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This paper will provide an overview of the contemporary surveillance environment in the age of Big Data and an insight into the complexities and overlap between security, bodily and informational surveillance as well as the subsequent impacts on privacy and democracy. These impacts include the ethical dilemmas facing librarians and information scientists as they endeavour to uphold principles of equality of access to information, and the support of intellectual freedom in private in an increasingly politicised informational environment. If we accept that privacy is integral to the notion of learning, free thought and intellectual exploration and a crucial element in the separation of the state and the individual in democratic society, then the emergence of the data age and the all-encompassing surveillance and exposure of once private acts will undoubtedly lead to the reimagining of the social and political elements of society.

Despite the increasing use of the agency perspective in studies of headquarters-subsidiaries relations in the multinational corporation (MNC), opponents fundamentally question its utility. In an attempt to contribute to this debate, we evaluate prior studies and develop considerations for future research. Our review of extant studies of headquarters-subsidiaries relations that make (explicit) use of the agency perspective reveals two significant shortcomings. First, we identify a need to validate the underlying assumptions when using the agency perspective in studies of headquarters-subsidiaries relations. Second, we detect a need to better account for the complex nature of headquarters-subsidiary relations in the MNC. A focus on these two areas can improve the use of the agency perspective and, ultimately, help resolve the contentious debate over the utility of the agency perspective.

In this chapter we highlight a neurodynamic approach that is showing promise as a quantitative measure of team performance.

During teamwork the rapid electroencephalographic (EEG) oscillations that emerge on the scalp were transformed into symbolic data streams which provided historical details at a second-by-second resolution of how the team perceived the evolving task and how they adjusted their dynamics to compensate for, and anticipate new task challenges. Key to this approach are the different strategies that can be used to reduce the data dimensionality, including compression, abstraction and taking advantage of the natural redundancy in biologic signals.

The framework emerging is that teams continually enter and leave organizational neurodynamic partnerships with each other, so-called metastable states, depending on the evolving task, with higher level dynamics arising from mechanisms that naturally integrate over faster microscopic dynamics.

The development of quantitative measures of the momentary dynamics of teams is anticipated to significantly influence how teams are assembled, trained, and supported. The availability of such measures will enable objective comparisons to be made across teams, training protocols, and training sites. They will lead to better understandings of how expertise is developed and how training can be modified to accelerate the path toward expertise.

The innovation of this study is the potential it raises for developing globally applicable quantitative models of team dynamics that will allow comparisons to be made across teams, tasks, and training protocols.

The paper considers the phenomenon of Big Data through the work of Hannah Arendt on technology and on thinking. By exploring the nuance to Arendt’s critique of technology, and its relation to the social and political spheres of human activity, the paper presents a case for considering the richness of Arendt’s thought for approaching moral questions of Big Data. The paper argues that the nuances of Arendt’s writing contribute a sceptical, yet also hopeful lens to the moral potential of Big Data. The scepticism is due to the potential of big data to reduce humans to a calculable, and thus manipulatable entity. Such warnings are rife throughout Arendt’s oeuvre. The hope is found in the unique way that Arendt conceives of thinking, as having a conversation with oneself, unencumbered by ideological, or fixed accounts of how things are, in a manner which challenges preconceived notions of the self and world. If thinking can be aided by Big Data, then there is hope for Big Data to contribute to the project of natality that characterises Arendt’s understanding of social progress. Ultimately, the paper contends that Arendt’s definition of what constitutes thinking is the mediator to make sense of the morally ambivalence surrounding Big Data. By focussing on Arendt’s account of the moral value of thinking, the paper provides an evaluative framework for interrogating uses of Big Data.

In their authoritative literature review, Breen and Jonsson (2005) claim that ‘one of the most significant trends in the study of inequalities in educational attainment in the past decade has been the resurgence of rational-choice models focusing on educational decision making’. The starting point of the present contribution is that these models have largely ignored the explanatory relevance of social interactions. To remedy this shortcoming, this paper introduces a micro-founded formal model of the macro-level structure of educational inequality, which frames educational choices as the result of both subjective ability/benefit evaluations and peer-group pressures. As acknowledged by Durlauf (2002, 2006) and Akerlof (1997), however, while the social psychology and ethnographic literature provides abundant empirical evidence of the explanatory relevance of social interactions, statistical evidence on their causal effect is still flawed by identification and selection bias problems. To assess the relative explanatory contribution of the micro-level and network-based mechanisms hypothesised, the paper opts for agent-based computational simulations. In particular, the technique is used to deduce the macro-level consequences of each mechanism (sequentially introduced) and to test these consequences against French aggregate individual-level survey data. The paper's main result is that ability and subjective perceptions of education benefits, no matter how intensely differentiated across agent groups, are not sufficient on their own to generate the actual stratification of educational choices across educational backgrounds existing in France at the beginning of the twenty-first century. By computational counterfactual manipulations, the paper proves that network-based interdependencies among educational choices are instead necessary, and that they contribute, over and above the differentiation of ability and of benefit perceptions, to the genesis of educational stratification by amplifying the segregation of the educational choices that agents make on the basis of purely private ability/benefit calculations.