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Creators and evolution of ideas

2021-07-06 来源: 51Due教员组 类别: Essay范文

今天给大家带来一篇优秀的论文  这篇论文主要讲述的是 在一个社会群体中,所有成员创造性受益于创造力,一些人仅仅是模仿或享受别人的创造性成果。这篇社会essay代写范文概述了这个问题,使用一个计算机模型的进化。基于神经网络的代理,通过修改现有的发明。对于所有水平的创造力,思想的多样性在人群中的比例呈正相关。一起来看看吧 有论文需要帮忙的亲亲 可以联系我们的专属客服 微信号 Even100100 进行咨询喔~

计算机科学正在越来越广泛地在寻找灵感搜索算法,通过解决问题的技巧,甚至以计算机为基础的艺术表现。可能最有效的问题是人类本身。其有效性在很大程度上源于缺乏创造力的事实。下面的essay代写范文进行详述。

Abstract
  There are both benefits and drawbacks to creativity. In a social group it is not necessary for all members to be creative to benefit from creativity; some merely imitate or enjoy the fruits of others’ creative efforts. What proportion should be creative? This paper outlines investigations of this question carried out using a computer model of cultural evolution referred to as EVOC (for EVOlution of Culture). EVOC is composed of neural network based agents that evolve fitter ideas for actions by (1) inventing new ideas through modification of existing ones, and (2) imitating neighbors’ ideas. The ideal proportion with respect to fitness of ideas occurs when thirty to forty percent of the individuals is creative; yet when creative agents innovate half of the time or less, this proportion grows to a hundred percent. For all levels or creativity, the diversity of ideas in a population is positively correlated with the ratio of creative agents.
 Introduction 
  Computer science is drawing ever more extensively upon the natural world for inspiration in the design of search algorithms, optimization tools, problem solving techniques, and even computer-based artistic expression. Probably the most effective problem solver Mother Nature has come up with is the human mind itself. Its effectiveness derives largely from the fact that it is endlessly creative, able to break out of ruts and come up with ideas and solutions that are new, useful, and appealing. Not only are we individually creative, but we build on each other’s creations such that over the centuries our ideas and inventions can be said to have evolved. In order for computer scientists to put to use the process by which creative ideas evolve through cultural exchange we must first get a deeper computational understanding of it. This paper presents investigations of one aspect of the process: the interaction between how creative individuals are, and how numerous they are in a society. That human creativity is immensely beneficial is fairly obvious. 
  Our considerable capacity for selfexpression, for finding practical solutions to problems of survival, and coming up with aesthetically pleasing objects that delight the senses, all stem from the creative power of the human mind. However, there are also considerable drawbacks to creativity. A creative solution to one problem often generates other problems or unexpected negative side effects that may only become apparent after much has been invested in the creative solution. Moreover, creative individuals are more emotionally unstable and prone to affective disorders such as depression and bipolar disorder, and have a higher incidence of schizophrenic tendencies, than other segments of the population (Andreason, 1987; Flaherty, 2005; Jamieson, 1989, 1993; Styron, 1990). They are also more prone to abuse drugs and alcohol (Goodwin, 1988, 1992; Ludwig, 1995; Norlander & Gustafson, 1996, 1997, 1998; Rothenberg, 1990) as well as suicide (Goodwin & Jamieson, 1990). 
  Also, creative people often feel disconnected from others because they defy the crowd (Sternberg & Lubart, 1995; Sulloway, 1996). However, in a group of interacting individuals only a fraction of them need be creative for the benefits of creativity to be felt throughout the group. The rest can reap the benefits of the creator’s ideas without having to withstand the dark aspects of creativity by simply copying, using, or admiring them. After all, few of us know how to build a computer, or write a symphony, or a novel, but they are nonetheless ours to use and enjoy when we please. One can thus ask: in order for a culture to evolve optimally, what proportion of individuals should be ’creative types’ and how creative should they be? This paper investigates this using an agent-based modeling approach. The agents are too simple to develop affective disorders or abuse alcohol; the drawback to their creativity is that complex solutions to multi-part problems (in biological terms, epistatic relations) break down when too much variation is introduced too quickly. Ideas that are too creative are not implemented as actions in the world; thus if an agent is overly creative, its creative potential is wasted. Moreover, since each iteration each agent either invents or imitates, by deciding (and failing) to invent, it forgoes a chance to imitate.
 The Modeling Approach 
  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, 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. essay代写)
  It has been proposed that 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, 2006, 2008). EVOC incorporates this in part by allowing agents to have multiple interacting needs, thereby fostering complex actions that fulfill multiple needs. Elsewhere (Gabora, 2008a,b) results of experiments using different needs and/or multiple needs are described, as well as how cultural evolution is affected 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. This paper investigates how different proportions of creative to uncreative agents affects the fitness and diversity of ideas.

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