May 15, 2013

Lecture to BEACON Center, Michigan State

On May 17th (Friday) at 3:30pm (EST), I will be giving a lecture entitled "Adventures in Quasi-Evolution" [1] to the BEACON Center [2]. The audience is Thrust Group 1 (Genomes, Networks, and Evolvability).

The first half of my talk will be on computational models of cellular reprogramming (e.g. evolutionary modulus, or the engineering on the remnants of evolutionary and developmental variation). The second half is some emerging work I am doing on the cultural evolution of economic value (e.g. evolutionary through the looking glass, or how evolutionary models [3] may explain current economic puzzles).


[1] "Adventures in Quasi-Evolution". Figshare, doi:10.6084/m9.figshare.701463 (2013). I define quasi-evolution as "changes over time not due to reproductive fitness or generational inheritance".

[2] For those who are unfamiliar, the BEACON Center (an NSF-funded center) is a multi-disciplinary, multi-University group interested in the intersection of biology and engineering with relevance to evolution. Catch the lecture at one of these locations:

Michigan State University: Biomedical and Physical Sciences Building, Room 1441 (BEACON seminar room), 3:30pm.

North Carolina A and T University: McNair Hall, Lecture Room 4, 3:30pm.

University of Idaho: Life Sciences South (LSS), Room 144, 12:30pm

University of Texas, Austin: Service Building (SER), Room 321H, 2:30pm.

University of Washington: UW Hutchinson Cancer Center, Building PAA Room 023D, 12:30pm

[3] The cultural evolutionary models used in my research are called Contextual Geometric Structures (CGSs). Contextual Geometric Structures (CGS) will never play chess well, or perhaps at all. They will never beat Ken Jennings on Jeopardy. That's not the point. They exist as soft or fuzzy (e.g. possibilistic, non-transitive) classifiers that capture (or at least reproduce) the structural features of cultural behavior. This is quite different from the dual inheritance models that are common in studies that focus on the geneaology of traits.

The "structure" of culture has been observed and pondered by many cultural anthropologists, from Claude Levi-Strauss to Pierre Bourdieu. Bourdieu used a construct called the "habitus" to characterize the relationship between individuals in a single generation and cultural structures that exist across multiple generations. CGSs provide a measure of computational precision (e.g. a kernel function)  to this and other conceptions of "cultural logic".

As a quasi-evolutionary phenomenon, CGSs are designed to capture neither the dual inheritance of genes and culture nor the connectionism of a traditional cognitive model. The outcomes of CGS agents are not geared towards optimal decision-making. Rather, they classify natural phenomena according to a set of discrete oppositions (or categories).

Some of these are based on premises (e.g. historically-determined preferences), while others are based on biological features of the organism. The CGS agents then use the classificatory state of other agents as a cue to either follow (conform) or disperse (dissent). What results is a form of social learning that can be used to update (or obliterate) the space between the lower-level categories.

CGS simulations can be run either in an liquid-like simulation, or independently. In the original conception, which maps CGSs to geographic and other spatial phenomena, a hybrid model was proposed. In experiments geared towards understanding the social construction of economic value, populations of CGS kernels and their agents are static with respect to spatial position. However, they exchange items and attach value to those items based on repeated interaction.

For more information, please see:
Alicea, B.  Contextual Geometric Structures: modeling the fundamental components of cultural behavior. Proceedings of Artificial Life, 13, 147-154 (2012).

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