A generalized logistic model for technological forecasting

Resulting from a predominantly forward-looking behavior of the producers, a generalized logistic model is developed and tested here as an approximation of engineering-design process.     

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.     

A reliability engineering approach for forecasting technological breakthroughs

One of the major attributes determining system reliability, the one that has received the most thorough and systematic study for many years, is system survival function. A “survival function” is a mathematical formula relating the probability of satisfactory performance of a system to time. Here, probability of satisfactory performance is synonymous with probability of nonfailure or probability of survival of a performing system.In breakthrough analysis of complex technological systems, the situation is somewhat similar but opposite to the above system reliability case. For breakthrough forecasting, the problem is to determine the probability of occurrence of success of a nonperforming system. Thus this paper presents a quantitative methodology for forecasting technological breakthroughs using a new concept of “attainability function,” derived in a similar fashion as the “reliability function.”     

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.     

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 a family of data-based transformed models useful in forecasting technological substitutions

This paper examines, via real data, some well known models for technology substitution analysis. We propose a family of data-based transformed models that will include the models under examination as special cases. The basic thrust of the paper is the recognition that for technology substitution analysis, the observations are time series data and hence are not independent. Also, the functional form of the model should be determined by both theoretical considerations as well as the data on hand. This suggests that the traditional ordinary least squares procedure used in estimating the parameters and the resulting forecasting procedures are not adequate. The existing models examined here are Fisher–Pry, Gompertz, Weibull, and Normal. We stress the statistical aspects of the models and their relative merits in terms of predictive power. The criteria used for the purpose of comparison are the mean squared deviation and the mean absolute deviation of the predicted values compared with the actual observations.     

A review of selected recent advances in technological forecasting

During the past decade, there have been some significant developments in technological forecasting methodology. This paper describes developments in environmental scanning, models, scenarios, Delphi, extrapolation, probabilistic forecasts, technology measurement and some chaos-like behavior in technological data. Some of these developments are refinements of earlier methodology, such as using computerized data mining (DM) for environmental scanning, which extends the power of earlier methods. Other methodology developments, such as the use of cellular automata and object-oriented simulation, represent new approaches to basic forecasting methods. Probabilistic forecasts were developed only within the past decade, but now appear ready for practical use. Other developments include the wide use of some methods, such as the massive national Delphi studies carried out in Japan, Korea, Germany and India. Other new developments include empirical tests of various trend extrapolation methods, to assist the forecaster in selecting the appropriate trend model for a specific case. Each of these developments is discussed in detail.     

Toward a model procedure for social forecasting

This article presents the main results of a methodological study on social forecasting. The purpose of the study was to investigate the possibility of a systematic process of making social predictions. The outcome of this research was positive insofar as a “model” procedure was constructed to assess the validity and verify the credibility of tentative forecasts. The article elucidates, in general terms, the principal elements and structural characteristics of the model in a two-dimensional framework (a predictability continuum and a validation sequence) that forms the conceptual matrix of the study. On the basis of this, the operational procedure of the model is given in the three phases (probabilism, voluntarism, possibilism) through which any forecast can be tested. The process is illustrated in a flowchart and summarized in a table.     

Some extensions to a stochastic model for technological forecasting

In this article we propose and analyze two stochastic models for technological forecasting. These models are extensions of the simple model proposed by the author in an earlier article.     

A modified procedure for simulation of macroeconometric models

The rational expectations literature suggests that even the effects of unanticipated changes in policy variables may not be accurately described by conventionalsimulations of macroeconometric models. Beginning with Zellner and Palm's time-series approach to structural econometric modelling, a modified simulation procedure is developed to examine the effects of changes in policy variables which are consistent with historic time-series processes. A state-space representation and the Akaike Information Criterion are used to model exogenous variable processes, and results from modified and conventional simulations are compared.     

The logistic envelope approximation in technological forecasting

In a great number of cases, the growth behavior of a field of technology is the sum total of many technological achievements, each following its own logistics-type life story. The tendency in sequential logistic lines positions is described with a logistic curve tangent to individual curves—a logistic envelope. A method of constructing such an envelope based on experimental data is proposed and applied to forecasting of primary electro-chemical cells' development.     

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.     

The Weibull distribution as a general model for forecasting technological change

This paper presents a simple and widely applicable model for technological forecasting using the Weibull distribution function. It shows the generality of the Weibull function, which can accommodate different patterns of technological change. A method for estimating parameters and some applications are presented to illustrate the usefulness of the model.     

A linear algebraic procedure for solving linear perfect foresight models

This paper presents a failsafe method for analyzing any linear perfect foresight model. It describes a procedure which either computes the reduced-form solution or indicates why the model has no reduced form.     

Two generalized rational models for forecasting innovation diffusion

This article demonstrates the development and use of two Generalized Rational Models I and II (GRM I and II) representing innovation diffusion. Specifically, the GRM II covers the same area as the NSRL model, which includes the Coleman and the Blackman/Fisher-Pry models, while the GRM I covers the same area as a modified NSRL model (mod. NSRL), also introduced hereby, and including Floyd, Blackman/Fisher-Pry, Sharif-Kabir and Exponential models. Both the GRM I and the GRM II provide a form of differential equation which always has for a solution a fact which cannot be met when dealing with the NSRL and mod. NSRL models.Some applications are presented, first to illustrate the wide applicability and the usefulness of the models and second to demonstrate the alternate use of the GRM I and mod. NSRL, and GRM II and NSRL models, which usually approximate very well.     

Technological growth curves : A competition of forecasting models

In order to determine procedures for appropriate model selection of technological growth curves, numerous time series that were representative of growth behavior were collected and categorized according to data characteristics. Nine different growth curve models were each fitted onto the various data sets in an attempt to determine which growth curve models achieved the best forecasts for differing types of growth data. The analysis of the results gives rise to a new approach for selecting appropriate growth curve models for a given set of data, prior to fitting the models, based on the characteristics of the data sets.     

Strategic planning I: The roles of technological forecasting

This series of papers deals with technological forecasting in strategic planning. In the first paper the author examines forecasting and planning as a process, identifying five roles of forecasting. (1) Identifying policy options: (2) Aiding strategy formulation: (3) Identifying program options: (4) Selecting programs for funding: and (5) Selecting oppurtunities for investment. The subsequent papers deal primarily with the first and third roles.     

The use of discounted least squares in technological forecasting

The use of growth curves in technological forecasting usually employs an equal weighting of all data points in the time series. This paper considers the benefits of weighting recent information more heavily through the utilization of discounted least squares. The method is used to model the growth of the percentage of households with CATV; discounting gives better results for short-term forecasting.