Dr. Martina Morris (PI) & Dr. Mark Handcock at the Center for Studies in Demography and Ecology of University of Washington are funding a SoNIA-related project contract to integrate dynamic network visualization techniques with the R statnet package developed by Morris, Handcock, et al at the CSDE. The funds come from the NIH grants supporting the Network Modeling Project at the University of Washington (grants R01 HD41877 and R01 DA12831)
The focus is on adapting and developing visualization techniques for dynamic network data. The specific emphasis will be on techniques relevant for understanding:
a) infectious disease transmission (change in state of elements due to a network diffusion process) and
b) the stationary dynamics (model-based addition and removal of nodes/edges) of longitudinal network data and simulation output.
The goal is to develop robust, rigorous and repeatable procedures for visually interpreting time-based network data. We will achieve this by linking existing software components and improving existing techniques to generate animations and export movies from R or other statistical packages into standard formats suitable for use in presentations or websites.
SoNIA is a Java-based package for visualizing dynamic or longitudinal “network” data. By dynamic, we mean that in addition to information about the relations (ties) between various entities (actors, nodes) there is also information about when these relations occur, or at least the relative order in which they occur.
This text was written soon after completing my undergrad thesis. Some things have changed, many themes are still the same
Brief Description of Research Interests——
July 2001
I’ve recently started referring to my area of concentration as “Cultural Mechanics†In the broadest sense, I’m interested in the fundamental parameters underlying the phenomena of cultural transmission and real-world socially mediated information exchange. Obviously there are tremendous number of areas which can fit under such a heading. I’m interested in evolutionary theory and questions about gene-culture co-evolution and the impact of the inclusion of cultural parameters into fitness descriptions. I’m intrigued by work being done on mathematical descriptions of cultural transmission processes.
“Culture … is that complex whole which includes knowledge, belief, art, morals, law custom, and any other capabilities and habits acquired by man as a member of society.”
-Sir Edward Burnett Tylor (Primitive Culture, 1871)
One of the crucial tools of modern evolutionary thinking is the notion that it is not only necessary to think about how a particular trait or relationship might be beneficial to its holder now, but also what circumstances were required for it to have become an adaptive variation in the first place. It is also essential to consider what structural factors must remain present to keep a trait from being removed from a population – is the behavior an evolutionarily stable strategy, or one which is subject to invasion by more “exploitive” traits? In this context, then, what are the biological-fitness enhancing values of “proto-culture” or communication which might have encouraged its emergence in early humans? Clearly this question is closely related to, and dependent upon, the adaptive value of cognition, consciousness, and communication. Is the continued existence of cultural behaviors which seem biologically maladaptive simply an unavoidable consequence of having big brains and jabbering mouths? Or are there group selection benefits? Are cultural behaviors subject to the same constraints of biological fitness, or do they reside in some other selective regime?
Even if the specifics of the processes of coding meaning and information in intentional (or unintentional) communication are not fully understood, some of the results may be visible on a social level. If individuals have exchanged information, there might be a convergence in the meanings they associate with certain signs or actions. There my be an increased tendency to see things from each other’s perspective. Through conversation they establish a common context, a dictionary of shared words, and a collection of common connotations and references which grows with each interaction. In Eco’s terms, the “semantic trees” exchange elements; the possible meanings attached to the symbolic referents they employ become more similar. In a sense, a micro-language is developed, a subculture between the interactants, a set of expectations and conventions. If it were possible to attach some metric to the degree of meaning convergence, this might be a usable measure of information transfer.
Fig. 7 Diagram shows a summary of differences in mean semantic loadings of terms for English (star) and Japanese (circle) speakers. In this spatial representation, emotion terms that are judged as more similar (by a method similar to Osgood’s) are closer to each other than terms that are judged less similar. The dimensions of the diagram are from a Principal Component Analysis. Dimension 1 appears to correspond to what Osgood referred to as the Evaluative Factor (good-bad, pleasant-unpleasant, positive-negative) and Dimension 2 appears related to his Activity Factor (fast-slow, active-passive, excitable-calm). (copied without permission form Romney et. al. 1999)
At the same time that I’ve been doing the literature research for this project, I’ve also been conducting a short term longitudinal study of social and informational networks as they develop among the entering first-year students of the Class of ’04 and the rest of the Bennington College community. I had several reasons for wanting to do this. Throughout this work I have been discussing and suggesting conceptual frameworks for thinking about information in social networks. My hope was that doing this kind of study might give me some real data for comparison – a qualitative check on how well theory actually describes what is going on. Ideally a network study of the campus might give me a baseline idea of the social structure of Bennington which could be built upon in future work. At the very least, attempting to construct and implement a study would teach me a great deal about methodologies and the complicating factors which will inevitably crop up when dealing with theory and data in the real world.
Dr. Martina Morris (PI) & Dr. Mark Handcock at the Center for Studies in Demography and Ecology of University of Washington are funding a SoNIA-related project contract to integrate dynamic network visualization techniques with the R statnet package developed by Morris, Handcock, et al at the CSDE. The funds come from the NIH grants supporting the Network Modeling Project at the University of Washington (grants R01 HD41877 and R01 DA12831)
