网范文：“Modeling the fundamental components”文化不能以简单的结构复杂性来进行建模，关于文化信仰或继承的思想而言。算法模型的文化产业，英语毕业论文，都集中在离散的文化单位,这很难定义和映射到实际的情况。人类学的探讨涉及到结构主义，可以帮助我们更好地理解文化独立分类和对立现象。当代认知神经科学的探讨表明,动态互补可能存在大脑中,但没有这些进化模型被提出。
在这篇范文要提及的措施，基于一个混合模型,近似于个人团体的文化实践和环境中。混合模型是由两个部分组成，第一个是一组离散的分类结构，它们定义空间嵌入粒子模拟的关系。下面的范文进行详述。
Abstract
The structural complexity of culture cannot be characterized by simply modeling cultural beliefs or inherited ideas. Formal computational and algorithmic models of culture have focused on the inheritance of discrete cultural units, which can be hard to define and map to practical contexts. In cultural anthropology, research involving structuralist and post-structuralist perspectives have helped us better understand culturally-dependent classification systems and oppositional phenomena (e.g. light-dark, hot-cold, good-evil).
Contemporary research in cognitive neuroscience suggests that complementary sets may be represented dynamically in the brain, but no model for the evolution of these sets has of yet been proposed. To fill this void, a method for simulating cultural or other highly symbolic behaviors called contextual geometric structures will be introduced. The contextual geometric structures approach is based on a hybrid model that approximates both individual/group cultural practice and a fluctuating environment. The hybrid model consists of two components. The first is a set of discrete automata with a soft classificatory structure. These automata are then embedded in a Lagrangian-inspired particle simulation that defines phase space relations and environmental inputs. The concept of conditional features and equations related to diversity, learning, and forgetting are used to approximate the goal-directed and open-ended features of cultural-related emergent behavior. This allows cultural patterns to be approximated in the context of both stochastic and deterministic evolutionary dynamics. This model can yield important information about multiple structures and social relationships, in addition to phenomena related to sensory function and higher-order cognition observed in neural systems.
Introduction
Why is cultural change so complicated? Intuitively speaking, it seems as though cultural change should be easy to predict. Given the adaptable nature of culture, changes in the environment should be quickly matched by corresponding changes in cultural representations. However, the need for cultural change often does not result in an adaptive response. In some cases, culture often seems to be maladaptive in the face of adaptive pressures. These anecdotal observations demonstrate that cultural change is highly complex. How can we represent this complexity using a computational framework? The patterns that define cultural behaviors across generations and contexts are most likely created via emergent and evolutionary processes.
Unlike goal-directed behaviors such as reaching for a cup of water or following a scent, there is often no clear outcome to pursue. Cultural representations should “make sense” of procedural knowledge in a way that is not only flexible but also constrained by conceptual interlinkage. Cultural systems have been understood using a number of theoretical perspectives. Structural [1] and post-structural [2] perspectives are based on the notion that cultural life is based on a set of structures orthogonal to human cognition. These structures ostensibly emerge from common patterns of behavior over multiple generations, and represent the outcomes of cultural evolution. One signature of these ephemeral structures is the cognitive representation of oppositional sets, which are bounded by extreme concepts for each category.
For example, there may be a phenomenological and objective category shared across cultures bounded by maximal luminance (light) and absolute lack of luminance (dark). The extremes of this category are bounded by human perceptual abilities, so that experience of each culture can be contained within. A "structure" can be defined as sets of relationships between objects in the environment, or experiences that can vary from person to person but are grounded in the same underlying concepts. These structures, which are a critical and implicit component of human cultural practice, have an underappreciated computational potential.
This is particularly useful since many of these features are essential to understanding the evolution of culture across multiple generations [3]. Even more importantly, these structures might be an essential feature of how cultural practices are represented in a neural architecture. In recent years, brain scientists have applied this idea to a system of oppositional sets called complementary pairs [4]. In this approach, oppositional sets are contingent upon coupling, oscillatory, and heterogeneity in the dynamics of neural circuits. While these approaches hold much promise for the study of culture and symbolic systems, there remains a need to more fully integrate dynamical and structural approaches.
I propose that by combining the structural features of cultural practice with a quasievolutionary perspective will result in a model of cultural evolution that maps to both social phenomenology and physiological function. In addition, cultural and symbolic behavioral systems share many features with physical systems that exhibit chaotic behavior. It is this combination of quasi-evolutionary and chaotic dynamics that makes my approach unique. The approach presented here, called Contextual Geometric Structures (CGS), is a Lagrangian-inspired approach that focuses on the structural complexity of cultural and other symbolic behavioral phenomena. In this , I will introduce a hybrid soft classification/hydrodynamics model in the context of cultural phenomena. Initially, basic features of the contextual geometric structure model will be introduced. It will then be demonstrate how this model fits into the milieu of cultural diversity and evolution. This includes features that approximate complex and diverse phenomena. Finally, we will consider this model in the context of neuronal processes.
Contextual Geometric Structures
Prior approaches to modeling culture have included forays into population genetics and game theory [5-7], memetic representations [8, 9], specialized genetic algorithms [10, 11], and conceptual blending models [12, 13]. In this , a computational approach focusing on the structural complexity of culture will be introduced. While the CGS approach incorporates some elements of these prior approaches, this is a fundamentally new approach to the problem. Contextual geometric structures provide advantages that previous models do not. Models inspired by population genetics and game theory are explicitly discrete and focus on inheritance, and so do not produce many of the nonlinear behaviors that culture embodies. While memetic and conceptual blending models may provide insights into the combinatoral potential of cultural change, neither are explicitly dynamical. While computationally efficient, specialized genetic algorithms do not express the fluid output of cultural behaviors not explicitly associated with beliefs. Perhaps greatest advantage of this approach is the mapping of both these properties to a set of formal, computable structures.
Single automata
The cultural repertoire of each automaton (or particle) uses a soft classification scheme to represent the elements of culture. Soft classification [10], a fuzzy logic-inspired methodology, provides several advantages. One of these advantages involves the capacity to represent different cultural contexts in the same model. Another advantage involves the capacity to represent degrees of specific cultural and symbolic behaviors rather than merely its presence or absence. All natural phenomena classified by any single cultural group has a membership function on a membership kernel (Figure 1), bounded by the capacity of a sensory system. The resulting cultural representation of a phenomena will sit somewhere on this scale.
Unlike probabilistic or likelihood models, soft classification does not require related objects and categories to be transitive, distributive, or symmetrical. This allows for the generation of context, which is central to many existing theories of culture. n-dimensional “Soft” Kernels Figure 1 shows one- and two-dimensional examples of cultural representations of "hot" to "cold". Figure 2 demonstrates the membership kernel for three different cultures. The logical structure consists of various membership kernels which serve to classify the experience of each automaton into a common, objective scale. This graded scale acts to link together related concepts as shown in Figure 1. In this sense, they can be high-dimensional structures. One- and two-dimensional structures tend to represent concepts related to practice, while higherdimensional structures represent a mapping from neurobiology to the cultural domain (see equations 1-5).
In Figure 2, the objective scale for hot and cold stimuli has been mapped to a 2-tuple surface for three cultures (A-C) and their overlap. There will be variability between individuals and cultures, which can be evaluated using a common scale. To map physiological function to cultural and symbolic representations, contextual anchors will be used (see 3-tuple surface, Figures 1 and 2). In context, contextual anchors provide a means to mediate the membership between hot and cold with procedural knowledge. When different cultural categories overlap, it may be indicative of previous contact. However, separation between categories may also be indicative of cultural diversity in the form of distinction. Cultural distinction is a common feature of cultural evolution which can sometimes be imposed by its practitioners. In our context, we will assume that cultural distinction is an emergent feature, and is specified by the segregation factor (see Equation 6). Segregation or distinction is characterized by the non-overlapping region between B and C in Figure 2.
Environment
The environmental component of contextual geometric structures involves a second-order Lagrangian system with dynamics that produce solutions analogous to Lagrangian Coherent Structures (LCS) [14]. LCS structures are defined as “ridges” of particles that aggregate in different portions of the flow field. Quantitatively, comparisons between particle positions can be made using either the Finite Time Lyapunov Exponent (FTLE - solved with regard to temporal divergence) or the Finite Space Lyapunov Exponent (FSLE - solved with regard to spatial divergence) [15-16]. Characterization of these features can be encapsulated in a measure called the iterated temporal divergence (see Equation 7). This methodology has previously been applied as a generalized analogy for evolvability in biological evolution [17]. This work is an extension of this application, the schematic of which is shown in Figure 3.
As can be seen in Figure 3, the automata are initialized in the same location and then get diffused by the force field environment. The automata also have properties of replicator vehicles that reproduce according to specified parameters. While the selective component of the model 6 has yet to be specified completely, LCS-like models should produce outcomes dominated by evolutionary neutrality [18]. In addition, our goal is to observe cultural diversity, which involves far-from-equilibrium and sub-optimal behaviors obscured by strong selective pressures. When applied to cultural systems, the LCS approach [19] typically involves observing the diffusion of particles in a hydrodynamic force field and tracking the structures that result (Figure 3). These structures are observed to collide, pull apart, and intermingle over time. Yet external forces introduced by the flow field can influence diffusion, and so the particles will still aggregate into recognizable and orderly structures. Contextual geometric structures show form as a consequence of evolutionary constraints and interactions between agents over time.()
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