Analysis of the Lotka-Volterra competition equations as a technological substitution model

This paper provides insight into the dynamics of the Lotka-Volterra competition (LVC) equations, a much used competition model, and compares the dynamics of LVC competitive substitution to that of several well-known substitution models. The behavior of the LVC equations is analyzed for the special case of a dominant competitor at equilibrium being replaced after the introduction of a small population of an invading competitor with a competitive advantage. Expressions are derived that describe the growth of the invading competitor and that growth is shown to be of four classes: left asymmetric, logistic, right asymmetric with 1−ε₂ asymptote and right asymmetric with γ asymptote. It is shown that the LVC model reverts to logistic substitution in a market of fixed size, a result with important implications. The LVC equations are fitted to the Gompertz, Bass, Non-Symmetrical Responding Logistic (NSRL) and Sharif-Kabir substitution models and compared using a novel graphical technique. The LVC equations can reasonably mimic the full range of curve shapes exhibited by each of these models.     

Adaptive technological substitution models

This article demonstrates the use of feedback estimation approaches to develop self-adaptive innovation diffusion models for forecasting technological substitution. Data from three innovations are analyzed, and limitations of the proposed procedures are discussed.     

Technological learning,technological substitution,and technological change

From a simple dynamic model of competition between product lines it is shown that the shape of learning curves has a powerful influence on the dynamics of technological substitution. Learning of both production efficiency and marketing efficiency is considered. It is asserted that both types of learning are important and that the two are complementary. It is further speculated that production learning is probably more important for commodities and in situations of low per capita income, whereas market learning gains ascendancy in cases of high income and specialized and diversified product lines. In closing, it is noted that simple competitive models are misleading, first because complementarities and coevolutionary processes are probably as important in the overall development of technology as are competitive processes, and second because optimization of the technological system's parts does not guarantee improvement of the performance of the system as a whole.     

Determinants for forecasting technological substitution

This paper investigates the factors determining the rate of technological substitution and evaluates the prospects for forecasting models based on market-share data only. Studies on underlying causes for the substitution of one technology or product by another reveal the frequent presence of a number of factors. These factors are first discussed in general terms so as to establish their causal relationships with the rate of substitution. On the basis of the identified cause-effect relationships, a composite model is formulated that incorporates the impact of individual factors in a combination of additive and multiplicative interactions. The relative importance and sensitivity of different factors in describing the behavior of the substitution process is then studied through a system-dynamics application of the model. Finally, using three specific cases, a comparison is given between the forecasts made by the comprehensive model, incorporating factors that are known to have significant impact on the rate of substitution, with that of a generalized model for forecasting technological substitution.     

Integrating time and space in technological substitution models

This article presents a model that simultaneously considers both time and space dimensions of innovation adoption for forecasting technological substitution. An empirical illustration is presented, and model limitations and further extensions are discussed.     

A generalized model for forecasting technological substitution

This paper presents a generalized mathematical model for forecasting technological substitution under a wide variety of circumstances. Some of the existing models are shown as special cases of the generalized model. Methods are also suggested for improving the reliability of the model by taking corrective measures on the available data and following a step-wise forecasting procedure.     

The automobile road to technological change : Diffusion of the automobile as a process of technological substitution

Advancement of the motor vehicle and its production methods is analyzed as a process of technological change. In a broader context, motor vehicles evolved as an integral component of road transportation through a series of interlaced substitutions of old by new technologies. Building on a large number of studies that described technological substitution processes, first it is shown how new energy forms replaced their predecessors and how the old marine-transport technologies were substituted by new ones. These examples constitute some of the oldest, empirically documented technological changes and show that many events in the dynamics of energy substitution and marine transport are related to technological changes in road transportation. It is shown that these substitution processes can be described by simple rules and that the replacement of old by new technologies in the energy and transport systems lasted about 80 years. The technological changes within road transportation, however, were more rapid. Replacement of horses by automobiles and older by newer generations of motor vehicles and production methods lasted only a few decades in the United States. Thus, technological substitutions within the road-transportation system were considerably shorter than the expansion of railroads, surfaced roads, all road vehicles together, and the more recent expansion of air transportation.     

The normal distribution as a model of technological substitution

A model for forecasting technological substitution, based on the use of the normal distribution, is presented. Both mathematical and graphical techniques are discussed. Examples from the literature are used to illustrate the model.     

A nonsymmetric responding logistic model for forecasting technological substitution

This article presents a simple model of technological substitution termed as nonsymmetric responding logistic (NSRL). Based on the theory that substitution is an imitation process, the model can accommodate different patterns of technological substitution by allowing the imitation effect to vary over time systematically. It allows the S-curve to be symmetrical as well as nonsymmetrical, with the point of inflection responding to the substitution process. Data from four medical innovations are analyzed to illustrate the generality of the model.     

On SD-oriented mechanism of technological innovation

If one enterprise just takes technological innovation as the way for economic profit but not the combinationalprofit of economy, society and ecology, it will never survive in this fierce competition market. Based on the relationshipbetween technological innovation and sustainable development, this paper brings forward that technological innovationmust take the sustainable1 development of society as the fundamental goal. Technological innovation should be the realimpulse for the development of society, economy and ecology. On the basis of the analysis on previous technologicalinnovation impetus mechanism, combined with the influential factors, this paper puts forward SD-oriented mechanism oftechnological innovation.     

Combined forecast process: Combining scenario analysis with the technological substitution model

Forecasts can be improved by combining separate forecasts obtained by different methods. The complementary nature of the scenario analysis and technological substitution models means that combining the two can obtain improved forecasts. The former has the strength of dealing with the uncertain future, while the later offers data-based forecasts of quantifiable parameters. This study thus proposes a process for combining the scenario analysis with the technological substitution model for discussing new generation technological developments. The proposed process not only has the strengths of scenario analysis, but also contains features that scenario analysis lacks, including predicting annual developments for future years, considering old technology development, and forecasting substitution of old technologies. This study uses the forecast of the market share of Fiber to the x in Taiwan over the next ten years as an example illustrating the proposed combined forecast process.     

Forecasting technological substitution: The logistic model of energy systems revisited

A simple logistic model of market substitution has been applied to situations where price and cost parameters vary in time. In such cases the market share does not necessarily evolve as a monotonous function of time. Although this particular feature of the model has not been validated against data from past experience, it can be applied heuristically to studying future trends in market penetration. As an example, possible competition patterns of coal and nuclear power as primary fuels for electricity production have been examined under various combinations of economical parameters. The illustrative results involve both cases where the nuclear contribution would be phased out as well as those where nuclear power could still maintain a strong position a few more decades to come.     

Looking back, looking ahead: Biased technological change, substitution and the wage gap

The implications of ICT on wage inequality are studied by applying a CES production function with skilled and unskilled labour. Skill-biased technological change increases wage inequality. The result is reinforced in a two-sector general equilibrium model if the income elasticity of the demand for high-tech goods and the elasticity of substitution between final goods are larger than one.     

Industry estimates of the elasticity of substitution and the rate of biased technological change between skilled and unskilled labour

We estimate the elasticity of substitution between skilled and unskilled labour and the pace of skill-biased technological change at the industry level. The data is compiled from the March extract of the Current Population Survey (CPS) from 1968 to 2006. Industry information provided by the survey is used to group workers into 13 industry categories and education levels are used to dichotomize workers as skilled or unskilled. We construct measures of the ratio of skilled to unskilled employment and the ratio of skilled to unskilled wages in each industry. Using a relationship implied by profit maximizing behaviour on the part of representative firms, this data generates estimates of structural parameters. We find considerable differences across industries in the elasticity of substitution between skilled and unskilled labour. Furthermore, while most industries have experienced skill-biased technological change, the pace of this change has varied widely across industries.     

推进我国科技进步体制和机制创新

科技快速进步是时代的特征,当代科技进步的特点是：高技术快速发展、不断创新的技术、众多领域技术并进、以当代科学新发现为基础的技术创新。近300年来西方国家工业技术的迅速发展,在于形成了和依靠了市场经济中促进技术进步的一整套制度安排和经济机制,面对世界科技创新的大潮,需要加强体制构建,完善经济机制,实现我国科学技术快速进步。     

On the mechanism of international technology diffusion for energy technological progress

As a needed methodological complement to the existing large-scale complex policy modelling for energy technology diffusion, this paper contributes to an analytical exposition of the fundamental mechanism of international technology diffusion (ITD) for energy technological progress. We offer two different and complementary perspectives to explore the dynamics of energy technology diffusion and progress. We first develop a Solow-type efficiency-improving model of energy technological progress which is described by improvements in primary energy-augmenting efficiency. We further provide a Romer-type variety-expanding model of energy technological progress which is represented by the expansion of differentiated varieties of primary energy technology blueprints. Analysis based on two different models reaches consistent results: there are potential forces in the world economy – working through ITD – that pull individual countries to advance energy technology, ensuring a cross-country convergence in the growth rates of energy technology in the balanced growth path. While ITD plays a role in a cross-country convergence in technological growth rates, cross-country differences in the efficiency of undertaking indigenous research and the capacity of absorbing foreign technology spillovers would lead to a cross-country divergence in the absolute levels of energy technology.     

Research on mechanism of technological diffusion in industry cluster

The main aim of the paper is to explain the reason and mechanism of technological diffusion in industry cluster. Following the idea showed in Zhou Qin＇s model, we further develop the theoretical analysis that how the technological gap between strong enterprises and weak enterprises determines the level and speed of technological diffusion. The bigger the technological disparity between strong enterprise and weak enterprise is, the quicker technology spreads and knowledge overflows; on the contrary, the smaller the technological disparity between strong enterprise and weak enterprise is, the slower technology spread and knowledge overflows. Therefore, this kind of mechanism is helpful for enterprises in an industry cluster to learn from each other and to enable each enterprise close to the ＂the average level＂ of technology or knowledge. As a result, we think that, there exists a close relation between technological gap and technological diffusion. The paper puts forward the way of knowledge overflows of the strong enterprises： imitationdemonstration effect, the longitudinal connection in the industrial chain, the labor force flow and transfer, informal exchange.     

Timing, diffusion, and substitution of successive generations of technological innovations: The IBM mainframe case

Based on the behavioral assumptions of diffusion theory, this article proposes an extension of the Bass diffusion model that simultaneously captures the substitution pattern for each successive generation of a durable technological innovation, and the diffusion pattern of the base technology. Normative guidelines based on the model suggest that a firm should either introduce a new generation as soon as it is available or delay its introduction to a much later date at the maturity stage of the preceding generation. The decision depends on a number of factors including the relative size of the market potentials, gross profit margins, the diffusion and substitution parameters, and the discount factor of the firm. This “now or at maturity” rule is thus an extension and generalization of the “now or never” rule of Wilson and Norton [25]. Empirical and normative implications of the proposed model are explored for four successive generations on IBM mainframe computers: first generation (vacuum tubes); second generation (transistors); 360 family (integrated circuits); and 370 family (silicon chips). The model describes the growth of these generations well. The application of normative guidelines suggests that IBM introduced the two successive generations of 360 and 370 families too late, i.e., their time to market should have been shorter. Limitations and further extensions of the model and the application are discussed.     

Assessing market penetration combining scenario analysis, Delphi, and the technological substitution model: The case of the OLED TV market

The technological substitution model, proposed in 1971, was designed to analyze the penetration process of new-generation technologies replacing old ones. It was expanded in 1979 to consider more than two generations and the substitution process among various entities in the market. However, this expanded model is often limited by a relative lack of data on the latest-generation technology.On the other hand, scenario analysis, as another breed to forecasting tool, provides rich and complex portraits of possible future development of technologies. However, it fails to provide quantifiable forecasts. Therefore, previous researchers combined these two methods to analyze the development of new technologies.Nevertheless, to make forecasting more holistic, the current opinions of seasoned experts should also be taken into account. However, because of the often-diverged expert opinions, a method for consensus building, such as the Delphi method, is also necessary. Therefore, we combined the scenario analysis with the Delphi method and the technological substitution model to analyze the development of a new technology, namely, the latest-generation television (TV), or the organic light-emitting diode (OLED) TV. We elaborate three possible scenarios, and forecast the market share of OLED TV and four other TV technologies in the global market over the next 10 years: cathode ray tube, rear projection, plasma display panel, and liquid crystal display.