In this paper, we provide theoretical arguments and empirical evidence for how Genetic Algorithms (GA) can be used for efficient estimation of macro-level diffusion models. Using simulations we find that GA and Sequential Search-Based-Nonlinear Least Squares (SSB-NLS) provide comparable parameter estimates when the data including peak sales are being used, for a range of error variances, and true parameter values commonly encountered in the literature. From empirical analyses we find that the forecasting performance of the GA estimates is better than that of SSB-NLS, Augmented Filter, Hierarchical Bayes, and Kalman Filter when only pre-peak sales data is available for estimation. When sales data until the peak time period are available for estimation, SSB-NLS is able to obtain parameter estimates when the starting values provided are the estimates from using GA. The estimates from GA are not biased and do not change in a systematic fashion when postpeak sales data are used, whereas the estimates from SSB-NLS are biased and change in a systematic fashion. Summarizing, we find that GA may be better suited for diffusion model estimation under the three conditions where SSB-NLS has been found to have problems.

The ability to forecast market share remains a challenge for many managers especially in dynamic markets, such as the telecommunications sector. In order to accommodate the unique dynamic characteristics of the telecommunications market, we use a multi-component model, called MSHARE. Our method involves a two-phase process. The first phase consists of three components: a projection method, a ring down survey methodology and a purchase intentions survey. The predictions from these components are combined to forecast category sales for the wireless subscribers market. In the second phase, market shares for the various brands are generated using the forecast of the number of subscribers tat are obtained in Phase I and the share predictions from the ring down methodology. The proposed methodology produces the minimum Relative Absolute Error for each market as compared to the forecasts from each individual component in the first phase. The value of the proposed model is illustrated by its application to a real world scenario. The managerial implications of the proposed model are also discussed.

Purchase intentions are routinely used to forecast sales of existing products and services. While past studies have shown that intentions are predictive of sales, they have only examined the absolute accuracy of intentions, not their accuracy relative to other forecasting methods. For example, no research has been bale to demonstrate that intentions-based forecasts can improve upon a simple extrapolation of past sales trends. We examined the relative accuracy of four methods that forecast sales from intentions. We tested these methods using four data sets involving different products and time horizons; one of French automobile sales, two of US automobile sales, and one of US wireless services. For all four products and time horizons, each of the four intentions-based forecasting methods was more accurate than an extrapolation of past sales. Combinations of these forecasting methods using equal weights lead to even greater accuracy, with error rates about one-third lower than extrapolations of past sales. Thus, it appears that purchase intentions can provide better forecasts than a study, the consistence of the results in our study suggest that intentions are a valuable input to sales forecasts.

This study tests and contrasts the ability of multidimensional scaling (MDS) and nonlinear mapping (NLM) in recovering complex data structures in attribute space, and aiding researchers and practitioners in making neighborhood interpretations. The relative merits of both MDS and NLM for product positioning are explored and discussed. A formal comparison of the performance of NLM versus MDS is presented using both simulated and actual data. The results of this study provid3e direction as to the conditions under which a nonlinear mapping algorithm is preferable over MDS.

This paper describes a decision support system developed to prioritize oil and gas exploration activities. During each budgeting cycle, the exploration division of a major oil company must review a number of potential oil and gas plays, and assign personnel to the most promising ones. These personnel are teams of geologists and geophysicists who are responsible for the exploration activities on the site. The number of interesting plays always exceeded the available exploration budget and manpower, so management was concerned about the development of a consistent ranking system of oil and gas plays that could be used to guide the budgeting and manpower decision. The development of the ranking system was based on familiar concepts from decision theory, specifically, multiattribute models and the value of information. Linear approximations to more complex mathematical functions were developed to simplify the model. However, this system was designed to be used on a routine basis by personnel unfamiliar with these technical issues, so they were masked from the user. The successful implementation of the proposed decision support system is described.

This study attempts to build a decision model to evaluate job alternatives for doctorial students. The decision model is based on the Von Neumann-Morgenstern utility function. In this case of uncertainty, the utility function can order only preferences. In the case of certainty, the measurable value function can order both preferences as well as preference differences. The utility function is tested for the presence of the strength of preference concept using the theorem of "Substitution of Equal Exchange." A methodology has also been proposed to obtain responses to the utility function and then transforming that function to a measurable value function. The importance of the concept of ordering preference differences and incorporating risk measure is revealed by the superiority of the measurable value function over the utility function in terms of predictive validity.

In order to effectively make forecasts in the telecommunications sector during the growth phase of a new product life cycle, we evaluate performance of an evolutionary technique: genetic algorithms (Gas), used in conjunction with a diffusion model of adoption such as the Bass model. During the growth phase, managers want to predict (1) future sales per period, (2) the magnitude of sales during peak, and (3) when the industry would reach maturity. At present, reliable estimation of parameters of diffusion models is possible, when sales data includes the peak sales also. Cellular phone adoption data from estimates obtained from Gas exhibit good consistency comparable to NLS, OLS, and a naïve time series model when the entire sales history is considered. When censored datasets (data points available until the inflection point) are used, the proposed technique provides better predictions of future sales; peak sales time period, and peak sales magnitude as compared to currently available estimation techniques.