Give the drummer some

James Brown’s funk is tight. On a track like ‘Licking Stick’, the music threatens to break loose at any time, barely contained by Brown and the beat. The little I’ve read about Brown suggests that this is no accident. He was apparently a difficult and demanding band leader, but listen to the result.

On several recordings, James Brown calls to Maceo Parker to take his solo. Oh, man, can Maceo play — the pacing, the expressiveness, the musicality — no wonder he’s gigged with everyone.

On ‘Cold Sweat’, as Maceo is finishing up, Brown asks, should we give the drummer some? Wikipedia says this is the first recording in which Brown does this. This call for a solo highlights the importance of the drummer for the whole enterprise. Maceo can play with the rhythm and Brown can give us all his famous ‘uhs’ and ‘good Gods’ because that drum is keeping things together, keeping it tight.

Now, let’s shift to some economics (sorry, but you knew it was coming). I’m involved in a few projects right now that are mainly modelling projects. We aren’t doing primary research in the sense of going out and collecting data and producing new empirical findings. Instead, we are organising existing information. We are using not only economic data, like price elasticity of demand, but also information from other disciplines, like dose-response functions for medicines or nitrogen leaching rates for different land uses.

It occurred to me that we are the drummers in these projects. We have a particular set of skills — keeping information organised and finding ways of making different types of data fit together. But the value of the drummer isn’t the particular beat they’re laying down. Their value is to provide a groove that the rest of the music can revolve around.

The drums provide a solid structure, and that’s what a good model does. As a result, the rest of the information makes more sense, in the same way that a horn solo makes more sense once the beat is established. A good model also demonstrates which parameters are important or which relationships determine the outcomes, just like a solid beat lets the singer shine.

Sometimes, we modellers even get the spotlight; sometimes, even the drummer gets him some.

Evolutionary fitness of homosexuality

Possibly out of the usual topic areas, but here goes. A preacher in North Carolina in the U.S. has been stirring up the usual anti-gay hatred. I grew up just one state north, so it is all depressingly familiar. I am fairly inured to intolerance because (a) there is so much of it and (b) picking fights with it only makes it stronger.

My first thought really is, haters gonna hate:

One of the arguments is that homosexuality is unnatural: because gays and lesbians won’t procreate, how could the gene be passed down? I built a toy model as an explanation. It didn’t quite work but let me explain the logic.

From an evolutionary perspective, genes want to reproduce themselves. Note that this isn’t about the longevity of an organism. It is about producing offspring who survive to produce offspring. Biologists know that this can be achieved with different strategies. One strategy is to protect and foster a few offspring. Another is to have lots of offspring and hope some survive.

Human genes have gone for the nurture-a-few strategy (in comparison to, say, 13-year cicadas). What if the genes could ensure not two adult guardians but three? That might give the offspring a larger chance of surviving. Maybe a set of genes could sort itself so that some offspring specialised in breeding and others specialised in providing material support for the next generation.

And that is why hereditary homosexuality can be ‘fit’ from an evolutionary perspective: it can provide extra aunts and uncles to protect the littlies. The genetic trick is to get the right proportion of breeders and non-breeders across the generations. That’s what I was working on modelling. I found that the ideal proportion of non-breeding adults was directly proportional to its impact on survival, a fairly trivial result.

So be glad for your gay relatives — they signal your own genetic fitness.

Deleuzian simulation models

I picked up a book a while back from a university bookstore solely for its title: A Deleuzian Century? I have started it a few times but not finished it — it’s that sort of book (or I’m that sort of reader). Foucault suggested that this century would be Deleuzian, that Gilles Deleuze, a French philosopher, would be central to thinking in the 21st century.

I made it through both volumes of Deleuze and Guattari’s Capitalism and Schizophrenia and also read Brian Massumi’s excellent A User’s Guide to Capitalism and Schizophrenia. I admit that I don’t ‘get’ Deleuze. I can read the sentences and paragraphs, I can start picking up concepts like bodies-without-organs and rhizomes, but I am always outside the concepts the same way one cooks from an unfamiliar recipe.

Nevertheless, I am beginning to think that Foucault was right.

One Deleuzian concept that I find utopian and naive is the rhizome. A rhizomatic plant sends stems through the ground, putting roots down and leaves up from new nodes. The structure is nonhierarchical. Deleuze contrasts this type of growth with things like trees, which have a center trunk, a hierarchy of limbs and twigs, and an integrated existence (it can be killed as a whole). He uses the rhizome metaphor to describe new ways of becoming that are lateral and invasive, that don’t depend on hierarchy or permission. It is meant as a liberating metaphor.

I have just been looking at cellular automata models, including Schelling’s model, The Game of Life, and Wolfram’s classification of one-dimensional models. They are discussed in a excellent on-line course by Scott Page. It occurred to me that these are mathematical representations of Deleuzian thought. They are presented as flat models, as full depictions of their worlds from which patterns of organization spontaneously emerge.

Key to both the Deleuzian metaphor and cellular automata model is the idea of ’emergent properties’. Patterns and organisation are thought to occur spontaneously as a result of individual elements just doing what they do. For Deleuze, these are new ways of being — I hesitate to say new ‘identities’, although I think that’s what he’s aiming at. For simulation modellers, it is complex order arising from simplicity, such as Wolfram Rule 110.

Simulation model thinking is rhizomatic thinking. Like John Wheeler’s ‘it from bit’, it considers that the binary on/off can be the basis for all organisation and the rest is just emergent properties. The simple rules of a Wolfram model push themselves through blank grids to establish new patterns, which can repeat themselves with relying on the rest of the pattern.

Simulation models are becoming more important in economic and policy analysis. Stats NZ, for example, recently hosted Martin Spielauer from Statistics Canada to talk about simulation modelling. As these models become more accepted, so will the underlying thinking.

We are becoming Deleuzian without even knowing it.