Ankle Bones, Rogues, and Sexual Freedom for Women

In this chapter we review the history of mathematics-based approaches to problem solving. The authors suggest that while the ability of analysts to deal with the extremes of data now available is leading to a new leap in the handling of data analysis, information processing, and control systems, that ability remains grounded in the work of early pioneers of statistical thought. Beginning with pre-history, the paper briefly traces developments in analytical thought to the present day, identifying milestones in this development. The techniques developed in studies of computational intelligence, the applications of which are presented in this volume, form the basis for the next great development in analytical thought.

Analyzing Strategic Stance in Public Services Management

This chapter describes the utilization of an uncertain reasoning-based technique in public services strategic management analysis. Specifically, the nascent NCaRBS technique (developed from Dempster-Shafer theory) is used to categorize the strategic stance of each state’s public long-term care (LTC) system to prospector, defender or reactor. Missing values in the data set are termed ignorant evidence and withheld in the analysis rather than transformed through imputation. Optimization of the classification of states, using trigonometric differential evolution, attempts to minimize ambiguity in their prescribed stance but not the concomitant ignorance that may be inherent. The graphical results further the elucidation of the uncertain reasoning-based analysis. This method may prove a useful means of moving public management research towards a state where LTC system development can be benchmarked and the relations between strategy processes, content, and performance examined.

Visual Data Mining for Discovering Association Rules

Both visualization and data mining have become important tools in discovering hidden relationships in large data sets, and in extracting useful knowledge and information from large databases. Even though many algorithms for mining association rules have been researched extensively in the past decade, they do not incorporate users in the association-rule mining process. Most of these algorithms generate a large number of association rules, some of which are not practically interesting. This chapter presents a new technique that integrates visualization into the mining association rule process. Users can apply their knowledge and be involved in finding interesting association rules through interactive visualization, after obtaining visual feedback as the algorithm generates association rules. In addition, the users gain insight and deeper understanding of their data sets, as well as control over mining meaningful association rules.

Applying Information Gathering Techniques in Business-to-Consumer and Web Scenarios

The last decade has shown the e-business community and computer science researchers that there can be serious problems and pitfalls when e-companies are created. One of the problems is related to the necessity for the management of knowledge (data, information, or other electronic resources) from different companies. This chapter will focus on two important research fields that are currently working to solve this problem — Information Gathering (IG) techniques and Web-enabled Agent technologies. IG techniques are related to the problem of retrieval, extraction and integration of data from different (usually heterogeneous) sources into new forms. Agent and Multi-Agent technologies have been successfully applied in domains such as the Web. This chapter will show, using a specific IG Multi-Agent system called MAPWeb, how information gathering techniques have been successfully combined with agent technologies to build new Web agent-based systems. These systems can be migrated into Business-to-

The Analytic Network Process – Dependence and Feedback in Decision-Making

Simple multi-criteria decisions are made by deriving priorities of importance for the criteria in terms of a goal and of the alternatives in terms of the criteria. Often one also considers benefits, opportunities, costs and risks and their synthesis in an overall outcome. The Analytic Hierarchy Process (AHP) with its independence assumptions, and its generalization to dependence among and within the clusters of a decision — the Analytic Network Process (ANP), are theories of prioritization and decision-making. Here we show how to derive priorities from pair-wise comparison judgments, give the fundamental scale for representing the judgments numerically and by way of validation illustrate its use with examples and then apply it to make a simple hierarchic decision in two ways: pair-wise comparisons of the alternatives and rating the alternatives with respect to an ideal. Network decisions are discussed and illustrated with market share examples. A mathematical appendix is also included.

A Tool for Assisting Group Decision-Making for Consensus Outcomes in Organizations

This chapter introduces an approach, ConSULT (Consensus based on a Shared Understanding of a Leading Topic), to enhance group decision-making processes within organizations. ConSULT provides a computer-mediated framework to allow argumentation, collection and evaluation of discussion and group decision-making. This approach allows for the articulation of all reasoning for and against propositions in a deliberative process that leads to cooperative decision-making. The chapter argues that this approach can enhance group decision-making and can be used in conjunction with any computational intelligence assistance to further enhance its outcome. The approach is particularly applicable in an asynchronous and anonymous environment.

Computational Intelligence as a Platform for a Data Collection Methodology in Management Science

With an increased focus in management science on how to collect data close to the real world of managers, we consider how agent-based simulations have interesting prospects that are usable for the design of business applications aimed at the collection of data. As an example of a new generation of data collection methodologies, this chapter discusses and presents a behavioral simulation founded in the agent-based simulation life cycle and supported by Web technology. With agent-based modeling the complexity of the method can be increased without limiting the research as a result of limited technological support. This makes it possible to exploit the advantages of a questionnaire, an experimental design, a role-play and a scenario, gaining the synergy of a combination of these methodologies. At the end of the chapter an example of a simulation is presented for researchers and practitioners to study. 1

Analytical Customer Requirement Analysis Based on Data Mining

This chapter applies data-mining techniques to help manufacturing companies analyze their customers’ requirements. Customer requirement analysis has been well recognized as one of the principal factors in product development for achieving success in the marketplace. Due to the difficulties inherent in the customer requirement analysis process, reusing knowledge from historical data suggests itself as a natural technique to facilitate the handling of requirement information and the tradeoffs among many customers, marketing and engineering concerns. This chapter proposes to apply data-mining techniques to infer the latent information from historical data and thereby improve the customer requirement analysis process.

Making Decisions with Data

This chapter identifies important barriers to the successful application of Computational Intelligence (CI) techniques in a commercial environment and suggests a number of ways in which they may be overcome. It identifies key conceptual, cultural and technical barriers and describes the different ways in which they affect both the business user and the CI practitioner. The chapter does not provide technical detail on how to implement any given technique, rather it discusses the practical consequences for the business user of issues such as non-linearity and extrapolation. For the CI practitioner, we discuss several cultural issues that need to be addressed when seeking to find a commercial application for CI techniques. The authors aim to highlight to technical and business readers how their different expectations can affect the successful outcome of a CI project. The authors hope that by enabling both parties to understand each other’s perspective, the true potential of CI can be realized.

Modeling Brand Choice Using Boosted and Stacked Neural Networks

Starting with a review of some classical quantitative methods for modeling customer behavior in the brand choice situation, some new methods are explained which are based on recently developed techniques from data mining and artificial intelligence: boosting and/or stacking neural network models. The main advantage of these new methods is the gain in predictive performance that is often achieved, which in a marketing setting directly translates into increased reliability of expected market share estimates. The new models are applied to a well-known data set containing scanner data on liquid detergent purchases. The performance of the new models on this data set is compared with results from the marketing literature. Finally, the developed models are applied to some practical marketing issues such as predicting the effect of different pricing schemes upon market share.