Network Theory (Part 30)

3 October, 2014

The network theory series is back! You may have thought it died out, but in fact it’s just getting started. Over the last year my grad students have made huge strides in working out the math of networks. Now it’s time to explain what they’ve done.

In the last three episodes I explained how electrical circuits made of resistors, inductors and capacitors are a great metaphor for many kinds of complex systems made of interacting parts. And it’s not just a metaphor; there are mathematically rigorous analogies—in other words, isomorphisms—between such electrical circuits and various other kinds of ‘passive linear networks’.

I showed you a chart of these analogies last time:

displacement:    q flow:      \dot q momentum:      p effort:           \dot p
Mechanics: translation position velocity momentum force
Mechanics: rotation angle angular velocity angular momentum torque
Electronics charge current flux linkage voltage
Hydraulics volume flow pressure momentum pressure
Thermal Physics entropy entropy flow temperature momentum temperature
Chemistry moles molar flow chemical momentum chemical potential

But what do I mean by a ‘passive linear network’? Let me explain this very roughly at first, since we’ll be painfully precise later on.

Right now by ‘network’ I mean a graph with gizmos called ‘components’ on the edges. For example:

In a network there is some kind of ‘flow’ running along each edge, and also some kind of ‘effort’ across that edge. For example, in electronics the flow is electrical current and the effort is voltage. The chart shows the meaning of flow and effort in other examples.

‘Passivity’ means roughly that none of the components put out energy that didn’t earlier come in. For example, resistors lose energy (which goes into heat, which we’re not counting). Capacitors can store energy and later release it. So, resistors and capacitors are passive—and so are inductors. But batteries and current sources actually put out energy, so we won’t allow them in our networks yet. For now, we’re just studying how passive components respond to a source of flow or effort.

For some subtleties that show up when you try to make the concept of passivity precise, try:

Passivity (engineering), Wikipedia.

Finally, ‘linearity’ means that the flow along each edge of our network is linearly related to the effort across that edge. Here are the key examples:

• For electrical resistors, linearity is captured by Ohm’s law. If an edge e in our network is labelled by a resistor of resistance R, usually drawn like this:

then Ohm’s law says:

V = R I

where V is the voltage across that edge and I is the current along that edge.

• If our edge e is labelled by an inductor of inductance L:

we have

\displaystyle{ V = L \frac{d I}{d t} }

Here we need to think of the voltage and current as functions of time.

• If our edge e is labelled by a capacitor of capacitance C:

we write the equation

\displaystyle{ I = C \frac{d V}{d t} }

where again we think of the voltage and current as functions of time.

Both linearity and passivity are simplifying assumptions that we eventually want to drop. If we include batteries or current sources, we’re dropping passivity. And if include transistors, we’re dropping linearity. Obviously both these are important!

However, there is a lot to do even with these simplifying assumptions. And now it’s time to get started!

In what follows, I will not use the terms ‘flow’ and ‘effort’, which are chosen to be neutral and subject-independent. Instead, I’ll use the vocabulary from electronics, e.g. ‘current’ and ‘voltage’. The reason is that we’ve all heard of resistors, capacitors, Ohm’s law and Kirchhoff’s laws, and while these have analogues in every row of the chart, it seems pointless to make up weird new ‘neutral’ terms for all these concepts.

But don’t be fooled by the jargon! We’re not merely studying electrical circuits. We’ll be studying passive linear networks in full generality… with the help of category theory.

Linear passive networks as morphisms

To get going, let’s think about circuits made of resistors. We can do this without harm, because we’ll later include capacitors and inductors using a simple effortless trick. Namely, we’ll generalize the ‘resistance’ of a resistor, which is a real number, to something called ‘impedance’, which is an element of some larger field. Everything will be so abstract that replacing resistances with impedances will be as easy as snapping our fingers.

Right now I want to define a category where the morphisms are circuits made of resistors. Any morphism will go from some ‘inputs’ to some ‘outputs’, like this:

So a morphism is roughly a graph with edges labelled by numbers called ‘resistances’, with some special nodes called ‘inputs’ and some special nodes called ‘outputs’.

What can do with morphisms? Compose them! So, suppose we have a second morphism whose inputs match the outputs of the first:

Then we can compose them, attaching the outputs of the first to the inputs of the second. We get this morphism as the result:

So, composing morphisms is a way to build big electrical circuits—or other ‘linear passive networks’—out of little ones.

This seems pretty simple, but let’s try to formalize it and see why we have a category. In fact it takes a bit of thought. To check that we get a category, we need to check that composition is associative:

(fg)h = f(gh)

and that each object x has an identity morphism 1_x : x \to x that does what an identity should:

f 1_x = f

1_x g = g

All these equations seem obviously true in our example… until you try to prove them.

You might think an identity morphism should be a bunch of straight pieces of wire—a bunch of edges each with an input node and an output node—but that doesn’t quite work, since sticking an extra edge onto a graph gives a new graph with an extra edge!

Also, we are composing circuits by ‘sticking them together’. This process is formalized in category theory using a pushout, and pushouts are only defined ‘up to canonical isomorphism’. The very simplest example is the disjoint union of two sets. We all know what it means, but if you examine it carefully, you’ll see it’s only defined up to canonical isomorphism, because it involves a choice of how we make the two sets disjoint, and this choice is somewhat arbitrary.

All this means the category we’re after is a bit subtler than you might at first expect; in fact, it’s most naturally thought of as a bicategory, meaning roughly that all the equations above hold only ‘up to canonical isomorphism’.

So, we proceed like this.

First we define a concept of ‘labelled graph’, where (for now) only the edges are labelled. We do this because we want our circuits to have edges labelled by ‘resistances’, which are real numbers. But we do it in greater generality because later we’ll want the edges to be labelled by ‘impedances’, which are elements of some other field. And since we’re studying electrical circuits just as examples of networks, later still we will probably want graphs whose edges are labelled in still other ways.

So:

Definition. A graph consists a finite set E of edges, a finite set N of nodes, and two functions

s,t : E \to N

Thus each edge e will have some node s(e) as its source and some node t(e) as its target:

Definition. Given a set L, we define an L-labelled graph to be a graph together with a function r : E \to L. This assigns to each edge e \in E its label r(e) \in L. We call L the label set.

We use the letter r because for circuits of resistors we will take the label set to be

L = (0,\infty) \subset \mathbb{R}

the positive real numbers, and r(e) will be the resistance of the edge e. For circuits that also contain inductors and capacitors we will take the label set to be the positive elements of some larger field… but more about that later!

Now we want to give our L-labelled graph a set of nodes called ‘inputs’ and a set of nodes called ‘outputs’. You might think the set of inputs should be disjoint from the set of outputs, but that’s a tactical error! It turns out an identity morphism in our category should have the inputs being exactly the same as the outputs… and no edges at all:

To handle this nicely, we need to make a category of L-labelled graphs. This works in the obvious way, if you’re used to this stuff. A morphism from one L-labelled graph to another sends edges to edges, nodes to nodes, and preserves everything in sight:

Definition. Given L-graphs \Gamma = (E,N,s,t,r) and \Gamma' = (E',N',s',t',r'), a morphism of L-labelled graphs from \Gamma to \Gamma' is a pair of functions

\epsilon: E \to E'

\nu : N \to N'

such that the following diagrams commute:

There is a category L\mathrm{Graph} where the objects are L-labelled graphs and the morphisms are as we’ve just defined them.

Warning: the morphisms in L\mathrm{Graph} are not the morphisms of the kind we really want, the ones that look like this:

They are just a step along the way. A morphism of the kind we really want is a diagram like this in L\mathrm{Graph}:

where \Gamma is an L-labelled graph and I, O are L-labelled graphs with no edges!

You see, if I and O have no edges, all they have is nodes. We call the nodes of I the inputs and those of O the outputs. The morphisms i: I \to \Gamma and o : O \to \Gamma say how these nodes are included in \Gamma. \Gamma is our circuit made of resistors.

In general, any diagram shaped like this is called a cospan:

If we turned the arrows around it would be called a span. Cospans are good whenever you a thing with an ‘input end’ and an ‘output end’, and you want to describe how the ends are included in the thing. So, they’re precisely what we need for describing a circuit made of resistors, like this:

This makes us want to cook up a category L\mathrm{Circ} where:

• an object I is an L-labelled graph with no edges. We can alternatively think of it as a finite set: a set of nodes.

• a morphism from I to O is a cospan of L-labelled graphs:

We still need to say how to compose these morphisms. We know it will amount to attaching the outputs of one circuit to the inputs of the next—that’s all there is to it! But we need to make this precise and prove we get a category. And as I’ve hinted, we will actually get something bigger and better: a bicategory! This will come as no surprise to if you’re familiar with span and cospan bicategories–but it may induce a heart attack otherwise.

This bicategory can then be ‘watered down’ to give our category L\mathrm{Circ}. And when we take

L = (0,\infty)

we’ll get the category where morphisms are circuits made of resistors! We’ll call this \mathrm{ResCirc}.

Then I’ll explain what we can do with this category! There’s no end of things we could do with it. But the main thing Brendan does is study the ‘black-boxing’ operation, where we take a circuit, forget its inner details, and only keep track of what it does. This turns out to be quite interesting.

References

I thank Brendan Fong for drawing some of the pictures of circuits here. For the details of what I’m starting to explain here, read our paper:

• John Baez and Brendan Fong, A compositional framework for passive linear networks.

You can learn more about the underlying ideas here:

• Dean C. Karnopp, Donald L. Margolis and Ronald C. Rosenberg, System Dynamics: a Unified Approach, Wiley, New York, 1990.

• Forbes T. Brown, Engineering System Dynamics: a Unified Graph-Centered Approach, CRC Press, Boca Raton, 2007.

• Francois E. Cellier, Continuous System Modelling, Springer, Berlin, 1991.


Network Theory News

28 September, 2014

 

You may be wondering, somewhere deep in the back of your mind, what happened to the Network Theory series on this blog. It’s nowhere near done! I plan to revive it, since soon I’ll be teaching a seminar on network theory at U.C. Riverside. It will start in October and go on at least until the end of March.

Here’s a version of the advertisement I sent to the math grad students:

Network Theory Seminar

I’ll be running a seminar on Network Theory on Mondays from 3:10 to 4:30 pm in Surge 268 starting on October 6th.

Network theory uses the tools of modern math—categories, operads and more—to study complex systems made of interacting parts. The idea of this seminar is to start from scratch, explain what my grad students have been doing, and outline new projects that they and other students might want to work on. A lot has happened since I left town in January.

I hope to see you there!

If you want more detail, here is a sketch of what’s been happening.

1) Franciscus Rebro has been working on “cospans”, a very general way to treat a physical system with inputs and outputs and treat it as a morphism in category. This underlies all the other projects.

2) Brendan Fong, a student of mine at Oxford, is working on a category where the morphisms are electrical circuits, and composing morphisms is sticking together circuits:

• Brendan Fong, A compositional approach to control theory.

• John Baez and Brendan Fong, A compositional framework for passive linear networks.

The first paper here is an outline of the project; the second one actually carries out the project, or at least a large chunk of it.

3) Blake Pollard, a student in the physics department, has been studying Markov processes. In a Markov process, things randomly hop from one vertex of a graph to another along edges. Blake has created a category where morphisms are ‘open’ Markov process, in which things flow in and out of certain special vertices called ‘terminals’.

4) Jason Erbele has been working on categories in control theory, the branch of math used to study physical systems that interact with the outside world via inputs and outputs. After finishing this paper:

• John Baez and Brendan Fong, Categories in control.

he’s been taking more concept from control theory and formalizing them using categories.

5) Oh yeah, and what about me? I gave a series of lectures on network theory at Oxford, and you can see videos of them here:

• John Baez, Network Theory.

Jacob Biamonte and I have also more or less finished a book on chemical reaction networks and Petri nets:

• John Baez and Jacob Biamonte, Quantum Techniques for Stochastic Mechanics.

But I won’t be talking about that; I want to talk about the new work my students are doing!


Exploring Climate Data (Part 2)

16 September, 2014

guest post by Blake Pollard

I have been learning to make animations using R. This is an animation of the profile of the surface air temperature at the equator. So, the x axis here is the longitude, approximately from 120° E to 280° E. I pulled the data from the region that Graham Jones specified in his code on github: it’s equatorial line in the region that Ludescher et al. used:

For this animation I tried to show the 1997-1998 El Niño. Typically the Pacific is much cooler near South America, due to the upwelling of deep cold water:

(Click for more information.) That part of the Pacific gets even cooler during La Niña:

But it warms up during El Niños:


You can see that in the surface air temperature during the 1997-1998 El Niño, although by summer of 1998 things seem to be getting back to normal:

I want to practice making animations like this. I could make a much prettier and better-labelled animation that ran all the way from 1948 to today, but I wanted to think a little about what exactly is best to plot if we want to use it as an aid to understanding some of this El Niño business.


The Logic of Real and Complex Numbers

8 September, 2014

I’ve always liked logic. I studied it a bunch in high school and college. Nowadays it’s a kind of hobby. I turn to it for relief sometimes when I become frustrated trying to figure out what I can do about global warming. Lately I’ve been digging a bit deeper into the logic behind the real and complex numbers. And I’m teaching a graduate course on real analysis this fall, so I actually have a slight excuse for doing this.

There’s something about logic that’s both fascinated and terrified me ever since I was a kid: it’s how we can’t fully pin down infinite structures, like the real or complex number systems, using a language with finitely many symbols and a theory with finitely many axioms.

It’s terrifying that we don’t fully know what we’re talking about when we’re talking about numbers! But it’s fascinating that we can understand a lot about the limitations.

There are many different things to say about this, depending on what features of these number systems we want to describe, and what kind of logic we want to use.

Maybe I should start with the natural numbers, since that story is more famous. This can also serve as a lightning review of some basic concepts which I’ll pretend you already vaguely know: first-order versus second-order logic, proofs versus models, and so on. If you don’t know these, you can either fake it or read some of the many links in this article!

Natural numbers

When Peano originally described the natural numbers he did so using axioms phrased in second-order logic. In first-order logic we can quantify over variables: for example, we can say

\forall x \; (P(x)) \; \Rightarrow \; P(y)) \;

which means that if the predicate P holds for all x it holds for any variable y. In second-order logic we can also quantify over predicates: for example, we can say

\forall P \; (P(x) \Leftrightarrow P(y)) \; \Leftrightarrow \; x = y

which says that x = y if and only if for every predicate P, P(x) is true precisely when P(y) is true. Leibniz used this principle, called the identity of indiscernibles, to define equality… and this is a nice example of the greater power of second-order logic. In first-order logic we typically include equality as part of the language and add axioms describing its properties, like

\forall x \; \forall y \; (x = y \Leftrightarrow y = x)

In second-order logic we can define equality and prove these properties starting from the properties we already have for \Leftrightarrow.

Anyway, in his axioms for the natural numbers, Peano used second-order logic to formulate the principle of mathematical induction in this sort of way:

\forall P \; \big[ P(0) \; \& \; \forall n \; ((P(n) \Rightarrow P(n+1)) \; \; \Rightarrow \; \; \forall n \; P(n))\big]

This says that if you’ve got any predicate that’s true for 0 and is true for n+1 whenever it’s true for n, then it’s true for all natural numbers.

In 1888, Dedekind showed that Peano’s original axioms for the natural numbers are categorical, meaning all its models are isomorphic.

The concept of ‘model’ involves set theory. In a model you pick a set S for your variables to range over, pick a subset of S for each predicate—namely the subset where that predicate is true —and so on, in such a way that all the axioms in that theory are satisfied. If two models are isomorphic, they’re the same for all practical purposes.

So, in simple rough terms, a categorical theory is one that gives a full description of the mathematical structure it’s talking about.

This makes Dedekind’s result sound like great news. It sounds like Peano’s original second-order axioms for arithmetic completely describe the natural numbers.

However, there’s an important wrinkle. There are many inherently undetermined things about set theory! So in fact, a categorical theory only gives a full description of the mathematical structure it’s talking about relative to a choice of what sets are like.

So, Dedekind’s result just shoves everything mysterious and undetermined about the natural numbers under the carpet: they become mysterious and undetermined things about set theory. This became clear much later, thanks to Gödel and others. And in the process, it became clear that second-order logic is a bit problematic compared to first-order logic.

You see, first-order logic has a set of deduction rules that are:

sound: Every provable sentence holds in every model.

semantically complete: Every sentence that holds in every model is provable.

effective: There is an algorithm that can correctly decide whether any given sequence of symbols is a proof.

Second-order logic does not! It’s ‘too powerful’ to also have all three of these nice properties.

So, these days people often work with a first-order version of Peano’s axioms for arithmetic. Instead of writing down a single axiom for mathematical induction:

\forall P \; \big[ P(0) \; \& \; \forall n \; P(n) \Rightarrow P(n+1)) \; \; \Rightarrow \;\; \forall n \; (P(n))\big]

we write down an axiom schema—an infinite list of axioms—with one axiom like this:

\phi(0) \; \& \; \forall n \; (\phi(n) \Rightarrow \phi(n+1)) \; \; \Rightarrow \;\; \forall n \; (\phi(n))

for each formula \phi that we can actually write down using the language of arithmetic.

This first-order version of Peano arithmetic is not categorical: it has lots of nonisomorphic models. People often pretend there’s one ‘best’ model: they call it the ‘standard’ natural numbers, and call all the others ‘nonstandard’. But there’s something a bit fishy about this.

Indeed, Gödel’s first incompleteness theorem says there are many statements about natural numbers that can neither be proved nor disproved starting from Peano’s axioms. It follows that for any such statement we can find a model of the Peano axioms in which that statement holds, and also a model in which it does not.

Furthermore, this remains true even if we add any list of extra axioms to Peano arithmetic, as long as there’s some algorithm that can list all these axioms.

So, I’d prefer to say there are many different ‘versions’ of the natural numbers, just as there are many different groups.

We can study these different versions, and it’s a fascinating subject:

• Wikipedia, Nonstandard models of arithmetic.

However, I want to talk about the situation for other number systems!

The real numbers

The situation is better for the real numbers—at least if we are willing to think about them in a ‘purely algebraic’ way, leaving most analysis behind.

To do this, we can use the theory of a ‘real closed field’. This is a list of axioms, formulated in first-order logic, which describe how +, \times, 0, 1 and \le work for the real numbers. You can think of these axioms as consisting of three parts:

• the field axioms: the usual algebraic identities involving +, \times, 0 and 1 together with laws saying that everything has an additive inverse and everything except 0 has a multiplicative inverse.

• the formally real field axiom, saying that -1 is not the square of anything. This implies that we can equip the field with a concept of \le that makes it into an ordered field—but not necessarily in a unique way.

• the real closed field axioms, which says that also for any number x, either x or -x has a square root, and every polynomial of odd degree has a root. Among other things this implies our field can be made into an ordered field in a unique way. To do this, we say x \le y if and only if y - x has a square root.

Tarski showed this theory is complete: any first-order sentence involving only the operations +, \times, 0, 1 and the relation \le can either be proved or disproved starting from the above axioms.

Nonetheless, the theory of real closed fields is not categorical: besides the real numbers, there are many other models! These models are all elementarily equivalent: any sentence involving just +, \times, 0, 1, \le and first-order logic that holds in one model holds in all the rest. But these models are not all isomorphic: we can’t get a bijection between them that preserves +, \times, 0, 1 and \le.

Indeed, only finite-sized mathematical structures can be ‘nailed down’ up to isomorphism by theories in first-order logic. You see, the Löwenheim–Skolem theorem says that if a first-order theory in a countable language has an infinite model, it has at least one model of each infinite cardinality. So, if we’re trying to use this kind of theory to describe an infinitely big mathematical structure, the most we can hope for is that after we specify its cardinality, the axioms completely determine it.

However, the real closed field axioms aren’t even this good. For starters, they have infinitely many nonisomorphic countable models. Here are a few:

• the algebraic real numbers: these are the real numbers that obey polynomial equations with integer coefficients.

• the computable real numbers: these are the real numbers that can be computed to arbitrary precision by a computer program.

• the arithmetical real numbers: these are the numbers definable in the language of arithmetic. More precisely, a real number x is arithmetical if there is a formula \phi in the language of first-order Peano arithmetic, with two free variables, such that

\displaystyle{ \forall m \; \forall n \; (\frac{m}{n} \le x \; \; \Leftrightarrow \; \; \phi(n,m)) }

Every computable real number is arithmetical, but not vice versa: just because you can define a real number in the above way does not mean you can actually compute it to arbitrary precision!

And indeed, there are other even bigger countable real closed fields, consisting of real numbers that are definable using more powerful methods, like second-order Peano arithmetic.

We can also get countable real closed fields using tricks like this: take the algebraic real numbers and throw in the number \pi along with just enough other numbers to get a real closed field again. Or, we could throw in both \pi and e. This probably gives a bigger real closed field—but nobody knows, because for all we know, \pi could equal e plus some rational number! Everyone believes this is false, but nobody has proved it.

There are also lots of nonisomorphic uncountable real closed fields, including ones that include the usual real numbers.

For example, we can take the real numbers and throw in an element \infty that is bigger than 1, 2, 3, \dots, and so on—and then do what it takes to get another real closed field. This involves throwing in elements like

-\infty, \; \infty + 1, \; 1/\infty, \; \infty^2, \; \sqrt{\infty}, \; \sqrt{\infty^2 + 17} , \dots

and so on. So, we get lots of infinities and infinitesimals.

It gets a bit confusing here, trying to figure out what equals what. But there’s another real closed field containing an infinite element that seems easier to manage. It’s called the field of real Puiseux series. These are series of the form

\sum_{i = k}^\infty a_i z^{i/n}

where k is any integer, perhaps negative, n is any
positive integer, and the coefficients a_i are real.

What’s z? It’s just a formal variable. But the real Puiseux series are real closed field, and z acts like 1/\infty: it’s positive, but smaller than any positive real number.

With considerably more work, we can make up a real closed field that:

• contains the real numbers,

• contains an element \infty bigger than 1,2,3, \dots, and

• obeys the transfer principle, which says that a first-order statement phrased in the usual language of set theory holds for the real numbers if and only if it holds for this other number system.

Any real closed field with these properties is called a system of hyperreal numbers. In the 1960s, the logician Abraham Robinson used them to make Leibniz’s old idea of infinitesimals in calculus fully rigorous. The resulting theory is called nonstandard analysis.

So, I hope you see there’s an exciting—or perhaps appalling—diversity of real closed fields. But don’t forget: they’re all elementarily equivalent. If a sentence involving just +, \times, 0, 1, \le and first-order logic holds in any one of these real closed fields, it holds in all of them!

You might wonder what second-order logic has to say about this.

Here the situation looks very different. In second-order logic we can do analysis, because we can quantify over predicates, which allows us to talk about subsets of real numbers. And in second-order logic we can write down a theory of real numbers that’s categorical! It’s called the theory of a Dedekind-complete ordered field. Again, we can group the axioms in three bunches:

• the field axioms: the usual algebraic identities involving +, \times, 0 and 1 together with laws saying that everything has an additive inverse and everything except 0 has a multiplicative inverse.

• the ordered field axiom, saying there is a total ordering \le such that x \le y and x' \le y' implies x + x' \le y + y' and x,y \ge 0 implies x y \ge 0.

• the Dedekind completeness axiom, which says that every nonempty subset with an upper bound has a least upper bound. But instead of talking about subsets, we talk about the predicates that hold on those subsets, so we say “for all predicates P such that…”

Because they’re categorical, people often use these axioms to define the real numbers. But because they’re second-order, the problem of many nonisomorphic models has really just been swept under the rug. If we use second-order logic, we won’t have a concept of ‘proof’ that’s sound, semantically complete and effective. And if we use first-order axioms for set theory to explicitly talk about subsets instead of predicates, then our set theory will have many models! Each model will have a version of the real numbers in it that’s unique up to isomorphism… but the versions in different models will be really different.

In fact, there’s a precise sense in which the ‘standard real numbers’ in one model of set theory can be the ‘hyperreals’ in another. This was first shown by Abraham Robinson.

The complex numbers

I mentioned that when we’re studying an infinite mathematical structure using first-order logic, the best we can hope for is to have one model of each size (up to isomorphism). The real numbers are far from being this nice… but the complex numbers come much closer!

More precisely, say \kappa is some cardinal. A first-order theory describing structure on a single set is called κ-categorical if it has a unique model of cardinality \kappa. And 1965, a logician named Michael Morley showed that if a list of axioms is \kappa-categorical for some uncountable \kappa, it’s \kappa-categorical for every uncountable \kappa. I haven’t worked my way through the proof, which seems to be full of interesting ideas. But such theories are called uncountably categorical.

A great example is the ‘purely algebraic’ theory of the complex numbers. By this I mean we only write down axioms involving +, \times, 0 and 1. We don’t include anything about \le this time, nor anything about complex conjugation. You see, if we start talking about complex conjugation we can pick out the real numbers inside the complex numbers, and then we’re more or less back to the story we had for real numbers.

This theory is called the theory of an algebraically closed field of characteristic zero. Yet again, the axioms come in three bunches:

• the field axioms.

• the characteristic zero axioms: these are an infinite list of axioms saying that

1 \ne 0, \quad 1+1 \ne 0, \quad 1+1+1 \ne 0, \dots

• the algebraically closed axioms: these say that every non-constant polynomial has a root.

Pretty much any mathematician worth their salt knows that the complex numbers are a model of these axioms, whose cardinality is that of the continuum. There are lots of different countable models: the algebraic complex numbers, the computable complex numbers, and so on. But because the above theory is uncountably categorical, there is exactly one algebraically closed field of characteristic zero of each uncountable cardinality… up to isomorphism.

This implies some interesting things.

For example, we can take the complex numbers, throw in an extra element, and let it freely generate a bigger algebraically closed field. It’s ‘bigger’ in the sense that it contains the complex numbers as a proper subset, indeed a subfield. But since it has the same cardinality as the complex numbers, it’s isomorphic to the complex numbers!

And then, because this ‘bigger’ field is isomorphic to the complex numbers, we can turn this argument around. We can take the complex numbers, remove a lot of carefully chosen elements, and get a subfield that’s isomorphic to the complex numbers.

Or, if we like, we can take the complex numbers, adjoin a really huge set of extra elements, and let them freely generate an algebraically closed field of characteristic zero. The cardinality of this field can be as big as we want. It will be determined up to isomorphism by its cardinality.

One piece of good news is that thanks to a result of Tarski, the theory of an algebraically closed field of characteristic zero is complete, and thus, all its models are elementarily equivalent. In other words, all the same first-order sentences written in the language of +, \times, 0 and 1 hold in every model.

But here’s a piece of strange news.

As I already mentioned, the theory of a real closed field is not uncountably categorical. This implies something really weird. Besides the ‘usual’ real numbers \mathbb{R} we can choose another real closed field \mathbb{R}', not isomorphic to \mathbb{R}, with the same cardinality. We can build the complex numbers \mathbb{C} using pairs of real numbers. We can use the same trick to build a field \mathbb{C}' using pairs of guys in \mathbb{R}'. But it’s easy to check that this funny field \mathbb{C}' is algebraically closed and of characteristic zero. Since it has the same cardinality as \mathbb{C}, it must be isomorphic to \mathbb{C}.

In short, different ‘versions’ of the real numbers can give rise to the same version of the complex numbers!

References

So, I hope you see that the logical foundations of the real and complex number systems are quite slippery… yet with work, we can understand a lot about this slipperiness.

Besides the references I’ve given, I just want to mention two more. First, here’s a free introductory calculus textbook based on nonstandard analysis:

• H. Jerome Keisler, Elementary Calculus: an Infinitesimal Approach, available as a website or in PDF.

And here’s an expository paper that digs deeper into uncountably categorical theories:

• Nick Ramsey, Morley’s categoricity theorem.


Science, Models and Machine Learning

3 September, 2014

guest post by David Tweed

The members of the Azimuth Project have been working on both predicting and understanding the El Niño phenomenon, along with writing expository articles. So far we’ve mostly talked about the physics and data of the El Niño, along with looking at one method of actually trying to predict El Niño events. Since there’s going to more data exploration using methods more typical of machine learning, it’s a good time to briefly describe the mindset and highlight some of differences between different kinds of predictive models. Here we’ll concentrate on the concepts rather than the fine details and particular techniques.

We also stress there’s not a fundamental distinction between machine learning (ML) and statistical modelling and inference. There are certainly differences in culture, background and terminology, but in terms of the actual algorithms and mathematics used there’s a great commonality. Throughout the rest of the article we’ll talk about ‘machine learning models’, but could equally have used ‘statistical models’.

For our purposes here, a model is any object which provides a systematic procedure for taking some input data and producing a prediction of some output. There’s a spectrum of models, ranging from physically based models at one end to purely data driven models at the other. As a very simple example, suppose you commute by car from your place of work to your home and you want to leave work in order to arrive homeat 6:30 pm. You can tackle this by building a model which takes as input the day of the week and gives you back a time to leave.

• There’s the data driven approach, where you try various leaving times on various days and record whether or not you get home by 6:30 pm. You might find that the traffic is lighter on weekend days so you can leave at 6:10 pm while on weekdays you have to leave at 5:45 pm, except on Wednesdays when you have to leave at 5:30pm. Since you’ve just crunched on the data you have no idea why this works, but it’s a very reliable rule when you use it to predict when you need to leave.

• There’s the physical model approach, where you attempt to infer how many people are doing what on any given day and then figure out what that implies for the traffic levels and thus what time you need to leave. In this case you find out that there’s a mid-week sports game on Wednesday evenings which leads to even higher traffic. This not only predicts that you’ve got to leave at 5:30 pm on Wednesdays but also lets you understand why. (Of course this is just an illustrative example; in climate modelling a physical model would be based upon actual physical laws such as conservation of energy, conservation of momentum, Boyle’s law, etc.)

There are trade-offs between the two types of approach. Data driven modelling is a relatively simple process. In contrast, by proceeding from first principles you’ve got a more detailed framework which is equally predictive but at the cost of having to investigate a lot of complicated underlying effects. Physical models have one interesting advantage: nothing in the data driven model prevents it violating physical laws (e.g., not conserving energy, etc) whereas a physically based model obeys the physical laws by design. This is seldom a problem in practice, but worth keeping in mind.

The situation with data driven techniques is analogous to one
of those American medication adverts
: there’s the big message about how “using a data driven technique can change your life for the better” while the voiceover gabbles out all sorts of small print. The remainder of this post will describe some of the basic principles in that small print.

Preprocessing and feature extraction

There’s a popular misconception that machine learning works well when you simply collect some data and throw it into a machine learning algorithm. In practice that kind of approach often yields a model that is quite poor. Almost all successful machine learning applications are preceded by some form of data preprocessing. Sometimes this is simply rescaling so that different variables have similar magnitudes, are zero centred, etc.

However, there are often steps that are more involved. For example, many machine learning techniques have what are called ‘kernel variants’ which involve (in a way whose details don’t matter here) using a nonlinear mapping from the original data to a new space which is more amenable to the core algorithm. There are various kernels with the right mathematical properties, and frequently the choice of a good kernel is made either by experimentation or knowledge of the physical principles. Here’s an example (from Wikipedia’s entry on the support vector machine) of how a good choice of kernel can convert a not linearly separable dataset into a linearly separable one:



An extreme example of preprocessing is explicitly extracting features from the data. In ML jargon, a feature “boils down” some observed data into a value directly useful. For example, in the work by Ludescher et al that we’ve been looking at, they don’t feed all the daily time series values into their classifier but take the correlation between different points over a year as the basic features to consider. Since the individual days’ temperatures are incredibly noisy and there are so many of them, extracting features from them gives more useful input data. While these extraction functions could theoretically be learned by the ML algorithm, this is quite a complicated function to learn. By explicitly choosing to represent the data using this feature the amount the algorithm has to discover is reduced and hence the likelihood of it finding an excellent model is dramatically increased.

Limited amounts of data for model development

Some of the problems that we describe below would vanish if we had unlimited amounts of data to use for model development. However, in real cases we often have a strictly limited amount of data we can obtain. Consequently we need methodologies to address the issues that arise when data is limited.

Training sets and test sets

The most common way to work with collected data is to split it into a training set and a test set. The training set is used in the process of determining the best model parameters, while the test set—which is not used in any way in determining the those model parameters—is then used to see how effective the model is likely to be on new, unseen data. (The test and training sets need not be equally sized. There are some fitting techniques which need to further subdivide the training set, so that having more training than test data works out best.) This division of data acts to further reduce the effective amount of data used in determining the model parameters.

After we’ve made this split we have to be careful how much of the test data we scrutinise in any detail, since once it has been investigated it can’t meaningfully be used for testing again, although it can still be used for future training. (Examining the test data is often informally known as burning data.) That only applies to detailed inspection however; one common way to develop a model is to look at some training data and then train the model (also known as fitting the model) on that training data. It can then be evaluated on the test data to see how well it does. It’s also then okay to purely mechanically train the model on the test data and evaluate it on the training data to see how “stable” the performance is. (If you get dramatically different scores then your model is probably flaky!) However, once we start to look at precisely why the model failed on the test data—in order to change the form of the model—the test data has now become training data and can’t be used as test data for future variants of that model. (Remember, the real goal is to accurately predict the outputs for new, unseen inputs!)

Random patterns in small sample sets

Suppose we’re modelling a system which has a true probability distribution P . We can’t directly observe this, but we have some samples S obtained from observation of the system and hence come from P . Clearly there are problems if we generate this sample in a way that will bias the area of the distribution we sample from: it wouldn’t be a good idea to get training data featuring heights in the American population by only handing out surveys in the locker rooms of basketball facilities. But if we take care to avoid sampling bias as much as possible, then we can make various kinds of estimates of the distribution that we think S comes from.

Let’s consider the estimate P' implied for S by some particular technique. It would be nice if P = P' , wouldn’t it? And indeed many good estimators have the property that as the size of S tends to infinity P' will tend to P . However, for finite sizes of S , and especially for small sizes, P' may have some spurious detail that’s not present in P .

As a simple illustration of this, my computer has a pseudorandom number generator which generates essentially uniformly distributed random numbers between 0 and 32767. I just asked for 8 numbers and got

2928, 6552, 23979, 1672, 23440, 28451, 3937, 18910.

Note that we’ve got one subset of 4 values (2928, 6552, 1672, 3937) within the interval of length 5012 between 1540 and 6552 and another subset of 3 values (23440, 23979 and 28451) in the interval of length 5012 between 23440 and 28451. For this uniform distribution the expectation of the number of values falling within a given range of that size is about 1.2. Readers will be familiar with how the expectation of a random quantity for a small sample will have a large amount of variation around its value that only reduces as the sample size increases, so this isn’t a surprise. However, it does highlight that even completely unbiased sampling from the true distribution will typically give rise to extra ‘structure’ within the distribution implied by the samples.

For example, here are the results from one way of estimating the probability from the samples:



The green line is the true density while the red curve shows the probability density obtained from the samples, with clearly spurious extra structure.

Generalization

Almost all modelling techniques, while not necessarily estimating an explicit probability distribution from the training samples, can be seen as building functions that are related to that probability distribution.

For example, a ‘thresholding classifier’ for dividing input into two output classes will place the threshold at the optimal point for the distribution implied by the samples. As a consequence, one important aim in building machine learning models is to estimate the features that are present in the true probability distribution while not learning such fine details that they are likely to be spurious features due to the small sample size. If you think about this, it’s a bit counter-intuitive: you deliberately don’t want to perfectly reflect every single pattern in the training data. Indeed, specialising a model too closely to the training data is given the name over-fitting.

This brings us to generalization. Strictly speaking generalization is the ability of a model to work well upon unseen instances of the problem (which may be difficult for a variety of reasons). In practice however one tries hard to get representative training data so that the main issue in generalization is in preventing overfitting, and the main way to do that is—as discussed above—to split the data into a set for training and a set only used for testing.

One factor that’s often related to generalization is regularization, which is the general term for adding constraints to the model to prevent it being too flexible. One particularly useful kind of regularization is sparsity. Sparsity refers to the degree to which a model has empty elements, typically represented as 0 coefficients. It’s often possible to incorporate a prior into the modelling procedure which will encourage the model to be sparse. (Recall that in Bayesian inference the prior represents our initial ideas of how likely various different parameter values are.) There are some cases where we have various detailed priors about sparsity for problem specific reasons. However the more common case is having a ‘general modelling’ belief, based upon experience in doing modelling, that sparser models have a better generalization performance.

As an example of using sparsity promoting priors, we can look at linear regression. For standard regression with E examples of y^{(i)} against P dimensional vectors x^{(i)} we’re considering the total error

\min_{c_1,\dots, c_P} \frac{1}{E}\sum_{i=1}^E (y^{(i)} - \sum_{j=1}^P c_j x^{(i)}_j)^2

while with the l_1 prior we’ve got

\min_{c_1,\dots, c_P} \frac{1}{E} \sum_{i=1}^E (y^{(i)} - \sum_{j=1}^P c_j x^{(i)}_j)^2 + \lambda \sum_{j=1}^P |c_j|

where c_i are the coefficients to be fitted and \lambda is the prior weight. We can see how the prior weight affects the sparsity of the c_i s:



On the x -axis is \lambda while the y -axis is the coefficient value. Each line represents the value of one particular coefficient as \lambda increases. You can see that for very small \lambda – corresponding to a very weak prior – all the weights are non-zero, but as it increases – corresponding to the prior becoming stronger – more and more of them have a value of 0.

There are a couple of other reasons for wanting sparse models. The obvious one is speed of model evaluation, although this is much less significant with modern computing power. A less obvious reason is that one can often only effectively utilise a sparse model, e.g., if you’re attempting to see how the input factors should be physically modified in order to affect the real system in a particular way. In this case we might want a good sparse model rather than an excellent dense model.

Utility functions and decision theory

While there are some situations where a model is sought purely to develop knowledge of the universe, in many cases we are interested in models in order to direct actions. For example, having forewarning of El Niño events would enable all sorts of mitigation actions. However, these actions are costly so they shouldn’t be undertaken when there isn’t an upcoming El Niño. When presented with an unseen input the model can either match the actual output (i.e., be right) or differ from the actual output (i.e., be wrong). While it’s impossible to know in advance if a single output will be right or wrong – if we could tell that we’d be better off using that in our model – from the training data it’s generally possible to estimate the fractions of predictions that will be right and will be wrong in a large number of uses. So we want to link these probabilities with the effects of actions taken in response to model predictions.

We can do this using a utility function and a loss
function
. The utility maps each possible output to a numerical value proportional to the benefit from taking actions when that output was correctly anticipated. The loss maps outputs to a number proportional to the loss from the actions when the output was incorrectly predicted by the model. (There is evidence that human beings often have inconsistent utility and loss functions, but that’s a story for another day…)

There are three common ways the utility and loss functions are used:

• Maximising the expected value of the utility (for the fraction where the prediction is correct) minus the
expected value of the loss (for the fraction where the prediction is incorrect).

• Minimising the expected loss while ensuring that the expected utility is at least some
value

• Maximising the expected utility while ensuring that the expected loss is at most some
value.

Once we’ve chosen which one we want, it’s often possible to actually tune the fitting of the model to optimize with respect to that criterion.

Of course sometimes when building a model we don’t know enough details of how it will be used to get accurate utility and loss functions (or indeed know how it will be used at all).

Inferring a physical model from a machine learning model

It is certainly possible to take a predictive model obtained by machine learning and use it to figure out a physically based model; this is one way of performing data mining. However in practice there are a couple of reasons why it’s necessary to take some care when doing this:

• The variables in the training set may be related by some
non-observed latent variables which may be difficult to reconstruct without knowledge of the physical laws that are in play. (There are machine learning techniques which attempt to reconstruct unknown latent variables but this is a much more difficult problem than estimating known but unobserved latent variables.)

• Machine learning models have a maddening ability to find variables that are predictive due to the way the data was gathered. For example, in a vision system aimed at finding tanks all the images of tanks were taken during one day on a military base when there was accidentally a speck of grime on the camera lens, while all the images of things that weren’t tanks were taken on other days. A neural net cunningly learned that to decide if it was being shown a tank it should look for the shadow from the grime.

• It’s common to have some groups of very highly correlated input variables. In that case a model will generally learn a function which utilises an arbitrary linear combination of the correlated variables and an equally good model would result from using any other linear combination. (This is an example of the statistical problem of ‘identifiability’.) Certain sparsity encouraging priors have the useful property of encouraging the model to select only one representative from a group of correlated variables. However, even in that case it’s still important not to assign too much significance to the particular division of model parameters in groups of correlated variables.

• One can often come up with good machine learning models even when physically important variables haven’t been collected in the training data. A related issue is that if all the training data is collected from a particular subspace factors that aren’t important there won’t be found. For example, if in a collision system to be modelled all data is collected about low speeds the machine learning model won’t learn about relativistic effects that only have a big effect at a substantial fraction of the speed of light.

Conclusions

All of the ideas discussed above are really just ways of making sure that work developing statistical/machine learning models for a real problem is producing meaningful results. As Bob Dylan (almost) sang, “to live outside the physical law, you must be honest; I know you always say that you agree”.


The Stochastic Resonance Program (Part 2)

28 August, 2014

guest post by David Tanzer

Last time we introduced the concept of stochastic resonance. Briefly, it’s a way that noise can amplify a signal, by giving an extra nudge that helps a system receiving that signal make the jump from one state to another. Today we’ll describe a program that demonstrates this concept. But first, check it out:

Stochastic resonance.

No installation required! It runs as a web page which allows you to set the parameters of the model and observe the resulting output signal. It has a responsive behavior, because it runs right in your browser, as javascript.

There are sliders for controlling the amounts of sine wave and noise involved in the mix. As explained in the previous article, when we set the wave to a level not quite sufficient to cause the system to oscillate between states, and we add in the right amount of noise, stochastic resonance should kick in:


The program implements a mathematical model that runs in discrete time. It has two stable states, and is driven by a combination of a sine forcing function and a noise source.

The code builds on top of a library called JSXGraph, which supports function plotting, interactive graphics, and data visualization.

Running the program

If you haven’t already, go try the program. On one plot it shows a sine wave, called the forcing signal, and a chaotic time-series, called the output signal.

There are four sliders, which we’ll call Amplitude, Frequency, Noise and Sample-Path.

• The Amplitude and Frequency sliders control the sine wave. Try them out.

• The output signal depends, in a complex way, on the sine wave. Vary Amplitude and Frequency to see how they affect the output signal.

• The amount of randomization involved in the process is controlled by the Noise slider. Verify this.

• Change the Sample-Path slider to alter the sequence of random numbers that are fed to the process. This will cause a different instance of the process to be displayed.

Now try to get stochastic resonance to kick in…

Going to the source

Time to look at the blueprints. It’s easy.

• Open the model web page. The code is now running in your browser.

• While there, run your browser’s view-source function. For Firefox on the Mac, click Apple-U. For Firefox on the PC, click Ctrl-U.

• You should see the html file for the web page itself.

• See the “script” directives at the head of this file. Each one refers to javascript program on the internet. When the browser sees it, the program is fetched and loaded into the browser’s internal javascript interpreter. Here are the directives:

<script src=
"http://cdn.mathjax.org/mathjax/latest/MathJax.js?config=default">
</script>

<script src=
"http://cdnjs.cloudflare.com/ajax/libs/jsxgraph/0.93/jsxgraphcore.js">
</script>

<script src="./StochasticResonanceEuler.js"></script>

<script src="./normals.js"></script>

The first one loads MathJax, which is a formula-rendering engine. Next comes JSXGraph, a library that provides support for plotting and interactive graphics. Next, StochchasticResonanceEuler.js is the main code for the model, and finally, normals.js provides random numbers.

• In the source window, click on the link for StochasticResonanceEuler.js — and you’ve reached the source!

Anatomy of the program

The program implements a stochastic difference equation, which defines the changes in the output signal as a function of its current value and a random noise value.

It consists of the following components:

1. Interactive controls to set parameters

2. Plot of the forcing signal

3. Plot of the output signal

4. A function that defines a particular SDE

5. A simulation loop, which renders the output signal.

The program contains seven functions. The top-level function is initCharts. It dispatches to initControls, which builds the sliders, and initSrBoard, which builds the curve objects for the forcing function and the output signal (called “position curve” in the program). Each curve object is assigned a function that computes the (x,t) values for the time series, which gets called whenever the input parameters change. The function that is assigned to the forcing curve computes the sine wave, and reads the amplitude and frequency values from the sliders.

The calculation method for the output signal is set to the function mkSrPlot, which performs the simulation. It begins by defining a function for the deterministic part of the derivative:

deriv = Deriv(t,x) = SineCurve(t) + BiStable(x),

Then it constructs a “stepper” function, through the call Euler(deriv, tStep). A stepper function maps the current point (t,x) and a noise sample to the next point (t’,x’). The Euler stepper maps

((t,x), noiseSample)

to

(t + tStep, x + tStep * Deriv(t,x) + noiseSample).

The simulation loop is then performed by the function sdeLoop, which is given:

• The stepper function

• The noise amplitude (“dither”)

• The initial point (t0,x0)

• A randomization offset

• The number of points to generate

The current point is initialized to (t0,x0), and then the stepper is repeatedly applied to the current point and the current noise sample. The output returned is the sequence of (t,x) values.

The noise samples are normally distributed random numbers stored in an array. They get scaled by the noise amplitude when they are used. The array contains more values than are needed. By changing the starting point in the array, different instances of the process are obtained.

Making your own version of the program

Now let’s tweak the program to do new things.

First let’s make a local copy of the program on your local machine, and get it to run there. Make a directory, say /Users/macbookpro/stochres. Open the html file in the view source window. Paste it into the file /Users/macbookpro/stochres/stochres.html. Next, in the view source window, click on the link to StochasticResonanceEuler.js. Paste the text into /Users/macbookpro/stochres/StochasticResonanceEuler.js.

Now point your browser to the file, with the URL file:///Users/macbookpro/stochres/stochres.html. To prove that you’re really executing the local copy, make a minor edit to the html text, and check that it shows up when you reload the page. Then make a minor edit to StochasticResonanceEuler.js, say by changing the label text on the slider from “forcing function” to “forcing signal.”

Programming exercises

Now let’s get warmed up with some bite-sized programming exercises.

1. Change the color of the sine wave.

2. Change the exponent in the bistable polynomial to values other than 2, to see how this affects the output.

3. Add an integer-valued slider to control this exponent.

4. Modify the program to perform two runs of the process, and show the output signals in different colors.

5. Modify it to perform ten runs, and change the output signal to display the point-wise average of these ten runs.

6. Add an input slider to control the number of runs.

7. Add another plot, which shows the standard deviation of the output signals, at each point in time.

8. Replace the precomputed array of normally distributed random numbers with a run-time computation that uses a random number generator. Use the Sample-Path slider to seed the random number generator.

9. When the sliders are moved, explain the flow of events that causes the recalculation to take place.

A small research project

What is the impact of the frequency of the forcing signal on its transmission through stochastic resonance?

• Make a hypothesis about the relationship.

• Check your hypothesis by varying the Frequency slider.

• Write a function to measure the strength of the output signal at the forcing frequency. Let sinwave be a discretely sampled sine wave at the forcing frequency, and coswave be a discretely sampled cosine wave. Let sindot = the dot product of sinwave and the output signal, and similarly for cosdot. Then the power measure is sindot2 + cosdot2.

• Modify the program to perform N trials at each frequency over some specified range of frequency, and measure the average power over all the N trials. Plot the power as a function of frequency.

• The above plot required you to fix a wave amplitude and noise level. Choose five different noise levels, and plot the five curves in one figure. Choose your noise levels in order to explore the range of qualitative behaviors.

• Produce several versions of this five-curve plot, one for each sine amplitude. Again, choose your amplitudes in order to explore the range of qualitative behaviors.


Information Aversion

22 August, 2014

 

Why do ostriches stick their heads under the sand when they’re scared?

They don’t. So why do people say they do? A Roman named Pliny the Elder might be partially to blame. He wrote that ostriches “imagine, when they have thrust their head and neck into a bush, that the whole of their body is concealed.”

That would be silly—birds aren’t that dumb. But people will actually pay to avoid learning unpleasant facts. It seems irrational to avoid information that could be useful. But people do it. It’s called information aversion.

Here’s a new experiment on information aversion:

In order to gauge how information aversion affects health care, one group of researchers decided to look at how college students react to being tested for a sexually transmitted disease.

That’s a subject a lot of students worry about, according to Josh Tasoff, an economist at Claremont Graduate University who led the study along with Ananda Ganguly, an associate professor of accounting at Claremont McKenna College.

The students were told they could get tested for the herpes simplex virus. It’s a common disease that spreads via contact. And it has two forms: HSV1 and HSV2.

The type 1 herpes virus produces cold sores. It’s unpleasant, but not as unpleasant as type 2, which targets the genitals. Ganguly says the college students were given information — graphic information — that made it clear which kind of HSV was worse.

“There were pictures of male and female genitalia with HSV2, guaranteed to kind of make them really not want to have the disease,” Ganguly says.

Once the students understood what herpes does, they were told a blood test could find out if they had either form of the virus.

Now, in previous studies on information aversion it wasn’t always clear why people declined information. So Tasoff and Ganguly designed the experiment to eliminate every extraneous reason someone might decline to get information.

First, they wanted to make sure that students weren’t declining the test because they didn’t want to have their blood drawn. Ganguly came up with a way to fix that: All of the students would have to get their blood drawn. If a student chose not to get tested, “we would draw 10 cc of their blood and in front of them have them pour it down the sink,” Ganguly says.

The researchers also assured the students that if they elected to get the blood tested for HSV1 and HSV2, they would receive the results confidentially.

And to make triply sure that volunteers who said they didn’t want the test were declining it to avoid the information, the researchers added one final catch. Those who didn’t want to know if they had a sexually transmitted disease had to pay $10 to not have their blood tested.

So what did the students choose? Quite a few declined a test.

And while only 5 percent avoided the HSV1 test, three times as many avoided testing for the nastier form of herpes.

For those who didn’t want to know, the most common explanation was that they felt the results might cause them unnecessary stress or anxiety.

Let’s try extrapolating from this. Global warming is pretty scary. What would people do to avoid learning more about it? You can’t exactly pay scientists to not tell you about it. But you can do lots of other things: not listen to them, pay people to contradict what they’re saying, and so on. And guess what? People do all these things.

So, don’t expect that scaring people about global warming will make them take action. If a problem seems scary and hard to solve, many people will just avoid thinking about it.

Maybe a better approach is to tell people things they can do about global warming. Even if these things aren’t big enough to solve the problem, they can keep people engaged.

There’s a tricky issue here. I don’t want people to think turning off the lights when they leave the room is enough to stop global warming. That’s a dangerous form of complacency. But it’s even worse if they decide global warming is such a big problem that there’s no point in doing anything about it.

There are also lots of subtleties worth exploring in further studies. What, exactly, are the situations where people seek to avoid unpleasant information? What are the situations where they will accept it? This is something we need to know.

The quote is from here:

• Shankar Vedantham, Why we think ignorance Is bliss, even when It hurts our health, Morning Edition, National Public Radio, 28 July 2014.

Here’s the actual study:

• Ananda Ganguly and Joshua Tasoff, Fantasy and dread: the demand for information and the consumption utility of the future.

Abstract. Understanding the properties of intrinsic information preference is important for predicting behavior in many domains including finance and health. We present evidence that intrinsic demand for information about the future is increasing in expected future consumption utility. In the first experiment subjects may resolve a lottery now or later. The information is useless for decision making but the larger the reward, the more likely subjects are to pay to resolve the lottery early. In the second experiment subjects may pay to avoid being tested for HSV-1 and the more highly feared HSV-2. Subjects are three times more likely to avoid testing for HSV-2, suggesting that more aversive outcomes lead to more information avoidance. We also find that intrinsic information demand is negatively correlated with positive affect and ambiguity aversion.

Here’s an attempt by economists to explain information aversion:

• Marianne Andries and Valentin Haddad, Information aversion, 27 February 2014.

Abstract. We propose a theory of inattention solely based on preferences, absent any cognitive limitations and external costs of acquiring information. Under disappointment aversion, information decisions and risk attitude are intertwined, and agents are intrinsically information averse. We illustrate this link between attitude towards risk and information in a standard portfolio problem, in which agents balance the costs, endogenous in our framework, and benefits of information. We show agents never choose to receive information continuously in a diffusive environment: they optimally acquire information at infrequent intervals only. We highlight a novel channel through which the optimal frequency of information acquisition decreases when risk increases, consistent with empirical evidence. Our framework accommodates a broad range of applications, suggesting our approach can explain many observed features of decision under uncertainty.

The photo, probably fake, is from here.


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