انتقال دانش در یک مدل شبیه سازی نوآوری

To understand the development of innovation processes in these knowledge-driven economies, one needs to focus on underlying processes of creating and sharing new knowledge. In this paper, an evolutionary simulation model is used to achieve some insights into these innovation processes. The model is based on the one hand on rules about market performance, investments and R&D strategies, and on the other hand on a model concerning knowledge creation (the ability of firms to create knowledge through intramural R&D efforts and the ability to discover and absorb new developments from basic academic research and competitors) and knowledge transfer based on an exponentionally expanding pool of (not necessarily new) knowledge of innovations in the own sector, but also from external sources. It is demonstrated that the imitative firm can be economically more successful but this strategy may prove to be superior only after a long time span.

This work is based on basic ideas of evolutionary economics. In this theory, the model of an economy is represented by heterogeneous companies interacting under several rules with the assumption of bounded rationality. The single entities in these economies are bounded in their ability to evaluate different complex alternatives. Companies evolve in different ways and are therefore described as a heterogeneous group with their own evolving histories. Evolutionary scholars studying innovation and processes of change have stressed the need of economic models that draw on empirical reality rather than on its abstract and logical examination. Models proposed by Malerba et al. [1] build upon the verbal logic that explains an economic process using causal arguments and the descriptive explanations that reproduce empirical reality. In other words: “in most of the cases it reflects what the analyst believes is really going on” ([1], p. 4). This type of model building requires firstly an accurate understanding of the variables and mechanisms supporting the causal arguments based on empirical experience. In order to be successful, the empirical research community has to accept the models as representing an accurate version of the empirical reality and, at the same time, the logic of the formal model has to be understandable for good management practices [2]. For this paper it is essential that firms can follow divergent strategies. This means different rules make up their behaviour. Biological evolution and also evolutionary theory include selection processes which are also part of the presented model. Companies may be driven out of the market under pressure of intense competition. One result of these evolutionary variation and selection processes is the emergence of new technologies. Different strategies and decisions in different market situations lead to new techniques and, for producing companies, to a new level of productivity. This paper attempts to model these aspects as well as the variation of companies by considering their actual knowledge as a driver for new business opportunities. Thus this paper is in some way similar to March [3] and March et al. [4] and in a different way comparable with Péli's and Nooteboom's [5] simulation model where also use is made of a two-dimensional knowledge space in which actors move.

This paper presents a model based on economic and knowledge constraints for an evolving market. Besides capital stock, productivity and market development, it takes into account several constraints regarding absorption and knowledge spill-overs as part of the technological experience of a company. This is based on the concept of a cognitive distance and is realized through a knowledge space to model these knowledge constraints. With these assumptions the model shows endogenous growth under several different settings. In this paper, only four parameters are presented which affect not only the economic development, but also the diverse strategies concerning innovation and imitation. Overall, the market under consideration shows high growth with a higher probability of new technologies and a low degree of locality of knowledge. As a result of discrete actions in the knowledge space, the evolution process is very dynamic but stable in the end, which may take many years on real markets. An analysis regarding innovation versus imitation strategies shows the expected behaviour under variation of all four parameters, but the model also demonstrates, even under repressive settings for imitators with a high degree for locality, that in highly innovative economies there may be a chance for a superior imitation strategy. But a high number of imitators limits the ability of competing enterprises to identify new technology, and thus this industry will come up with a lower average productivity. This finding is relevant for market structure and competition policy. In dynamic areas of technology, too many competing firms will cause productivity problems and thus impede the diffusion of this new technology. On the other hand, however, in a science-intensive environment with high technology density and sticky spillover effects, the strategy of imitation may contribute to a more dynamic development of the enterprises on average. To follow such a strategy, R&D activities are required to identify the technologies, and the relation of R&D expenditure for innovation versus imitation matter.