Optimal Enhancement and Lifetime of Software Systems: A Control Theoretic Analysis

We develop an optimal control model to maximize the net value provided by a software system over its useful life. The model determines the initial number of features in the system, the level of dynamic enhancement effort, and the lifetime of the system. The various factors affecting these optimal choices are systems characteristics (e.g., complexity, age, quality), user learning, and process maturity. We also consider that there is a time lag between the addition of a feature and the realization of its benefit to users. The basic model is extended to consider the decision of replacing the existing system by a new one.     

Effect of urbanization on the dynamics of ecosystem services: An analysis for decision making in Kolkata urban agglomeration

Urban Ecosystems - Urbanization has profound influence on the changes of land use and land cover, which on the other hand exert significant impact on ecosystem services and their values, especially...     

A Resource‐Based View of Strategic IT Alignment: How Knowledge Sharing Creates Competitive Advantage

A critical decision problem for top management, and the focus of this study, is whether the CEO (chief executive officer) and CIO (chief information officer) should commit their time to formal planning with the expectation of producing an information technology (IT)‐based competitive advantage. Using the perspective of the resource‐based view, a model is presented that examines how strategic IT alignment can produce enhanced organizational strategies that yield competitive advantage. One hundred sixty‐one CIOs provided data using a postal survey. Results supported seven of the eight hypotheses. They showed that information intensity is an important antecedent to strategic IT alignment, that strategic IT alignment is best explained by multiple constructs which operationalize both process and content measures, and that alignment between the IT plan and the business plan is significantly related to the use of IT for competitive advantage. Study results raise questions about the effect of CEO participation, which appears to be the weak link in the process, and also about the perception of the CIO on the importance of CEO involvement. The paper contributes to our understanding of how knowledge sharing in the alignment process contributes to the creation of superior organizational strategies, provides a framework of the alignment‐performance relationship, and furnishes several new constructs.     

Projecting populations for major Pacific Island countries with and without COVID-19: pro-active insights for population policy

Journal of Population Research - The paper projects aggregate populations of six Pacific Island countries in both pre- and post-COVID19 scenarios using a Cohort Component Method for the period...     

Bayesian neural tree models for nonparametric regression

Frequentist and Bayesian methods differ in many aspects but share some basic optimal properties. In real-life prediction problems, situations exist in which a model based on one of the above paradigms is preferable depending on some subjective criteria. Nonparametric classification and regression techniques, such as decision trees and neural networks, have both frequentist (classification and regression trees (CARTs) and artificial neural networks) as well as Bayesian counterparts (Bayesian CART and Bayesian neural networks) to learning from data. In this paper, we present two hybrid models combining the Bayesian and frequentist versions of CART and neural networks, which we call the Bayesian neural tree (BNT) models. BNT models can simultaneously perform feature selection and prediction, are highly flexible, and generalise well in settings with limited training observations. We study the statistical consistency of the proposed approaches and derive the optimal value of a vital model parameter. The excellent performance of the newly proposed BNT models is shown using simulation studies. We also provide some illustrative examples using a wide variety of standard regression datasets from a public available machine learning repository to show the superiority of the proposed models in comparison to popularly used Bayesian CART and Bayesian neural network models.