ماشینهای خودکار سلولی برای گسترش فن آوری ها در سیستم های اقتصادی - اجتماعی
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|8613||2007||11 صفحه PDF||سفارش دهید||محاسبه نشده|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Physica A: Statistical Mechanics and its Applications, Volume 383, Issue 2, 15 September 2007, Pages 660–670
We introduce an agent-based model for the spreading of technological developments in socio-economic systems where the technology is mainly used for the collaboration/interaction of agents. Agents use products of different technologies to collaborate with each other which induce costs proportional to the difference of technological levels. Additional costs arise when technologies of different providers are used. Agents can adopt technologies and providers of their interacting partners in order to reduce their costs leading to microscopic rearrangements of the system. Analytical calculations and computer simulations revealed that starting from a random configuration of different technological levels a complex time evolution emerges where the spreading of advanced technologies and the overall technological progress of the system are determined by the amount of advantages more advanced technologies provide, and by the structure of the social environment of agents. We show that agents tend to form clusters of identical technological level with a power law size distribution. When technological progress arises, the spreading of technologies in the system can be described by extreme order statistics.
Recently, the application of statistical physics and of the theory of critical phenomena provided novel insight into the dynamics of socio-economic systems , , , , , ,  and . Various types of models have been developed which capture important aspects of the emergence of communities , opinion spreading , , , ,  and  or the evolution of financial data . The dynamics of innovation and the spreading of new technological achievements show also interesting analogies to complex physical systems , ,  and . The process of innovation has recently been studied by introducing a technology space based on percolation theory . In this model new inventions arise as a result of a random search in the technology space starting from the current best-practice frontier. The model could reproduce the interesting observation that innovations occur in clusters whose sizes are described by the Pareto distribution . Another important aspect of technological development is the spreading of new technological achievements. In a socio-economic system different level technologies may coexist and compete as a result of which certain technologies proliferate while others disappear from the system. One of the key components of the spreading of successful technologies is the copying, i.e., members of the system adopt technologies used by other individuals according to certain decision mechanisms. Decision making is usually based on a cost-benefit balance so that a technology gets adopted by a large number of individuals if the upgrading provides enough benefits. The gradual adaptation of high level technologies leads to spreading of technologies and an overall technological progress of the socio-economic system. In the present paper we consider a simple agent-based model of the spreading of technological achievements in socio-economic systems. Agents of the model may represent individuals or firms which use certain technologies to collaborate with each other. For simplicity, we assume that costs of the cooperation arise solely due to the incompatibility of technologies used by the agents which then have two origins: on the one hand, difference of technological levels incurs cost, the larger the difference is, the higher the cost gets. On the other hand, technologies used by agents may belong to different providers which induce additional costs. Agents interacting with their social neighborhood can decrease their cost by adopting technologies of their interacting partners. The local rejection–adaptation strategy of agents can lead to interesting changes of the system on the meso- and macro-level, namely, agents can form clusters with identical technological levels, which can also be accompanied by an overall technological progress of the system. We analyze the time evolution of this model socio-economic system starting from a random configuration of technological levels and providers without considering the possibility of innovation. Based on analytic calculations and computer simulations we study how the adaptation of technologies of interacting partners leads to spreading of technological achievements. We characterize the microstructure of communities of agents, and the technological progress of the system on the macro-level.
نتیجه گیری انگلیسی
We presented an agent-based cellular automata model to study the spread of technological achievements in socio-economic systems. Agents of the model can represent individuals or firms which use different level technologies to collaborate with each other. Costs arise due to the incompatibility of technological levels and to different technological providers. Agents can reduce their costs by adopting the technologies and providers of their interacting partners. We showed by analytic calculations and computer simulations that the local adaptation–rejection mechanism of technologies results in a complex time evolution accompanied by microscopic rearrangements of technologies with the possibility of technological progress on the macro-level. We showed that agents tend to form clusters of equal technological levels. If higher level technologies provide advantages for agents, the system evolves to a homogeneous state but clusters show a power law size distribution for intermediate times. The redistribution of technological levels involves extreme order statistics leading to an overall technological progress of the system. The presence of providers proved to play a substantial role in the time evolution. The competition of providers seems to make the system more sensitive to advantages provided by the higher level technologies and can lead to additional technological progress by forcing the agents to select locally the more advanced technology. Our model emphasizes the importance of copying in the spreading of technological achievements and considers one of the simplest possible dynamical rules for the decision mechanism. In the model calculations no innovation was considered, i.e., agents could not improve their technological level by locally developing a new technology instead of only taking over of the technology of others. Innovation in the model can be taken into account by randomly selecting agents to increase their technological level by a random amount according to some probability distribution. The generalization of the model in this direction is in progress. Our calculations show also the importance of the structure of local communities in the time evolution of the system which addresses interesting questions for future studies of the model varying the coordination number of the lattice, and on small-world and scale-free network topologies , , ,  and . The emergence of power law size distribution of clusters of agents with equal technological level and the behavior of the exponents on different topologies can be relevant also for applications. Compared to opinion spreading models like the Sznajd-model  and  and its variants ,  and , the main difference is that in our case the technological level of agents is a continuous random variable; furthermore, the decision making is not a simple majority rule but involves a minimization procedure. A closer analogy can be found when two providers are considered in the system so that the spreading of a provider could be interpreted as a success of one of two competing “opinions”. Opinion of individuals can also be represented by a continuous real variable which makes possible to study under which conditions consensus, polarization or fragmentation of the system can occur . Such models show more similarities to our spreading model of technologies. It is interesting to note that our model captures some of the key aspects of the spreading of telecommunication technologies, where for instance mobile phones of different technological levels are used by agents to communicate/interact with each other. In this case, for example, the incompatibility of MMS-capable mobile phones with the older SMS ones may motivate the owner to reject or adopt the dominating technology in his social neighborhood by taking into account the offers of providers of the interacting partners.