Scan barcode
alexander0's review
4.0
This book attempts to apply quantitative models of genetic population dynamics to cultural concepts. In total this is a really solid attempt to create linear and complex models for an extremely difficult to capture set of phenomena.
That said, I think there is a reason that most of cultural studies do not borrow from models such as these. Firstly, the "traits" which need to be captured have yet to be well defined. Of course one could simply point to a "Mendelian" cultural trait and its alternatives and apply this to them, but doing so appears to lead to insignificant results, as the authors point out frequently. This phenomena seems adequately complex enough that perhaps a few option should be considered as better starting points for analysis.
One could start with more flexible "traits" needs to be captured such as semiotic signs, sign systems, or fuzzy objects. Secondly, one could begin to study from a different level of phenomena than with approximate units of culture, and derive them later. Either would enable the eventual value of this book however. This book would simply be a conclusion rather than an initial point of scientific discovery.
Another point of difficulty here is that at this point in time, cultural and sociological tracings of information diffusion are more often modeled within graph theoretic math rather than these more cumbersome models. While there's nothing wrong with using these differential models and looking at linear convergences and divergencies, they are not used as frequently in social sciences and information sciences. This is because largely works like this assume directions of causal adoption. These are parent to progeny, teacher to student, and peer to peer sharing, each which assume certain qualities of information diffusion and different population dynamics. At this moment, tracing culture in the contexts which are more popular in information diffusion happen in digital spaces where "population" and direction of information are not clearly determined. Because of this, network models are more often used, and their dynamics are often an after thought or a conjecture. It happens to be pretty rare that one could apply such clear directional models to contemporary cultural diffusion research.
That said, I think there is a reason that most of cultural studies do not borrow from models such as these. Firstly, the "traits" which need to be captured have yet to be well defined. Of course one could simply point to a "Mendelian" cultural trait and its alternatives and apply this to them, but doing so appears to lead to insignificant results, as the authors point out frequently. This phenomena seems adequately complex enough that perhaps a few option should be considered as better starting points for analysis.
One could start with more flexible "traits" needs to be captured such as semiotic signs, sign systems, or fuzzy objects. Secondly, one could begin to study from a different level of phenomena than with approximate units of culture, and derive them later. Either would enable the eventual value of this book however. This book would simply be a conclusion rather than an initial point of scientific discovery.
Another point of difficulty here is that at this point in time, cultural and sociological tracings of information diffusion are more often modeled within graph theoretic math rather than these more cumbersome models. While there's nothing wrong with using these differential models and looking at linear convergences and divergencies, they are not used as frequently in social sciences and information sciences. This is because largely works like this assume directions of causal adoption. These are parent to progeny, teacher to student, and peer to peer sharing, each which assume certain qualities of information diffusion and different population dynamics. At this moment, tracing culture in the contexts which are more popular in information diffusion happen in digital spaces where "population" and direction of information are not clearly determined. Because of this, network models are more often used, and their dynamics are often an after thought or a conjecture. It happens to be pretty rare that one could apply such clear directional models to contemporary cultural diffusion research.
More...