December 31, 2008

Florida Museum of Natural History: prehistory on film.

This is really cool! Who says that YouTube is contributing to the downfall of civilization (I am not among those people, but whatever)? Have a look at these videos from the UF libraries archive. I used to work here as a Masters student:

This is the dedication of Dickinson Hall, which is now the "old" FLMNH (where they house most of the collections and scientific research today). By contrast, the "new" FLMNH (with the fully-functioning Butterfly rainforest) is out on Museum road near 34th Street (which is just off the lower left edge of the map shown below):

The whole buliding was designed to look (and apparently function, since it dosen't have freight elevators) like a Mayan pyramid nestled into the side of a hill. Notice how the front entrance appears tobe a single story while the plaza in back looks quite expansive with staircases and everything. In the background you can see Carrtram Hall (a portmanteau of Carr and Bartram Halls, home of the Zoology and Botany departments).

If you ever pass by (right on I-75) or visit Gainesville, FL, do yourself a favor and visit this museum. You'll learn a hell of a lot more than you will at Disney, and they have some pretty good collections, especially pertaining to Florida, Carribean, and South American biodiversity/cultural history.

More UF trivia as the opportunity presents itself......

December 23, 2008

Evolution as an Inverse Problem, Part I

A few years back, I was at a conference (SwarmFest '04, if you must know) at which I heard Marco Dorigo (from the Swarm Intelligence community) characterize the collective behavior of social insects (nest building in this case) as an "inverse problem". For those of you unfamiliar, inverse problems are complex problems where the data is well-known, but the model parameters are not.

Definition of an Inverse Problem (Wolfram Mathworld)

Take the construction of Dorigo's anthill problem as an example. We can easily observe the actions of each ant and the interactions between them. This can even be extrapolated to a description of the anthill structure. However, this description is not generalizable to all instances of anthill. The reason for this is that because the structure is emergent, an anthill of a particular morphology can be had using any number of equally-suitable sequences of interactions. In other words, there are many different equally-suitable ways to produce to the observed structure. If we were to attempt a reconstruction of the anthill without our a priori observations, we would fail: for this reason, inverse problems such as these are called ill-posed problems. Other behaviors (such as arm movements) have also been called ill-posed problems.

Why is this relevant to the post?

If the obvious parallels to evolution weren't obvious with the word "reconstruction", consider the following: there are many possible ways
to get to a coherent anthill, just as there are many ways to get to a fit phenotype. A form of convergent evolution called neutral networks, where selection is not extreme and many genotypes are fit enough to provide an adaptive solution, comes to mind here.

Definition of Neutral Networks

In addition, the concept (if not the analytical techniques) of inverse problems could be useful for a better understanding of parameters such as transcriptional regulation, gene action, selection, and even fitness. This is especially important for understanding how these parameters assemble a complex phenotype from a genotype.

In the next installment, I will consider the basic combinatorics of anthills and gene action, and how thismight produce emergent structures with and without "selection".

December 15, 2008

Review of "The Complementary Nature"

Review of: Kelso, J.A.S. and Engstrom, D.A. (2006). The Complementary Nature. MIT Press, Cambridge, MA.
By Bradly Alicea

This is a potentially momentous book. That being said, it is far from a synthesis. From a superficial perspective, it seems more like a long-winded manifesto with nice headshots of famous people. Nevertheless, the core idea is clear; namely that mentally-represented physical phenomena come in "complementary pairs", and that they form an interstitial and heterogeneous continuum between them. There is even a pairings glossary at the end of the book; each set of concepts is modified by a tilde (~) which denotes the link between two discrete states represented by linguistic titles.

The universal tilde designation is my major objection to this approach. The pairs actually seem to come in one of three varieties: binary oppositions, causal pairs, and hierarchical nestings. This enables higher-level mathematical operations, and scalable models to be constructed. Yet pairs of all categories are annotated in much the same way. Think of the tilde as a mathematical operator, which I'm sure was the authors’ ultimate intent given their tone. Following this logic, if pairs come in qualitatively different types, then the authors should have used different operators for each type. It would make the entire enterprise much more straightforward, especially when mapping pairs to a phase space as occurs later in the book.

Conceptual Taxonomy and Functional Information
Binary oppositions (linear~nonlinear) are by far the most straightforward. People tend to be most comfortable the outcome of such pairings, and can most intuitively analyze their outcomes. Consider the physical and mental aspects of hot~cold. Conditions in a physical
system range from hot to cold; indeed, not only is there a linguistic dichotomy, but a physical one as well. Because the mapping between the two is relatively seamless, we can easily quantify "hot" vs. "cold" using both a dichotomous representation coupled to quantitative instruments. It is the pairings that do not fall cleanly into this category that cause potential confusion. For example, causal pairs (reaction~anticipation) and hierarchical nestings (individual~society) might be considered differentiated states in a superficial sense, but treated as such may not map to a formalized phase space well.

One of the most intriguing ideas in the book is the way in which the authors conceptualize functional information. One example from brain science involves the specificity of COMT expression in prefrontal cortex. The initiation of gene expression at certain points in life history relies on the correct environmental conditions; interactions with surrounding proteins lead to specific types of emergent structures and specific phenotypes. No surprise there; classifying such processes as the flow of information is increasingly commonplace. The potential food for thought offered here is that this is part of an emergent process. In general, complex systems use functional information to build complexity. While information is a necessary prerequisite for emergent complexity, it is most powerful when coordinated by concurrent processes.

Treatment of Complexity
Their treatment of emergence (micro~macro) is one of the best I've seen, and is at once
mathematically rigorous and intuitive. They treat "individual" and "collective" as a metastable system (two local minima on opposite sides of a metastable "saddle point"). The system is driven by the values of a few key parameters; these parameters represent the reciprocal forces of downward and upward causation. Instability in these parameters drives the system towards a phase transition; more intuitively, the system climbs out of one stable state to a metastable plateau. At this point, it is free to return to its original state, remain unstable, or change to a new state. Dealing with the effects of causality on the initiation of phase transitions front and center makes for a much cleaner model than many of the other approaches out there.

More formal complex systems models becomes the thrust of this book's second part. Readers not familiar with Kelso's 1995 book "Dynamic Patterns" would do well to go there for a formal mathematical treatment. Once you understand the underlying concepts of coordination dynamics, go back and read "The Complementary Nature" again. Fresh eyes will provide you with a new perspective on the pairings. For example, pairings might be viewed as the boundary conditions of an n-dimensional phase space, or as discrete states in a multistable system. In any event, it is the space between the discrete states that are of interest to the authors. The take home message seems to be that this space is complex, unstable, and potentially fertile ground for the gray areas that humanities, brain science, and complexity scholars alike must understand.