a last-ditch attack on the sleuth by the perpetrator, and a several of the red herrings lead to resolutions of sub-plots. There is the fey but practical friend of the sleuth, the macho but sensitive boyfriend, the students who should know better, the cop who’s a buddy and the one who isn’t, and so on.
      So, given a pretty good concept for setting and a plot, and the usual cast of genre-characters, how does Madison handle it? Merely average. A beach-book, you can read it with half your attention on something else. The academic setting is merely sketched, the ambience is suggested by scattered brand references, adjectives appear where they aren’t needed, the characters are vague and nebulous. Knowles is a puzzle-setter by avocation, but we don’t see any of them (it would have added a nice layer of diversion). Back when pulp fiction came in magazines, this would have been ruthlessly edited down to novella length. As it is, it’s a lazy read. Not unpleasant, but not exactly an attention grabber. Later printings included some puzzles.  *½

George Johnston, underrated.

In 1959, George Johnston published a collection of poems titled The Cruising Auk. It went through five impression by 1964, when I bought our copy after hearing Johnston read his poems. He was charming and diffident, and so were his poems. They have been underrated, I think. The last 5 lines of “War on the Periphery” may show why. He’s watching his children grow up:


They eat my heart and grow to men.

I watch their tenderness with fear
While on the battlements I hear
The violent, obedient ones
Guarding my peaceful life with guns.

Wikipedia has a good article about him. The book is out of print. If you find one, buy it, and cherish it. See also my longer review posted 2017-10-23.

Murphy's Law: Why we think things go wrong when they don't.

     Richard Robinson. Why the Toast Always Lands Butter Side Down (2005) Or: The Science of Murphy’s Law. The title suggests that we’ll ,learn physics and chemistry and stuff like that. Instead we learn about perception. Murphy’s Law is in the eye of the victim: Our understanding of the way the world works is good enough for dealing with everyday risks such as sabre tooth tigers, but simply wrong when it comes to reality. We overestimate and underestimate odds depending on whether the event is good or bad; we assume cause-effect when there is simple coincidence; the world as we experience it is a roughly computed illusion based on limited and filtered sense data; we see what we expect to see and ignore what we don’t expect; we rely on quick, mostly sub-conscious calculations; we extrapolate patterns in time and space from the flimsiest data. In fact, it’s surprising that we manage as well as we do.
     Well-done. Robinson has the knack of making abstruse concepts clear, of seeing the example in daily experience that makes his point. He’s also careful to reference sources: all the counter-intuitive claims sport a footnote number. Come to think of it, the book implies a better definition of intuitive: it just means “matching the illusions our sense present to us.”
     Recommended. ***

Is Banksy's popularity evidence of inability to see art?

Found in the Guardian: Something that needed to be said about Banksy and other easy-to-assimilate art. Including music, which the iPod and iTunes have reduced to sonic wallpaper and mere ear-massage.

Cartoons and Comic Strips: Larson and Trudeau

     Gary Larson. Wildlife Preserves (1989) I never tire of Gary Larson. I think this is the fifth time I’ve read this collection of his cartoons. His gift is to imagine how a different context would affect the lives of people, animals, and of course monsters. Such as the unfortunate fish whose tail is embedded in two styrofoam shoes, which drag him up to “sleep with the humans.”  Or a flea painting a dogscape, which consists of acres of fur. Or Thor’s workbench, on which rest his hammer, his screwdriver, and his crescent wrench.
     Well, maybe you have to have the same sense of seeing the logically absurd.
     Recommended. ****

     G. B. Trudeau. Check Your Egos at the Door (1984, 1985) A Doonesbury collection. These strips were drawn during the reelection of Reagan. It’s depressing to see how little has changed since then. The only real difference is that liberals and conservatives were still talking to each other, whereas now they either scream at or ignore each other. The strips rely on words, so a brief quote is impossible, but I’ll try:
     Duane: I can’t get over these figures, Rick. Suburbanites went for Reagan 65% to 35%, fundamentalist 89% to11%, car dealers 54% to 46%...
     Rick: Duane, you can’t let all that get to you....
     Sounds a lot like the Dems trying to figure out how they lost to Trump. Except that Reagan won the popular vote, and Trump didn’t. ****

You may want to write a script for this

An image from this series was posted on a Usenet group. I liked it.

Some theoretical talk about theories.

Theory, Model, Algorithm, and the Limits of Knowledge

These three terms that are often used interchangeably. They do have something in common, we’ll see what it is after an attempt to differentiate them, by describing how what they refer to differs.

Framework: The world we live in is “reality”. We interact with it in various ways. As we grow from infancy to adulthood, we develop various methods of predicting how reality works so that we can get what we need and want. Explicit ideas about how reality works are the theories on which we base our actions. We reason about the state of reality right now so that we can change it to suit ourselves.

For example, we plant seeds when we figure the weather is favourable so that we will get tomatoes a couple of months or so later. We add fertiliser and soil conditioners and water to ensure that the tomatoes will grow. Those actions are based on a bundle of interconnected ideas and observations that form a more or less coherent theory about how tomatoes grow from seeds.

Theory: An explanation of how something works the way it does. It’s what you get when you test a hypothesis, which is a more or less speculative explanation of some observation(s). Many hypotheses are prompted by anecdotes about some oddity, or about some claim that strikes the hypothesiser as odd. A good hypothesis links the observation(s) to some existing explanation, and predicts additional observations. If those links hold up, and/or the predictions prove true, then the hypothesis is confirmed and becomes a theory. A good theory implies or suggests further hypotheses, which in turn imply new observations.

When a theory is applied to some practical problem, we get a model. That, and the desire to just figure things out, are what drive science and engineering.

Model: An explanation that can be used to predict how some part of reality will work. We use this term because a conceptual model about growing tomatoes is analogous to a physical model of, say, a steam locomotive. A scale model is not a replica, it is something that looks like, and in  some specific, limited ways works like its prototype. The model locomotive may operate on steam as the prototype does, but even so, there will be compromises. E.g., the thickness of the boiler shell will not be to scale for that would make it too weak to contain the necessary steam pressure. And so on.

We use both models and theories to plan what to do so as to get some desired result. The difference is subtle. We test a theory’s predictions in order to discover its limits, so that if necessary we can modify it, extend it, or even replace it. We use a model within its limits to control some aspect of reality as much as possible. We may use a model to test a theory: An experiment is a model constructed from that part of a theory that we wish to test. It’s not easy to derive a model from a theory: Models also have to be tested.

Both models and theories are true insofar as they work. When a model becomes a precise set of reliable rules, it becomes an algorithm.

Algorithm: A set of procedures applied to some inputs that will produce outputs in a predictable way. Thus, “long division” is an algorithm because it describes how to manipulate the input numbers (divisor and dividend) to get the answer (quotient). A recipe for a grilled cheese sandwich is an algorithm because it describes how to manipulate the inputs (bread, cheese, and a grill) so as to get an output (a tasty sandwich). And so on.

Algorithms are everywhere. They are especially handy for determining future values of present states. In this sense, an algorithm is a knowledge machine: input information about “this thing here and now”, turn the crank, and you get information about “this thing somewhere, somewhen, somehow else”.

If the above comments make sense, we may see a model as a set of mutually compatible and interconnected algorithms, and a theory as a set of validated and interconnected models.

And that brings us to what they have in common: All three are modes of gaining new knowledge. All three operate on the same fundamental principle: “If you do this, you will find out that”. None of them “describe reality”. They describe only how we may interact with or observe certain aspects of reality. Which ones? Those that the theory or model or algorithm “is about.” What “is about” means is not easy to say. An example will explain (as far as the example applies, that is):

We may use Newton’s laws of motion to build a model that calculates the course of a rocket launched towards Jupiter. If we know its mass and its velocity, the varying gravitational forces of the Moon and Mars etc, we can calculate, and recalculate, its course to whatever precision we like. But the model will tell us nothing about the health of the crew. If we want to know that, we need another (and more complicated and less certain) model. The model cannot tell us what the rocket “really is”, only how it interacts with gravitational fields and the reaction forces of its engines. If we want to know other things, such as its shell’s resistance to fatigue cracking, we must use other models. What’s more, even to monitor the course of the rocket, we have to use other models, the ones that describe how our instruments work.

Thus all theories, all models, all algorithms are knowledge engines. They are epistemological devices. They are not ontological descriptions. But they are limited. They can’t tell us what some entity really is, only how we can interact with it, and what will happen when we do so.

Even the notion of “entity” is fundamentally epistemological: An entity is a more or less consistent bundle of expected interactions. If any of them are missing or unexpected, we doubt that we are interacting with that entity. It may be an hallucination, or a dream, or a fake, or merely an image of the entity. Or a model of it.

Kant was right, I think: There is no way to know reality in itself. That doesn’t mean there is no reality “out there”. It just means that we can know only our interactions with it. That we can know even that much is I think at least as great a puzzle as what it is that we can’t know.

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