Murali S. Shanker

An application of agent-based simulation to knowledge sharing

This paper explores knowledge sharing using an agent-based simulation model. Built using Repast, our application allows managers to simulate employee knowledge-sharing behaviors by making parametric assumptions on employee decision strategies and organizational interventions that affect identifiability, benefits, and costs. Our results show that in the presence of non-linear and adaptive interaction, unintended and unpredictable outcomes might occur, and that knowledge sharing results from the complex interaction between employee behavior and organizational interventions.

Effect of data standardization on neural network training

Data transformation is a popular option in training neural networks. This study evaluates the effectiveness of two well-known transformation methods: linear transformation and statistical standardization. These two are referred to as data standardization. A carefully designed experiment is used in which data from two-group classification problems were trained by feedforward networks. Different kinds of classification problems, from relatively simple to hard, were generated. Other experimental factors include network architecture, sample size, and sample proportion of group 1 members. Three performance measurements for the effect of data standardization are employed. The results suggest that networks trained on standardized data yield better results in general, but the advantage diminishes as network and sample size become large. In other words, neural networks exhibit a self-scaling capability. In addition, impact of data standardization on the performance of training algorithm in terms of computation time and number of iterations is evaluated. The results indicate that, overall, data standardization slows down training. Finally, these results are illustrated with a data set obtained from the American Telephone and Telegraph Company.

Using neural networks to predict the onset of diabetes mellitus.

Classification is an important decision making tool, especially in the medical sciences. Unfortunately, while several classification procedures exist, many of the current methods fail to provide adequate results. In recent years, artificial neural networks have been suggested as an alternative tool for classification. Here, we use neural networks to predict the onset of diabetes mellitus in Pima Indian women. The modeling capabilities of neural networks are compared to traditional methods like logistic regression and to a specific method called ADAP, which has been used to predict diabetes. The results indicate that neural networks are indeed a viable approach to classification. Furthermore, we attempt to provide a basis upon which neural networks can be used for variable selection in statistical modeling.

An agent-based diffusion model with consumer and brand agents

Market members interact within a complex, adaptive system to effect adoption decisions and the resulting diffusion of innovations. Agent-based modeling (ABM) is a methodology well suited for simulating this system. It complements and extends econometric approaches by incorporating interactions among system members, and adaptation in the system, revealing emergent results. Since ABM allows study at the individual unit level, heterogeneity among system members is reflected and modeling at the brand level is possible. Here an ABM with consumer and brand agents is described. The brand and product diffusion curve output allows study of diffusion at micro and macro levels, respectively.

Estimation of posterior probabilities of consumer situational choices with neural network classifiers

Abstract This study shows how neural networks can be used to estimate the posterior probabilities in a consumer choice situation. We provide the theoretical basis for its use and illustrate the entire neural network modeling procedure with a situational choice data set from AT&T. Our findings supported the appropriateness of this application and clearly illustrate the nonlinear modeling capability of neural networks. The posterior probability estimates clearly add to the usefulness of the technique for marketing research.

Estimating breast cancer risks using neural networks

Breast cancer is one of the most important medical problems. In this paper, we report the results of using neural networks for breast cancer diagnosis. The theoretical advantage is that posterior probabilities of malignancy can be estimated directly, and coupled with resampling techniques such as the bootstrap, distributions of the probabilities can also be obtained. These allow a researcher much more insight into the variability of estimated probabilities. Another contribution is that we present an integrative approach to building neural network models. The issues of model selection, feature selection, and function approximation are discussed in some detail and illustrated with the application to breast cancer diagnosis.

Modeling consumer situational choice of long distance communication with neural networks

This study shows how artificial neural networks can be used to model consumer choice. Our study focuses on two key issues in neural network modeling - model building and feature selection. Using the cross-validation approach, we address these two issues together and specifically examine the effectiveness of a backward feature selection algorithm for consumer situational choices of communication modes. Results indicate that the proposed heuristic for feature selection is robust with respect to validation sample variation. In fact, the feature selection approach produces the same best subset of features as the all-possible-subset approach.

Cost Efficiency Benchmarking for Operational Units with Multiple Cost Drivers

We consider the activity-based costing situation, in which for each of several comparable operational units, multiple cost drivers generate a single cost pool. Our study focuses on published data from a set of property tax collection offices, called rates departments, for the London metropolitan area. We define what may be called benchmark or most efficient costs per unit of driver. A principle of maximum performance efficiency is proposed, and an approach to estimating the benchmark unit costs is derived from this principle. A validation approach for this estimation method is developed in terms of what we call normal-like-or-better performance effectiveness. Application to longitudinal data on a single unit is briefly discussed. We also consider some implications for the more routine case when costs are disaggregated to subpools associated with individual cost drivers.

An approximate model for field service territory planning

Abstract Field service managers are often faced with the problem of balancing the number of technicians, territory size, and field service quality. This paper presents an approximate state-dependent queuing model that can help field service managers make these tradeoffs. Simulation experiments over a variety of field service environments demonstrate that this model is quite accurate for predicting mean travel time and mean response time. The approximate queuing model has been imbedded in a decision support system and implemented by a Fortune 100 company. Management found the decision support system very useful in making important field service decisions.

Estimating probabilities of diabetes mellitus using neural networks.

Abstract Classification problems are often encountered in medical diagnosis. This paper presents an introduction to classification theory and shows how artificial neural networks can be used for classification. We also map out a bootstrapped procedure for interval estimation of posterior probabilities. The entire procedure is illustrated using the diabetes mellitus data in Pima Indians.