The major problem — one of the major problems, for there are several — one of the many major problems with governing people is that of whom you get to do it; or rather of who manages to get people to let them do it to them.

To summarize: it is a well known fact that those people who most want to rule people are, ipso facto, those least suited to do it. To summarize the summary: anyone who is capable of getting themselves made President should on no account be allowed to do the job. To summarize the summary of the summary: people are a problem.

Douglas Adams, the pre-eminent social and political philosopher of our times.  Right behind Monty Python.  Then probably Jon Stewart.  With Friedrich Hayek and John Rawls taking a joint and distant fourth.

But Adams has a point.  People are the problem.  People disagree, for various and manifold reasons.  That disagreement is a problem, since it prevents us from fixing problems, and moving in whatever direction the body politic believes is good, given a strong following.

And now, in the United States, there are 350 million of us, and growing.  Do you know what the probability of us all agreeing is?

There are complicated stochastic models — interacting particle systems — which describe the full probability distribution of any combination of pairwise agreement statistics for this population (voter and contact models, see works by Thomas Liggett and Rick Durrett, in particular).  In such models, there are cases where the population will eventually reach consensus.  But the time required for the population to reach consensus is astronomically increasing with the number of people involved.  With hundreds of millions, we are guaranteed that no process which involves people talking to each other (this simplfies our exact situation, but….) will come to consensus in a population this size before the sun burns out, on average.  If we’re lucky — we end up with periods of metastability where we hover in a bounded region of state space before we wander off and “change” into something new.  When we look back, we see a “historical progression” but all it really consists of is the cumulative history of how we’ve agreed and disagreed.

Granted, this is a drastically simplified model.  In reality, we live in societies which are much more like the Potts model, or specifically, the q-state threshold Potts model described by Axelrod in his cultural polarization and cohesion simulations in the late 1990′s.  Their behavior is roughly similar at a macroscale, however, and consensus happens for a small range of parameters but a large part of the state space is coexistence of diversity, with endless wandering through the state space, especially near critical values.

In terms of political philosophy, what this means is that Montesquieu was correct with his “small republic” hypothesis, in empirical terms.  Consensus, and thus harmony on most aspects of social life, is possible with a small population, or with small numbers of attributes that define us as “us.”  As population rises, and the richness of what divides “us” from “them” rises in the Potts model, the more time we spend wandering through inconclusive regions of the state space, where we have lots of change and no stable customs, etc.

This means Madison might be wrong about his “big republic” hypothesis, at least in terms of the classical portrayal of these two thinkers and their relation to classical republican ideals.  But as we know from modern work on first and second-order social punishment, group formation, social network structure, green-beard models, and similar ways of creating ways out of the prisoner’s dilemma, we have ways of making “many overlapping small republics” out of  ”one big republic,” which means if we figure out a better way to blend our opinions — not the old state’s rights divisions, but some new way of slicing and dicing our diversity for purposes of developing a working majority, we have a chance of managing this big Madisonian republic while giving everyone the feeling of involved, empowered inclusion that really sits behind our concepts of citizenship and liberty.

And yes, this really was triggered by Douglas Adams.

Happy Towel Day!

Jeez Louise, this is easier and with much richer functionality. On Typepad, I tried a bunch of CSS-based hacks to get decent footnotes on my posts, for the more academically oriented stuff, and eventually trashed all of them and did ugly manual ASCII footnotes in parentheses and then typed notes in order at the bottom of the post.¹ But the hardest part about doing footnotes in WordPress was deciding which of several supported plugins would look best with my design.². The great thing, is I can change my mind and start using a different plugin, without editing all my old posts.³

And I suspect I can use the Chrome Developer tools and even change the typography of the footnotes, given CSS tweaks.⁴

And studying the full spatial behavior of stochastic processes (including evolutionary theory, in its many guises), especially when interaction and fitness are relative to a complex network of contacts or relationships, is hard. Usually so hard, that we don’t have analytic models for the full behavior of sets of stochastic processes operating on complex networks, or interacting in complex ways. We resort to simulation since the models we can solve are very simple, and few. And we seek guidance for the “average” behavior — the nonspatial global behavior of a model — in mean-field approximations. We temporarily ignore fluctuations, write deterministic mean-field equations for the dynamics, analyze those, and then add fluctuations back, in the form of simple white noise. We take the deterministic mean-field equations and derive pair-approximations or moment closures, and analyze at least the summary statistics for correlations between classes or traits we’re tracking, since we can’t analyze much else spatially. We reduce complex epidemic diffusion models to percolation problems. But mostly, we simulate.

And we do this, and let the mathematicians like Thomas Liggett and Rick Durrett and their students attempt to extend the realm of analytic solutions, because it turns out dynamical systems act differently when interactions are local than when interactions are global. It’s universal, wherever we look. Prisoner’s dilemma models give different answers concerning optimal strategies when the model is well-mixed, and when individuals can only interact with local “neighbors.” Cooperation flourishes when interaction (and dependency) is local, and fade when groups get large and impersonal. Epidemics spread through populations differently when everyone is in mutual contact, and when individuals have sparse and clustered social connections, encountering only subsets of the community. Ideas and cultural information flow differently through populations, depending upon structure. Genes are subject to different selective forces, again depending upon structure.

And it turns out, that even at the level of the molecular chemistry of proteins within our cells. This week Koishi Takahashi and colleagues discovered that when you compare detailed molecular models, between standard mean-field chemical rate equations and a full spatially explicit molecular simulation, that spatiotemporal correlations between molecules matter. A protein kinase cascade, called MAPK, is widespread in eukaryotic organisms, and it turns out that spatial association between enzyme binding sites changes the rate and dynamics of the whole process, which explains why enzymes might release ADP slowly to dampen and stabilize the cascade against the instabilities caused by spatial correlations introducing rate variations.

The details don’t really matter here, but the point is, even down to the molecular level, the dynamics of biological and evolutionary processes are inherently spatiotemporal. Mean-field models are useful for theory building and giving us null models against which the behavior of spatiotemporal models can be compared, and of course there are empirical situations which can be well approximated by mean-field dynamics. But it turns out that mean-field analysis doesn’t tell us the full picture; it’s not just a matter of not having good predictions about spatial distributions in empirical cases, although that’s important. The real point is that mean-field models simply cannot explain some of the phenomena we see in the real world.

And that makes the study of complex spatiotemporal processes worthwhile, despite the fact that it’s hard, and that we lack the ability to even formulate the full dynamics of such systems in many cases. And despite the fact that we have to get good at simulating complex systems and analyzing the behavior of computational models.

In a previous project on signaling theory, I looked at some of the newer literature on coevolutionary or “adaptive” network models. A coevolutionary network model is a dynamic process (for example, an evolutionary game theory model) whose interactions are localized to the structure of a mathematical graph or network. The network topology thus exerts an influence on the solution space of the game, and thus the outcomes which occur for any particular state of the population. In addition, the results of each round of the game have an effect upon the edges and nodes of the network itself, causing “rewiring” of the network and thus changes in the interaction between individuals for the next round. In the case of the costly signaling theory model I was exploring, the setup looks like this:

So the difficulty with standard dynamical models (i.e., ODE models) of dynamical processes on graphs, is that it becomes difficult to do anything but a modified mean-field model, with constraints so that vertices are degree uncorrelated. With adaptive network models and game dynamics, nearly all the literature is reliant on numerical simulation (see Gross and Blasius’s 2007 and 2008 reviews, links coming soon). No real analytical models.

I pretty much bought the notion that correlations and too many degrees of freedom in the network would kill any ability to analyze an adaptive network model analytically. Until I started into the spin glass literature.

Spin glasses are examples of disordered statistical systems, where in addition to dynamical variables which evolve, there are one or more sources of disorder, often structural. An easy example is a standard Ising spin system (or spatial two-allele genetic system with neutral fitness, doing a Moran model), but instead of the Hamiltonian containing only spin configurations and a global coupling constant, the coupling constants vary spatially, and are random variables themselves.

Sound familiar? Coupling constants are the links in the network. If they’re spatially disordered, we have a random graph, not a lattice or mean-field model. If the coupling constants evolve along with the dynamic variables, we have annealed disorder. If the coupling constants are random for a given instantiation of the dynamics, but randomly chosen from an ensemble distribution, we have quenched disorder.

Given what I’d read in the adaptive network literature, the first situation (annealed disorder) sounded harder to solve than the second (quenched disorder). In other words, the evolving network was harder than the static network.

But my reading in the spin glass literature seems to indicate exactly the opposite. We have fewer methods which yield good results of complex systems with quenched disorder, than annealed.

This makes me wonder — conjecture even — that it should be possible to study and solve coevolutionary network models more easily than static models of network dynamics. There’s probably a couple of hundred caveats and exceptions here, but if it’s true, that’s great. Coevolutionary models between contact network and game dynamics are what we need to study as social scientists — models with static networks were typically simplifications of the social phenomena we’re trying to explain.

Hmm…need to read more on spin glasses; the complexity of the dynamics and models make it difficult stuff, but potentially rewarding. I always wondered why the Santa Fe Institute guys kept on about spin glasses….

In population genetics and cultural transmission models, we often see equations which predict the evolution of a quantity over time, given parameter values and functions which describe rates of change. In other words, models for the evolution of trait frequencies tend to be difference or differential equations, depending upon whether the population is assumed to evolve continuously (overlapping generations) or discretely (as in the Wright-Fisher model). Here’s a simple example:

The above model will be most familiar to epidemiologists (and some economists), since it is a deterministic SIS epidemic model. It describes the diffusion of a single pathogen through a population, where “I” indicates the density of “infected” individuals. Not explicitly mentioned in the model is the fact that individuals begin as “S” or “susceptible,” and are infected by interaction with a previously infected individual. The final “S” indicates that individuals are infected for some period, and then become susceptible again. The greek letter beta is a parameter, indicating the transmission rate of the pathogen (per encounter); the greek letter gamma denotes the rate at which infected individuals lose the pathogen and become susceptible again.

Another common interpretation of the SIS model is unbiased social learning of a single cultural trait, where individuals also “forget” or “drop” a variant after some period of use. Alternatively, “forgetting” a trait can also simply mean that they adopt a different, unspecified cultural variant, since we are tracking only a single variant’s frequency here.

Immediately apparent in the above model is that none of the terms refer to interactions between any specific individuals. The model is written, instead, in terms of the density of infected individuals and the rate parameters. The first term is the rate at which infections increase in the population: every infective event requires a susceptible and an infected individual, so the rate of new infections is proportional to the balance between susceptibles and infectives, times the probability of transmission (beta). The second term is the rate at which infections decrease in the population: simply the density of infected individuals times the “recovery” rate (gamma). The long-term prevalence of the pathogen or variant within the population is obtained by solving for the steady-state rate (set the left-hand side to zero and solve).

A stochastic version of this mean-field model differ only in adding noise to the system due to accidents of sampling interactions between infectives and susceptibles. Whether represented fully as a Markov chain or written as a diffusion approximation, the resulting mean-field model has the same qualitative results — the process converges to a Gaussian distribution of infected individuals where the mean of the distribution is the solution to the deterministic ODE model given above, and a variance related to the population size and equilibrium number of infected individuals.

I described this model in some detail so that its structure is clear. On the face of it, there is no individual interaction here, no “microscopic dynamics” which lead to macroscopic observables. The mean-field model is written entirely in terms of the macroscopic observables. How do we get from individual interactions to this type of global, “averaged” description? How does a mean-field description arise from individual behavior?

We begin by setting up the dynamics of individual interactions. For example, in the Moran process of population genetics, transmission events proceed in continuous time. At some rate (usually a Poisson process), one individual is selected to update its state, and we want the probability of a state change to conform to the desired SIS model. We do so as follows. If the focal individual is already infected, then the probability of “recovering” and becoming susceptible again is simply the recovery rate (if we assume that recovery rates are Poisson distributed). If the focal individual is susceptible, they become infected with probability proportional to the transmission rate multiplied by the frequency of infected individuals they are in contact with. Since the Moran process proceeds at continuous time, the probability of two individuals updating in the same infinitesimal interval dt is small enough to ignore (technically, it is O(dt^2), meaning that as we shrink the time interval, the probability of > 1 event goes to zero much faster than the probability of a single event).

If, during each elemental step, each individual encounters a subset of the population (as would be the case in a real population), then the relevant probability for infection is calculated with reference to that subset. Given the stochastic nature of the Moran process, this means that each individual will face a slightly different prevalence of infectives in their subset, and thus have a different probability of infection. In other words, there will be local variation in the “neighbor infection rate.”

Since the only thing that matters (in this model) is the proportion of neighbors that are infected, we can model the neighbor infection rate as a field, to which individuals are exposed. The local strength of the field is simply the local density of infected individuals. In the full model, this field varies over time at each location within the population. The smaller the “neighbor” subset (relative to the whole population) we consider, the more spatial and temporal variation in this field. And the more difficult the challenge of characterizing the full variation in the field’s dynamics.

Conversely, the larger we make the subset with which each individual interacts, the less variable the field will be over space. As the size of the interacting subsets approaches the size of the entire population, the field simply becomes the average density of infected individuals in the population. Each individual thus interacts with a field which is the population average — hence the name “mean-field” approximation. [2]

In arriving at the mean-field model in this way, it becomes obvious how we are discarding information on variation along the way. This may be fully appropriate, especially for laboratory experiments in transmission where the experimental design has everyone interacting, or interacting with a suitably large sample of others. Or it may be appropriate for studying transmission within small social groups in living populations (although even here there will be major variation in the strength of interaction between individuals even in a small village). But when we study transmission over regional scales, we should not immediately assume that individuals equally interacted over the entire region. Everything we know about contemporary and archaeological cultures tells us otherwise.

Nearly all of the cultural transmission models in the anthropological literature today are mean-field models, including nearly all of my own previous work (apart from some spatial simulation models). This doesn’t mean that past results are wrong, merely that they discard information that can help us understand the spatial variation we see in cultural evolution.

Ultimately, my point in discussing how mean-field models arise and are structured is to understand how best to model transmission without mean-field interaction. In other words, constructing and analyzing models where individuals interact with small subsets of the total population, and thus generate spatial structure and variation. There are several methods for proceeding, and some history of doing so in epidemic and economic models of information diffusion, and a long history of spatial genetics models, so we don’t have to reinvent the wheel so much as we need to adapt these methods to our specific needs in archaeology. In upcoming posts I review some of this work and discuss more of the issues in adapting it to our needs.

NOTES:

[1] One can easily argue that spatial or non-mean-field models will be useful in other disciplines and in business contexts, but for current purposes I’m focusing on the requirements of disciplines which seek to explain a fossil record of human cultural evolution, not living populations or controlled experiments. Archaeology leads the exploration of fossil human cultural variation, so it’s a good disciplinary shorthand for historical, “deep time” empirical studies rather than contemporary cases.

[2] This description, of passing from local interaction to a limit of well-mixed, full population interaction, is also relevant to evolutionary game theory. When we examine the Nash equilibria and ESS solutions of a normal-form game, we assume random (or well-mixed) interaction within a population. The dynamics of such interactions are described by the replicator equation: a mean-field model of a game. Spatial evolutionary game theory, on the other hand, describes models where we localize interaction among smaller subsets of the population and examine the variation in equilibria and basins of attraction.

My premise, in this post, is that the generic structure of archaeological data places a distinctive set of requirements on transmission models meant for studying cultural transmission over long time scales. My goal is to describe why this might be, and what implications it carries for archaeologists and other historical studies of cultural transmission. In particular, I want to make the case that we need to be moving beyond well-mixed or “mean-field” mathematical models for cultural transmission when we want such models to be useful in explaining quantitative data about past cultural practices and artifact traditions.In archaeology, we typically cannot observe individual events of transmission: cases where individuals imitate one another, or when information is intentionally passed in verbal or written form. Over longer time scales, and larger spatial scales, what we can study instead is the history of the information itself. How it spreads through a population, how often new innovations occur, how long on average they persist, how much variation (say in a specific way of performing a task) is maintained in a population, and so on.

What we can observe — and this is often a different set of concepts from the theoretical quantities that we study — is the relative frequency of cultural information, measured by counting occurrences in some location, and at some point in time. There’s a subtlety here that will become quite important as I go through future posts and introduce some of my ongoing research. The act of counting is very different than measuring in some ways. When we count things, it doesn’t happen at a “point” in space or time. We always choose some chunk of both space and time in which to count.

If we are doing laboratory experiments, watching subjects imitate each other or do a “transmission chain” experiment, the block of space and time might feel small enough to think of it as a “point” in space and time, but of course it’s not really. We’re counting the relative frequency of classes of information within the testing facility, and perhaps over the course of an hour or day.

When our counting units are this tightly constrained in space and time, we often don’t need to make our mathematical models of social learning and transmission explicitly reference variation in space, we can just focus on what happens over time, perhaps by repeating our counting every day, or for several iterations of the experiment in a row. When we build a mathematical model of a social learning process — for example, prestige-biased transmission — we can develop a model based upon ordinary difference or differential equations. If we are tracking two alternative classes (or “traits”, but I’ll return in a future post to unpack this fairly fuzzy term), then a single differential equation will do (since the relative frequencies sum to 1.0, we only need to solve for the equation of motion of one of the traits, because the other equation of motion will be its mirror image). In situations where we study a set of alternatives, we typically set up a system of coupled differential equations, the solution of each being the equation of motion for a single trait out of the ensemble of alternatives.

If this sounds like the basic structure of most theoretical population genetics, that’s because it is. From Wright and Fisher to contemporary research, population geneticists most frequently study models constructed within “well-mixed” or relatively structureless populations. There are exceptions, of course. There is a rich history of spatial population genetics, beginning with Wright, and continuing through the work of Gustave Malecot, Kimura, and down to contemporary research by Slatkin, Epperson, and others.

We don’t have a comparable tradition of “spatial cultural transmission” modeling, however. And I’m arguing here that it’s high time we developed such a tradition, especially for archaeological use. Archaeologists confront a record of human history devoid of actual behavior — we confront physical objects, in a geological and spatial context. We infer (or “measure”) their position in time through the use of various dating methods, and understanding of the context of their discovery, and we directly measure fine- and coarse-grained spatial patterns in their discovery locations. We study variation in the shape, materials, design, and engineering properties of artifacts. In other words, our data consist of three axes: space, time, and form (as pointed out by Gordon Willey half a century ago).

Applying a cultural transmission model to archaeological data is fundamentally unlike applying one to observations of social learning in a living population, or in a laboratory experiment. In particular, if we do not model the population-level consequences of social learning over both time and space, then we discard much of the information we have available for distinguishing between models and hypotheses. We try to sit, effectively, on a two-legged stool. The most likely consequence is that most of our models end up giving us very similar temporal signatures, and thus can’t be discriminated in real data sets.

What does it mean to model the population consequences of transmission over both time and space?

Primarily it means dropping the mean-field approximation. Said differently, we move away from well-mixed models and explicitly model the spatial or social network distance between individuals, and analyze the population-level consequnces, and archaeological correlates, of transmission on these explicit population structures.

In practical terms, this means that transmission is modeled as being essentially local; individuals only talk to those with whom they have a social network connection, and cultural information diffuses across the network. There is a large literature attempting to model diffusion processes on social networks, which will help. Most of this literature is from economics and epidemiology, with some in statistical physics. The actual “transmission” models are fairly simple, and are mostly unbiased. So we’ll need to inject some anthropological content and modeling into the overall structures the models provide.

Analyzing diffusion models on social networks isn’t easy, even when the modes of transmission are simple. If we track the frequency of traits for each individual (say, out of a population of N) in the network, we simply end up with N coupled differential equations, and far more equations than unknowns, typically. Even if the system of ODE’s can be solved, the answers are idiosyncratic, depending upon the initial conditions. We’re after repeatable patterns here, summary statistics, not idiosyncratic solutions.

At the other extreme, we could pursue a “modified mean-field” approximation, say by classifying nodes in the network by their degree (or number of social network connections), and writing a differential equation for each degree class, and suppress other positional or graph-theoretic variation between nodes as long as they possess the same number of network edges. This approach is widely employed, following Pastor-Satorras and Vespigiani, Lopez-Pintado, and others.

We can also take a state-space correlation view, albeit a non-spatial correlation perspective, and use pair approximation to track the frequencies of trait associations among vertices, rather than trait frequencies themselves. With some effort, this approach can be extended to track the frequencies of multi-vertex “motifs” in addition to pairs.

And finally, there is a more complex approach, which treats the interacting individuals and their state as a single model — an interacting particle system — and uses the techniques of statistical physics to study the dynamic behavior of an ensemble of populations as they evolve under a transmission rule. This approach is rarely amenable, for rules and populations of any realistic complexity, to analytical solution. Instead, the statistical and spatial properties of a model’s evolutionary dynamics are derived from extensive numerical and monte-carlo simulation.

In the posts that follow, I will be examining each of these approaches, and the original mean-field approach in more detail, and charting a path forward for cultural transmission research to move beyond mean-field approximations and well-mixed models. I will be relying upon the literature in epidemic diffusion, the diffusion of innovations, an explosion of literature on statistical mechanics, and the attempts by mathematical probabilists to give rigorous results for the study of stochastic interacting systems, spin glasses and other models of disordered systems, and spatial stochastic models.

I’ve had the Nook for a day or so, long enough to load a large batch of PDF documents on it, download a book from B&N, and run the device through its paces.  Here are some initial thoughts.

The packaging was great – well designed both functionally and aesthetically.  Perhaps a bit nicer than Amazon, but also a lot more wasteful than Amazon’s, with a hard shell transparent polycarb box.  I gave it awhile plugged in, but no real indicators of when it was charged and ready.  Eventually I got it to boot after pushing the power button a few dozen times while it was charging.

Unlike the Kindle, the Nook did not come already set up and registered to me; I had to register the device with my Barnes & Noble account.  This was easy but it’s interesting how the little things matter; I remember my delight at taking the Kindle out of its packaging and having it boot up to show me my name and all ready to buy a book and download it.  The Nook really doesn’t take too much longer to set up, but it’s an additional step and there’s no little personal surprise factor.

I loaded both a formatted eBook and a batch of academic journal articles, in PDF format, and tested out the device.  The goal is to see whether the Nook is useful both for pleasure reading, which nearly always involves formatted eBooks, as well as reading journals with complex content.  Most of the journal articles had embedded graphics, tables, but most especially complex mathematics.

The formatted eBook looks great, and there are no problems with line wrapping.  However, some PDF eBooks I looked at do have line wrap issues, or scaling issues, and have long lines interspersed with very short lines, and that’s incredibly irritating to read.  But if you mostly read formatted eBook content, bought from Barnes and Noble or another source, the Nook looks good.

Where things fall apart is trying to read arbitrary journal articles in PDF on the Nook.  The Nook has a small screen — it’s sized like a second-gen small Kindle, instead of the larger Kindle DX, and so it does  two things with complex PDFs.  First it displays a scaled image of the entire page you’re viewing, but with a standard journal article the type is way too small to actually read.  Then you hit “next,” and that same page is redisplayed in scveral screens, apparently by extracting the text from the PDF.  This involves some of the same wrapping issues previously described, but much worse, this extraction and reformatting process makes complete hash out of any mathematics in the text.  Usually there is 2-3 pages of this extraction and reformatting, and then you get the next “real” page of the PDF, again scaled down and displaying the unreadable whole page, etc.

So basically, the Nook in its current form is pretty useless for complex sideloaded content.  Perhaps if (a) they make a larger screen version, like the Kindle DX, and (b) allow one to turn on and off the “extraction and redisplay” of PDF pages, it would work.  But at the moment I don’t think it’s usable for reading journal articles in the sciences.

There are also small irritations in the UI.  When you select a document from the table of contents display, instead of being taken to the document, you see an almost blank “header” page with the directory path of the document, and a “Read” button down in the keyboard/pointer area.  You have to click “Read” to actually open the document.  This is minor, but wholly unnecessary — it’s like they hired the guys who used to design extraneous Windows dialog boxes.  You sure you want to read this document?  Hell yes I’m sure, and if not, don’t put me two clicks away from changing my mind.

Finally, the device is slow in comparison to the Kindle.  I put just a few dozen PDF files on the Nook, instead of the 300+ I currently have on the Kindle DX, but when the Nook boots up the table of contents is empty.  It approximately 10 seconds to scan the device’s storage and build the table of contents for maybe four dozen files.  The device also boots quite slowly, and when it goes to sleep and wakes up, it actually *reboots* instead of waking up, or at least that’s the behavior I’ve seen.

In general, my first impressions are that Barnes and Noble tried to do a Kindle, and focused on the big stuff to exclusion of detail.  The UI is clunky, the device is slow, and various features (like PDF handling) look like last-minute hacks by the programming team.  I’m not impressed thus far, sadly.

One caveat is that if you only read content off eBook provider websites, such as Barnes and Noble, you’ll probably be fine in terms of basic functionality.  But as a competitor to the Kindle DX, the Nook isn’t going to find a place in my laptop bag anytime soon.

Between February 2004 and March 2009, I wrote a regular blog, where I concentrated mostly on law, politics, and personal topics. When a critical mass of my friends began using Facebook, I slowly stopped writing blog posts as often, and became too busy with my research (and a fundraising project in my adopted hometown) to write much about political topics.

Facebook mostly solves the need to update friends and family about what’s happening in my life, and I probably won’t write much about my daily life here, so join me on Facebook if you want to know what culinary projects I’ve got going, what cocktails I’ve been experimenting with, discussion of interesting wines, and this sort of thing.

For politics and technology, I try to occasionally post essays to a Posterous blog I share with another friend named Mark Madsen. I have to say I think the Posterous blogging platform sucks, but it’s tightly integrated with Facebook, easy to use, and sufficient to the purpose. Though maybe if we blogged a bit more often, I’d argue for moving to WordPress.

So what does that leave? My research, comments on science and whatever I’m reading in the scientific literature, and discussions of mathematics, scientific software, and simulation modeling. Obviously, that’s a specialized audience, so if it’s not your thing, see you on Facebook. Otherwise, I hope you enjoy and read in the months (and hopefully years) to come.

Naturally, this blog is configured to update Facebook and Twitter when I post, and have the usual RSS/Atom feed options (actually, the addition of this paragraph is an attempt to test those links….)