Когда речь о проникновении моделей, взятых из теории эвоолюции, в социологию, историю и т.п., вспоминают обычно социалдарвинизм - а он был в 1920-е годы и давно кончился. Поэтому легко махнуть рукой и более не заморачиваться. Однако все это время такие попытки существовали, и по сю пору они есть - разной степени удачности. Вот некоторый обзор 2018 года и очередная попытка - с полной, как всегда уверенностью, что - надо. Ну а как? Неумолимое течение познания, никуда не деться.
Авторы разумно говорят, что прежние попытки были редукционистскими и потому ничего не вышло. Они разумно говорят, что культурная эволюция, в отличие от генетической, включает очень большую долю горизонтальных переносов, не связанных с наследованием.
И они говорят простую вещь. Сейчас эти модели, взятые из теории биологической эволюции, и добавленные к ним соображения о "горизонтальном переносе", прямых культурных заимствованиях, - служат основой вычислительных моделей. То есть когда идет речь о прогностике - что будет, когда закончится нефть? что случится, когда население увеличится вдвое? как повлияет изоляция на экономику? - в общем, как только задают вопрос, в ответ идут компьютерные модели, которые объективно показывают - вот вам сильный вариант, вот слабый, вот средний: выбирайте! А при создании этих моделей используются алгоритмы, проверенные. в частности, для задач биологической эволюции. И споров о "правомерности" нет, и тихо всё, и польза есть. Тем самым - всё хорошо.



Critiques of evolutionary models of culture have a long history in the Americanist anthropological tradition (Carneiro, 2003; Mace, 2014; Perry and Mace, 2010), and today there remains question about the appropriateness of the ‘analogy’ between cultural and biological evolution (Claidi`ere and Andr'e, 2011; Gabora, 2013). Cultural evolution uses different information channels, with different properties.
Note that while some view the central criterion of evolution to be replication with variation and selection (e.g., Hull et al., 2001), this is but one form of evolution. Evolution can also occur through communal exchange and self-organization (Gabora, 2013; Vetsigian, 2006) and through context-driven actualization of potential (Gabora, 2005, 2006) (for specific and general discussions of this topic see Kopps et al., 2015 and Gabora and Aerts, 2002, respectively; see also Appendix 1.) This approach is sometimes referred to as Self-Other Reorganization, because it involves both interactions within self-organizing structures, and interactions between them. We emphasize that for a process to be evolutionary (whether it be Darwinian evolution, or not), change must occur on the basis of a fitness function, or an environment that confers constraints and affordances. If not, i.e., if change is random, it is not due to evolution but to processes such as drift (i.e., variation in the relative frequency of different genotypes in a small population, owing to the chance disappearance of particular genes as individuals die or do not reproduce). Cultural evolution is fueled by the generation of, and reflection on, creative ideas, which may exist not in the form of a collection of explicitly actualized variants as is required for biological evolution, but in a state of potentiality

If an idea in a state of potentiality is considered with respect to one context it evolves one way, whereas if considered with respect to another context it evolves another way; there are no variants that get actualized and selected amongst. The mathematical description of evolution through variation and selection is very different from that of evolution through actualization of potentiality, which can be mathematically described drawing on the formalisms of superposition and interference (this is explored in the following literature: Aerts et al., 2016; Gabora and Aerts, 2005; Gabora and Carbert, 2015; Gabora and Saab, 2011). The principal differences between biological and cultural information (e.g. see Richerson et al., 2010) are addressed by the EES. For example, cultural information has the potential to evolve faster than biological information (e.g., Reynolds, 1994; Gabora, 1997), proposed by some to result in genetic evolution lagging behind cultural evolution in the face of selective pressure change. An example of this can be found in dietary changes that have arisen culturally since the Neolithic, for which the human genome has not yet fully responded (Arnold, 2014), with phenotypic plasticity maintaining fitness in the interim (Perreault, 2012).

Another major difference between cultural and biological evolution is that culture (extrasomatic information) may be transmitted horizontally, among members of a given generation, and in so being has long been called fundamentally non-evolutionary in its processes. However, horizontal gene transfer (discussed further below) is prevalent in the world of the asexually-reproducing species, and has been since lifebegan billions of years ago (Bock, 2010; Dunning Hotopp, 2011; McDaniel et al., 2010; Syvanen, 2012). Thus, with respect to the Inheritance of Acquired Characteristics sense, Lamarck was broadly correct about a fundamental evolutionary mechanism for most life (which is microbial) and for all of the history of life on Earth--and in the case of cultural information, horizontal transmission of information has been important since at least the time of the most recent evolutionary transition (sensu Szathmary and Maynard-Smith) which included the evolution of complex, learned and shared extrasomatic guides to behavior, also known as ‘culture’. Finally, it has been convincingly argued that ecologically-deterministic models of cultural selection that do not account for the variability of human behavior are unrealistically crude, reducing primate individuals to Optimal Foragers slaved to fitness calculations (e.g., Laland, 2015). However, EES-influenced workers are responding; Gabora (1999, 2013) has proposed an evolutionary (in the above sense) albeit non-Darwinian model of culture that highlights individual agency in an evolutionary framework.







At this point, it is important to address the serious danger attempting to explain the frequency of cultural traits in terms of a biologically-derived conception of fitness. We suggest that this issue may account for the sterility of the highly reductionist approaches to cultural evolution— including evolutionary psychology and memetics—of the 1990-2000 era (e.g. Cosmides and Tooby, 1997). In biology, characteristics that confer lower fitness can persist for several reasons, i.e., they may be ‘hitchhiker genes’ that piggyback alongside other genes that confer adaptive benefit (Smith and Haigh, 1974). Similarly, an ‘optimizing’ approach to cultural evolution is problematic because maladaptive cultural traits can hitchhike alongside beneficial ones (Gabora, 1997), and even persist for centuries in certain conditions (Edgerton, 1991). This persistence may reflect that a given cultural trait may be adaptive for some, and not for others (e.g., slavery, see Donald, 1997 and Wolf, 1982) or adaptive in some contexts, and not others (Pierce and Ollason, 1987).
It is easy to misleadingly overextend analogies between cultural and biological processes (Claidi`ere and Andr'e, 2011; Mesoudi, 2015). Nevertheless, cultural evolution is not simply ‘like’ biological evolution; it is an evolutionary process. Computational models of cultural evolution exhibit not just the cumulative, adaptive, open-ended change that defines an evolutionary process, but other key attributes such as epistasis, drift, overdominance, and underdominance, as well as incorporating phenomena unique to culture, such as the capacity to learn trends and use them to bias the generation of novelty, and the capacity to mentally simulate outcomes without having to actualize or manifest them (Gabora, 1995; 2008). Where it is not (or where the issue is for the moment unclear), we may still make use of metaphor in discussing biological and cultural evolutionary processes; useful metaphors can stimulate exploration, communicate essences, serve as aids to memory and aid in experimental design, but we must beware not to reify them, or develop a false sense of understanding when using them; for Hoffman (1980), “…scientists…can be quite well aware of the differences between concepts given by the theories and concepts suggested by metaphors [which are] used to explore nature and to lead to modification of principles” (pp. 403). We know that musical notes are not played by DNA code, but a useful metaphor for the revelations of developmental genetics is that is that the genome is more ‘like a jazz score than a blueprint’ (Porta, 2003), an heuristic device that does not build a “just so” story (see also Boone and Smith, 1998 and Smith et al., 2001). As Sober (2006) points out, “descriptors singled out for treatment in science always abstract from complexities. If there is an objection to the descriptors used in models of cultural evolution, it must concern the details of how these models are constructed, not the mere fact that they impose a descriptive framework of some sort or other” (pp. 487).

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