# My Year in Publications

This is, apparently, a time for reflection. What have I been up to?

And so this is Korrasmas
Things have been Done
Kuvira is fallen
A new ‘ship just begun

Kor-ra-sa-mi
We all knew it
Kor-ra-sa-mi
now-ow-ow-owwwwwww

Well, other than watching cartoons?

At the very beginning of 2014, I posted a substantial revision of “Eco-Evolutionary Feedback in Host–Pathogen Spatial Dynamics,” which we first put online in 2011 (late in the lonesome October of my most immemorial year, etc.).

In January, Chris Fuchs and I finished up an edited lecture transcript, “Some Negative Remarks on Operational Approaches to Quantum Theory.” My next posting was a solo effort, “SIC-POVMs and Compatibility among Quantum States,” which made for a pretty good follow-on, and picked up a pleasantly decent number of scites.

Then, we stress-tested the arXiv.

By mid-September, Ben Allen, Yaneer Bar-Yam and I had completed “An Information-Theoretic Formalism for Multiscale Structure in Complex Systems,” a work very long in the cooking.

Finally, I rang in December with “Von Neumann was Not a Quantum Bayesian,” which demonstrates conclusively that I can write 24 pages with 107 references in response to one sentence on Wikipedia.

# It’s Good to Laugh

Alleged intellectual Christina Hoff Sommers (I know, I know, it’s bad form to give away the punchline of a joke so early) recently had this to say:

Dear liberals, When you side with today’s 3rd wave intersectional feminism, you are siding with the intellectual equivalent of creationism.

As a liberal feminist whose day job actually is studying evolutionary dynamics, I can only say this:

# Lacking Tonka

Dawkins claims that Hölldobler has “no truck with group selection”. Wilson and Hölldobler (2005) proposes, in the first sentence of its abstract, that “group selection is the strong binding force in eusocial evolution”. Later, Hölldobler (with Reeve) voiced support for the “trait-group selection and individual selection/inclusive fitness models are interconvertible” attitude. Hölldobler’s book with Wilson, The Superorganism: The Beauty, Elegance, and Strangeness of Insect Societies (2008), maintains this tone. Quoting from page 35:

It is important to keep in mind that mathematical gene-selectionist (inclusive fitness) models can be translated into multilevel selection models and vice versa. As Lee Dugatkin, Kern Reeve, and several others have demonstrated, the underlying mathematics is exactly the same; it merely takes the same cake and cuts it at different angles. Personal and kin components are distinguished in inclusive fitness theory; within-group and between-group components are distinguished in group selection theory. One can travel back and forth between these theories with the point of entry chosen according to the problem being addressed.

This is itself a curtailed perspective, whose validity is restricted to a narrow class of implementations of the “multilevel selection” idea. (Yeah, the terminology in this corner of science is rather confused, which doesn’t make talking about it easier.) Regardless, I cannot think of a way in which this can be construed as having “no truck with group selection”. The statement “method A is no better or worse than method B” is a far cry from “method A is worthless and only method B is genuinely scientific”.

If Dawkins has some personal information to which the published record is not privy, that’s fine, but even if that were the case, his statements could not be taken as a fair telling of the story.

EDIT TO ADD (21 November 2014): I forgot this 2010 solo-author piece by Hölldobler, in a perspective printed in Social Behaviour: Genes, Ecology and Evolution (T. Székely et al., eds). Quoting from page 127:

I was, and continue to be, intrigued by the universal observation that wherever social life in groups evolved on this planet, we encounter (with only a few exceptions) a striking correlation: the more tightly organized within-group cooperation and cohesion, the stronger the between-group discrimination and hostility. Ants, again, are excellent model systems for studying the transition from primitive eusocial systems, characterized by considerable within-group reproductive competition and conflict, and poorly developed reciprocal communication and cooperation, and little or no between-group competition, one one side, to the ultimate superorganisms (such as the gigantic colonies of the Atta leafcutter ants) with little or no within-group conflict, pronounced caste systems, elaborate division of labour, complex reciprocal communication, and intense between-group competition, on the other side (Hölldobler & Wilson 2008 [the book quoted above]).

And, a little while later, on p. 130:

In such advanced eusocial organisations the colony effectively becomes a main target of selection […] Selection therefore optimises caste demography, patterns of division of labour and communication systems at the colony level. For example, colonies that employ the most effective recruitment system to retrieve food, or that exhibit the most powerful colony defence against enemies and predators, will be able to raise the largest number of reproductive females and males each year and thus will have the greatest fitness within the population of colonies.

Google Scholar is definitely missing citations to my papers.

The cited-by results for “Some Negative Remarks on Operational Approaches to Quantum Theory” [arXiv:1401.7254] on Google Scholar and on INSPIRE are completely nonoverlapping. Google Scholar can tell that “An Information-Theoretic Formalism for Multiscale Structure in Complex Systems” [arXiv:1409.4708] cites “Eco-Evolutionary Feedback in Host–Pathogen Spatial Dynamics” [arXiv:1110.3845] but not that it cites My Struggles with the Block Universe [arXiv:1405.2390]. Meanwhile, the SAO/NASA Astrophysics Data System catches both.

This would be a really petty thing to complain about, if people didn’t seemingly rely on such metrics.

EDIT TO ADD (17 November 2014): Google Scholar also misses that David Mermin cites MSwtBU in his “Why QBism is not the Copenhagen interpretation and what John Bell might have thought of it” [arXiv:1409.2454]. This maybe has something to do with being worse at detecting citations in footnotes than in endnotes.

# Multiscale Structure via Information Theory

We have scienced:

B. Allen, B. C. Stacey and Y. Bar-Yam, “An Information-Theoretic Formalism for Multiscale Structure in Complex Systems” [arXiv:1409.4708].

We develop a general formalism for representing and understanding structure in complex systems. In our view, structure is the totality of relationships among a system’s components, and these relationships can be quantified using information theory. In the interest of flexibility we allow information to be quantified using any function, including Shannon entropy and Kolmogorov complexity, that satisfies certain fundamental axioms. Using these axioms, we formalize the notion of a dependency among components, and show how a system’s structure is revealed in the amount of information assigned to each dependency. We explore quantitative indices that summarize system structure, providing a new formal basis for the complexity profile and introducing a new index, the “marginal utility of information”. Using simple examples, we show how these indices capture intuitive ideas about structure in a quantitative way. Our formalism also sheds light on a longstanding mystery: that the mutual information of three or more variables can be negative. We discuss applications to complex networks, gene regulation, the kinetic theory of fluids and multiscale cybernetic thermodynamics.

There’s much more to do, but for the moment, let this indicate my mood:

# 10 LINKS 20 GOTO 10

My “Worked Physics Homework Problems” book now stands at 372 pages. If you ever wonder what I do instead of meeting people.

“You’ll get so preoccupied with equations that you forget to eat!” #BadWaysToPromoteScienceToYoungWomen

# Modern Evolutionary Theory Reading List

The following is a selection of interesting papers on the theory of evolutionary dynamics. One issue addressed is that of “levels of selection” in biological evolution. I have tried to arrange them in an order such that the earlier ones provide a good context for the ones listed later.

I’ve met, corresponded with and in a couple cases collaborated with authors of these papers, but I’ve had no input on writing or peer-reviewing any of them.

Last October, a paper I co-authored hit the arXivotubes (1110.3845, to be specific). This was, on reflection, one of the better things which happened to me last October. (It was, as the song sez, a lonesome month in a rather immemorial year.) Since then, more relevant work from other people has appeared. I’m collecting pointers here, most of them to freely available articles.

I read this one a while ago in non-arXiv preprint form, but now it’s on the arXiv. M. Raghib et al. (2011), “A Multiscale maximum entropy moment closure for locally regulated space-time point process models of population dynamics”, Journal of Mathematical Biology 62, 5: 605–53. arXiv:1202.6092 [q-bio].

Abstract: The pervasive presence spatial and size structure in biological populations challenges fundamental assumptions at the heart of continuum models of population dynamics based on mean densities (local or global) only. Individual-based models (IBM’s) were introduced over the last decade in an attempt to overcome this limitation by following explicitly each individual in the population. Although the IBM approach has been quite insightful, the capability to follow each individual usually comes at the expense of analytical tractability, which limits the generality of the statements that can be made. For the specific case of spatial structure in populations of sessile (and identical) organisms, space-time point processes with local regulation seem to cover the middle ground between analytical tractability and a higher degree of biological realism. Continuum approximations of these stochastic processes distill their fundamental properties, but they often result in infinite hierarchies of moment equations. We use the principle of constrained maximum entropy to derive a closure relationship for one such hierarchy truncated at second order using normalization and the product densities of first and second orders as constraints. The resulting `maxent’ closure is similar to the Kirkwood superposition approximation, but it is complemented with previously unknown correction terms that depend on on the area for which third order correlations are irreducible. This region also serves as a validation check, since it can only be found if the assumptions of the closure are met. Comparisons between simulations of the point process, alternative heuristic closures, and the maxent closure show significant improvements in the ability of the maxent closure to predict equilibrium values for mildly aggregated spatial patterns.

In network science, one can study the dynamics of a network — nodes being added or removed, edges being rewired — or the dynamics on the network — spins flipping from up to down in an Ising model, traffic flow along subway routes, an infection spreading through a susceptible population, etc. These have often been studied separately, on the rationale that they occur at different timescales. For example, the traffic load on the different lines of the Boston subway network changes on an hourly basis, but the plans to extend the Green Line into Medford have been deliberated since World War II.

In the past few years, increasing attention has been focused on adaptive networks, in which the dynamics of and the dynamics on can occur at comparable timescales and feed back on one another. Useful references:

# Of Predators and Pomerons

Consider the Lagrangian density

$\mathcal{L} (\tilde{\phi},\phi) = \tilde{\phi}\left((\partial_t + D_A(r_A – \nabla^2)\right)\phi – u\tilde{\phi}(\tilde{\phi} – \phi)\phi + \tau \tilde{\phi}^2\phi^2.$

Particle physicists of the 1970s would recognize this as the Lagrangian for a Reggeon field theory with triple- and quadruple-Pomeron interaction vertices. In the modern literature on theoretical ecology, it encodes the behaviour of a spatially distributed predator-prey system near the predator extinction threshold.

Such is the perplexing unity of mathematical science: formula X appears in widely separated fields A and Z. Sometimes, this is a sign that a common effect is at work in the phenomena of A and those of Z; or, it could just mean that scientists couldn’t think of anything new and kept doing whatever worked the first time. Wisdom lies in knowing which is the case on any particular day.

[Reposted from the archives, in the light of John Baez’s recent writings.]