Modeling Cultural Dynamics--论文代写范文精选

增加领导的数量可以减少这种影响。模拟一个代理的条件移民，从一个文化到另一个新思想,领导人的榜样有什么影响，在一个特定的文化，下面的paper代写范文进行论述。

Abstract 
EVOC (for EVOlution of Culture) is a computer model of culture that enables us to investigate how various factors such as barriers to cultural diffusion, the presence and choice of leaders, or changes in the ratio of innovation to imitation affect the diversity and effectiveness of ideas. It consists of neural network based agents that invent ideas for actions, and imitate neighbors’ actions. The model is based on a theory of culture according to which what evolves through culture is not memes or artifacts, but the internal models of the world that give rise to them, and they evolve not through a Darwinian process of competitive exclusion but a Lamarckian process involving exchange of innovation protocols. 

EVOC shows an increase in mean fitness of actions over time, and an increase and then decrease in the diversity of actions. Diversity of actions is positively correlated with population size and density, and with barriers between populations. Slowly eroding borders increase fitness without sacrificing diversity by fostering specialization followed by sharing of fit actions. Introducing a leader that broadcasts its actions throughout the population increases the fitness of actions but reduces diversity of actions. Increasing the number of leaders reduces this effect. Efforts are underway to simulate the conditions under which an agent immigrating from one culture to another contributes new ideas while still ‘fitting in’.

Introduction 
What impact do leaders and role models have on the views and behaviors in a given culture? Is a dictatorial or a distributed mode of leadership more effective? What is the effect of complete or semi-permeable barriers to trade and immigration, or barriers that erode or strengthen with time? And what implications do these kinds of cultural patterns have on how best to negotiate, conduct business, or simply behave in a foreign land? These questions and others are addressed using a computer model of culture referred to as EVOC (for EVOlution of Culture). EVOC consists of neural network based agents that invent ideas for actions, and imitate neighbors’ actions (Gabora, 2008). EVOC is an elaboration of Meme and Variations, or MAV (Gabora, 1994, 1995), the earliest computer program to model culture as an evolutionary process in its own right. 

MAV was inspired by the genetic algorithm (GA), a search technique that finds solutions to complex problems by generating a ‘population’ of candidate solutions through processes akin to mutation and recombination, selecting the best, and repeating until a satisfactory solution is found. Although MAV has inspired the incorporation of cultural phenomena (such as imitation, knowledge-based operators, and mental simulation) into evolutionary search algorithms (e.g. Krasnogor & Gustafson, 2004), the goal behind MAV was not to solve search problems, but to gain insight into how ideas evolve. It used neural network based agents that could (1) invent new ideas by modifying previously learned ones, (2) evaluate ideas, (3) implement ideas as actions, and (4) imitate ideas implemented by neighbors. 

Agents evolved in a cultural sense, by generating and sharing ideas for actions, but not in a biological sense; they neither died nor had offspring. The approach can thus be contrasted with computer models of the interaction between biological evolution and individual learning (Best, 1999, 2006; Higgs, 2000; Hinton & Nowlan, 1987; Hutchins & Hazelhurst, 1991). MAV successfully modeled how ‘descent with modification’ can occur in a cultural context, but it had limitations arising from the outdated methods used to program it. Moreover, although new ideas in MAV were generated making use of acquired knowledge and pattern detection, the name ‘Meme and Variations’ implied acceptance of the notion that cultural novelty is generated randomly, and that culture evolves through a Darwinian process operating on discrete units of culture, or ‘memes’. Problems with memetics and other Darwinian approaches to culture have become increasingly apparent (Boone & Smith, 1998; Fracchia & Lewontin, 1999; Gabora, 2004, 2006, 2008; Jeffreys, 2000). One problem is that natural selection prohibits the passing on of acquired traits (thus you don’t inherit your mother’s tattoo). 1 

In culture, however, ‘acquired’ change—that is, modification to ideas between the time they are learned and the time they are expressed—is unavoidable. Darwinian approaches must assume that elements of culture are expressed in the same form as that in which they are acquired. Natural selection also assumes that lineages do not intermix. However, because ideas cohabit a distributed memory with a multitude of other ideas, they are constantly combining to give new ideas, and their meanings, associations, and implications are constantly revised. It has been proposed what evolves through culture is not discrete memes or artifacts, but the internal models of the world that give rise to them (Gabora, 2004), and they evolve not through a Darwinian process of competitive exclusion but a Lamarckian process involving exchange of innovation protocols (Gabora, 206, 2008). (paper代写)

EVOC incorporates this in part by allowing agents to have multiple interacting needs, thereby fostering complex actions that fulfill multiple needs. Elsewhere (Gabora, 2008) the results of experiments using different needs and/or multiple needs are described. This paper describes other experiments carried out with EVOC that were not possible to carry out with MAV. These experiments investigate how cultural evolution is affected by leadership, and by affordances of the agents’ world, such as world shape and size, population density, and barriers that impede information flow, and potentially erode with time.(paper代写)

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