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In order to better understand the role of evolution in the emergence of contextual geometric structures, it is important to take a closer look at the outcome of interactions between three distinct automata populations. Figure 4 shows an example run using automata from three distinct cultures (red, blue, and black). This 2-D LCS volume features 165 automata present at the following frequencies: black (0.35), blue (0.35), and red = (0.30). This allows us to observe a number of purely physical outcomes after the evolution of an initial population. The first of these are loosely-organized vortices, which can either be homogeneous (all automata of the same color) or heterogeneous (automata of multiple colors). The second physical feature is a cluster often found along edges of the volume. These aggregates can be either homogeneous or heterogeneous, and can be considered products of pure diffusion. The third physical feature is a ridge, which can be either homogeneous or heterogeneous and often leads to the formation of vortices. The fourth physical feature is a vortex, which is a tightly packed aggregation of automata which is usually homogeneous. Yet how exactly do these formations map to the evolution of culture? Using a mixed initial population can lead to competition, selection, and other quasi-evolutionary dynamics. The soft classifications inherent to each automaton must be coordinated using a series of features based on principles of attraction and repulsion to allow the diffusion of automata within a flow field to exhibit behaviors relevant to cultural structures and practice. Three features are expected to produce a broad range of highly-complex and realistic cultural scenarios. Initial condition of model The choice of a hybrid soft classificatory/hydrodynamics model may allow us to observe evolution enforced by self-organization. The tracking of particle populations allows for complex dynamics to emerge out of interactions between automata and the environment. In the model presented here, a forcing mechanism more complex than uniform diffusion may be required to produce quasi-evolutionary dynamics (see Supplemental Information). I propose the use of virtual flow jets (embodied in rulesets), which can mimic the uniform diffusive properties of neutral evolution [20]. Likewise, we can approximate natural selection by adding 1/f noise to the flow field. This and other forms of asymmetric perturbation can mimic the directional properties of selection [21]. Depending on force parameters that constrain the simulation environment, the simulation can yield vastly different behaviors. Yet the relational structure between concepts can remain quite similar across contexts. One feature of evolutionary systems is that they are often constrained to a particular evolutionary trajectory by past trajectories and current features [22]. These constraints combined with environmental fluctuations simulated by the addition of systematic noise produce quasi-evolutionary dynamics. Features that shape evolution As previously mentioned, systematic noise can be used to perturb the flow field. This perturbation can approximate different evolutionary dynamics. In a like manner, conditional features are top-down, deterministic perturbations of the flow field that act like selective mechanisms. Three conditional features are proposed: purity, associativity, and syncretism. These features are predicted to produce a wide range of contextual geometric structures that may be identified as complex cultural dynamics (see Figure 5). Each conditional feature operates on the 8 n-dimensional kernels of each automaton. While a lack of selection can produce evolutionary dynamics, higher- level organizational features can also increase the adaptive capacity of an evolutionary system [23, 24]. In this model, this is realized via simple interaction rules which lead to complex and highly-ordered outcomes. Purity is successfully enforced when two or more distinct structures are formed. These structures are distinct in that all automata flow inward towards discrete vortices (Figure 5, Scenario #1). Over time, automata of different subpopulations exhibit total separation from one another. Associativity is successfully enforced when automata flow outward from established vortices along several trajectories towards one another (Figure 5, Scenario #2). Associativity often results in heterogeneous structures, and may lead to interactions between subpopulations. The effectiveness of the purity and associativity sorting mechanisms can be detected using the conditional diversity measure, shown in Equation 8. This measure provides a profile of all automata within a certain level of Lagrangian divergence in the flow field by using a single parameter D. When the value converges upon 0.5, the collection of automata that compose a loosely-associated structure or ridge is highly homogeneous. When the value approaches 0.0, the collection of automata is highly heterogeneous. Syncretism involves the dispersion of automata towards automata of a competing population. This generally involves automata that are aggregated around two or more vortices. Based on this conditional feature, automata spiral outward from these aggregation centers towards each other in overlapping patterns (Figure 5, Scenario #3). The particles (automata) are freely interchanged in the resulting vortex and trailing flow (Figure 5, Scenario #3). The predicted features shown in Figure 4 are approximations of what could be referred to as cultural practice space. In this sense, structures represent the aggregation of different cultures, which are distinct from individual automata holding representations for multiple cultures. This may allow us to make complex cross-cultural comparisons. Intermittent and transient dynamics One of our main assumptions is that variation in a flow field of variable turbulence might contribute to local changes in the rate of evolution. Indeed, actively manipulating the flow parameters is another way to observe the “churn” of cultural evolution. Yet the relationship between the two model components might also allow us to observe selective conservation across cultural structures and practices. What is the evolutionary relationship between the kernel values housed by individual automata and the Lagrangian unfolding in environmental space? To address this, we constructed a rate measure for learning and forgetting (see Equation 9). This measure bridges the gap between model components by tying kernel value segregation between populations to their distance in the Lagrangian flow field. These distances between concepts of practice and the evolutionary trajectory of individual automata (respectively) can be thought of as gaps that are translated between the two models. Learning occurs in cases where the gap between kernel values for different populations of automata is transferred to the evolutionary space (e.g. where 9 the ITD value becomes larger over time). Forgetting occurs when the gap between kernel values for different populations of automata is transferred to the evolutionary space (e.g. where the ITD value becomes smaller over time). Applying this measure when comparing subpopulations refines the model’s ability to simulate the navigation of culturally-specific structures, which result in more coherent structures and life-like behavior. When very large rLF values occur, learning predominates. When very small rLF values occur, forgetting predominates. As in real culture, we expect representations of 10 practice to fluctuate between extremes when the environment is unpredictable. In our model, this could be accomplished when the flow parameters produce a turbulent regime. Conclusions In this , I have proposed both an architecture and set of testable predictions for a model of cultural evolution focused on approximating the structures of practice. There are also several conclusions regarding the applicability of this model to real-world settings. The ultimate goal is to model the diversity and evolutionary dynamics of context. The common features and shortcomings of this model can tell us something about the cultural structures related to practice. Why choose this particular model? The soft classificatory structures were chosen as a way to map cultural practices to both a quantitative scheme and perceptual mechanisms in the brain. The fuzziness of this model is particularly useful in capturing the nuance that cultural representations tend to exhibit. Coupling this to a LCS-inspired model is done to extend the static nature of the classification scheme to an evolutionary context. It is my contention [see 17] that LCS-inspired models capture evolutionary phenomena that fitness landscapes cannot. In the model presented here, flow fields can help us better understand the dynamics of intermingling during cultural contact and intentional segregation based on cultural content. This can lead us to better theories about cultural universals and perhaps even the neural bases of culture. The take-home message from this work is twofold. One part of the message is that the inability of culture to adapt to rapidly-changing environments is not simply inertia. The other part of this message is to suggest that the ability of culture to adapt rapidly to environmental challenges is not free of constraints. Given these conclusions, this method is not meant to be a general-purpose model for understanding every cultural phenomenon. Rather, the focus is on cultural practices and the structures that underlie descriptive structures. To better understand the adaptive capacity of cultural systems, our ultimate goal is to characterize the labyrinthine features of a practice or ritual. This might explain why some practices are resistant to change (such as religious rites), while others can be highly improvisational (such as a jazz score). Notably, this model does not account for hierarchical and ecological relationships between cultural and social groups. Our focus is more on the origins of cultural complexity and the spontaneous nature of cross-cultural interplay. Idiosyncrasies observed in the adaptive capacity of culture can be seen in behaviors unique to our approach. The supplemental information section provides a link to an Animation that demonstrates how automata and even entire structures can exhibit recursive behaviors such as local cycling and clustering by automata type. These are essential ingredients for determining cultural context, but need further development. One key advantage of this model over previous approaches to modeling culture is its relevance to neurobiological processes. Objective categories that incorporate information about cultural context can be placed explicitly in the context of integrative mechanisms in the brain. Similar to a typical model of brain function, the fine-grained biological details are implicit in our 11 soft classification model. Yet unlike a typical model of brain function, the evolution of collective behavior and shared cultural information over time are simulated using a physics-based model. One example of dynamic, nonlinear neuronal processing related to symbolic behavior is multisensory integration. Multisensory integration involves the integration of visual, auditory, and somatosensory information at selective sites in the brain [25]. In mammals, the superior colliculus integrates visual and auditory sensory information for further processing relevant to the orienting function of attention [26]. This combination of senses is not linear, and the coincidence of stimuli in space and time results in a superadditive electrophysiological response [27]. However, neural integration may not be limited solely to combining information from sensory systems [28]. In this model, the soft classification schemes form the basis of cultural practice structures as they might be represented in the brain. For example, a group membership ritual or political campaign can involve many procedures, classifications, and judgements about the natural world that make no sense in isolation or outside the context of a specific ritual. As a neural mechanism, integration may also play a critical role in switching between the logic of cultural structures and active cognition, and may be particularly important when approximating diverse responses to common stimuli that due to context. Future work should also focus on several common phenomena in cultural systems. One example of this is when selected dimensions of a kernel (such as the light-dark or good-bad oppositions) are treated as the entire practice. This often occurs in fundamentalist religions. Another target for future research involves understanding seemingly illogical behaviors, such as reinforced ritualized behaviors, despite the need for cultural change. Placing the evolution and information processing of these phenomena within a logical framework may lead to further advances in understanding behavior and ultimately human nature.()，英语毕业论文，英语论文题目 |
