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Technology for training military teams has evolved through a convergence of advances in simulation technology for individual and collective training, methods for analyzing teamwork and designing training solutions, and intelligent tutoring technologies that adapt training to the student, to accelerate learning. A number of factors have slowed this evolution toward intelligent team tutoring systems (ITTS), including the challenges of processing communications data, which are the currency of teamwork, and the paucity of automated and generalizable measures of team work. Several systems fulfill a subset of the features required of an ITTS, namely the use of team training objectives, teamwork models, measures of teamwork, diagnostic capability, instructional strategies, and adaptation of training to team needs. We describe these systems: the Advanced Embedded Training System (AETS), Synthetic Cognition for Operational Team Training (SCOTT), the AWO Trainer, the Benchmarked Experiential System for Training (BEST), and the Cross-Platform Mission Visualization Tool. We close this chapter with recommendations for future research.

Over the past few years, mathematical and computational models of organizations have attracted a great deal of interest in various fields of scientific research (see Lin & Carley, 1993 for review). The mathematical models have focused on the problem of quantifying the structural (mis)match between organizations and their tasks. The notion of structural congruence has been generalized from the problem of optimizing distributed decision-making in structured decision networks (Pete, Pattipati, Levchuk, & Kleinman, 1998) to the multi-objective optimization problem of designing optimal organizational structures to complete a mission, while minimizing a set of criteria (Levchuk, Pattipati, Curry, & Shakeri, 1996, 1997, 1998). As computational models of decision-making in organizations began to emerge (see Carley & Svoboda, 1996; Carley, 1998; Vincke, 1992), the study of social networks (SSN) continued to focus on examining a network structure and its impact on individual, group, and organizational behavior (Wellman & Berkowitz, 1988). Most models, developed under the SSN, combined formal and informal structures when representing organizations as architectures (e.g., see Levitt et al., 1994; Carley & Svoboda, 1996). In addition, a large number of measures of structure and of the individual positions within the structure have been developed (Roberts, 1979; Scott, 1981; Wasserman & Faust, 1994; Wellman, 1991).

This study proposes targeted modernization of the Department of Defense (DoD's) Joint Forces Ammunition Logistics information system by implementing the optimized innovative information technology open architecture design and integrating Radio Frequency Identification Device data technologies and real-time optimization and control mechanisms as the critical technology components of the solution. The innovative information technology, which pursues the focused logistics, will be deployed in 36 months at the estimated cost of $568 million in constant dollars. We estimate that the Systems, Applications, Products (SAP)-based enterprise integration solution that the Army currently pursues will cost another $1.5 billion through the year 2014; however, it is unlikely to deliver the intended technical capabilities.

Ranked preference data arise when a set of judges rank, in order of their preference, a set of objects. Such data arise in preferential voting systems and market research surveys. Covariate data associated with the judges are also often recorded. Such covariate data should be used in conjunction with preference data when drawing inferences about judges.

To cluster a population of judges, the population is modeled as a collection of homogeneous groups. The Plackett-Luce model for ranked data is employed to model a judge's ranked preferences within a group. A mixture of Plackett- Luce models is employed to model the population of judges, where each component in the mixture represents a group of judges.

Mixture of experts models provide a framework in which covariates are included in mixture models. Covariates are included through the mixing proportions and the component density parameters. A mixture of experts model for ranked preference data is developed by combining a mixture of experts model and a mixture of Plackett-Luce models. Particular attention is given to the manner in which covariates enter the model. The mixing proportions and group specific parameters are potentially dependent on covariates. Model selection procedures are employed to choose optimal models.

Model parameters are estimated via the ‘EMM algorithm’, a hybrid of the expectation–maximization and the minorization–maximization algorithms. Examples are provided through a menu survey and through Irish election data. Results indicate mixture modeling using covariates is insightful when examining a population of judges who express preferences.

Academic knowledge can be put to use in a commercial environment in several ways. One such mechanism to transfer knowledge to the market place is the start of a new, separate company, termed an academic spin-off company, with the aim to commercially develop and exploit the knowledge generated in the university (Fontes, 2003). In 1999, the Dutch Ministry of Economic affairs published a paper stating that the number of high-tech start-ups in the Netherlands lags behind compared to other EU countries and the United States. Subsequently, initiatives were started to stimulate commercial exploitation of knowledge generated within universities. A specific initiative by the Dutch government in the area of the Life Sciences was the so-called Biopartner programme. This was started in 2000 with the objective to enhance the business climate for start-ups in the Life Sciences and to realize 75 start-ups within 5 years (Dutch Ministry of Economic Affairs, 1999). Actions were directed toward increasing awareness, stimulating starters, establishing facilities like a seed fund and academic incubators, and promoting the commercialization of academic knowledge within universities. A few years later, the Technopartner program and the Valorization Grant were implemented with similar instruments aiming at scientists in universities (Dutch Ministry of Economic Affairs, 2003).

In this chapter, I review recent evidence on the developmental origins of health inequality. I discuss the origins of the education-health gradient, the long-term costs caused by early life adversity, and how early life experiences affect the biology of the body. Additionally, I provide complementary evidence on enrichment interventions which can at least partially compensate for these gaps. I highlight emerging lines of scientific inquiry which are likely to have a significant impact on the field. I argue that, while the evidence that early life conditions have long-term effects is now uncontroversial, the literature needs to be expanded both in a theoretical and empirical direction. On the one hand, a model linking early life origins to ageing needs to be developed; on the other hand, a better understanding of the mechanisms – both biological and socioeconomic – is required, in order to design more effective interventions.

This chapter considers some of the limit points of contemporary relations between International Large-Scale Assessments, learning analytic platforms, and theories of mind circulating in contemporary comparative and transnational educational policy discourses. First, aspects of the rise of Big Data and predictive analytics are historicized, with particular attention to how emergent notions of concepts like an intelligent educational economy paradoxically seem to offer unprecedented opportunities for personalizing education that increasingly rely on efforts to construct, universalize, and predict transnational benchmarks. Then, the chapter pursues how such efforts to universalize measures and predict changes have located the mind as a primary target for solving social problems through educational reform. More specifically, the emergence and circulation of the perceptron in the United States during the 1950s and 1960s is suggested as one example of how efforts to model the human mind as a neuro-dynamic learning system became entangled with efforts to produce universal, mobile, and adaptive neuro-dynamic learning systems targeting the transnational optimization of human minds.

Integrating relationship marketing and management research, the author explores internal selling (i.e., a salesperson’s internally focused efforts intended to identify, solicit, and use internal sales resources to support external selling activities) as a unique source of salespeople role stress and examine its contingent outcomes. The conceptual model suggests that internal selling as a job demand and stressor leads to increased salespeople role stress. However, a number of situational (i.e., selling organization market orientation, service climate, and seller–buyer relationship) and individual factors (i.e., networking ability and psychological capital of the salespeople) serve as job and personal resources to moderate the internal selling–outcome relationships, such that when such resources are adequate, internal selling will reduce role stress and increase sales performance. The author also examines situational (i.e., customer solutions offering and formalization of the selling organization) and individual (i.e., salespeople power and social status) antecedents of internal selling. The model provides useful insights and practical guidance for selling organizations to recognize mechanisms associated with internal selling in their organizations, and to intentionally design within organization support systems to enhance salespeople well being and enable them to participate effectively in the relational process of selling. The chapter stresses the need to develop context-specific stress models for different occupations and job roles.

I examined the association between economic, savings, and psychological factors on participation in traditional Individual Retirement Accounts (IRAs) (1983–1985). The data were panels of tax returns representing households qualifying for the maximum IRA contribution and whose only sources of income were employment and investments. Along with traditional economic variables, my regressions included psychological factors such as framing effects based on adaptive expectations. Although both economic and psychological constructs were important in explaining savings behavior, the latter were shown as more salient. Households having less favorable than expected withholding positions increased IRA participation, a finding corroborating prior research. Savings propensity (SAVE) and past participation were the most important factors linked to IRAs. Unexpected investment income was significantly related to IRA participation, providing evidence that deductible IRA contributions represent new savings rather than reshuffled old savings. The policy implications of this study suggest that savings plans redesigned to encourage greater retirement savings should include tax benefits that are in temporal proximity to the desired savings behavior.