The SEC’s retail investor 2.0: Interactive data and the rise of calculative accountability

The Securities and Exchange Commission (SEC) in the United States mandated a new digital reporting system for US companies in late 2008. The new generation of information provision has been dubbed by Chairman Cox, ‘interactive data’ (SEC, 2006a). Despite the promise of its name, we find that in the development of the project retail investors are invoked as calculative actors rather than engaged in dialogue. Similarly, the potential for the underlying technology to be applied in ways to encourage new forms of accountability appears to be forfeited in the interests of enrolling company filers. We theorise the activities of the SEC and in particular its chairman at the time, Christopher Cox, over a three year period, both prior to and following the ‘credit crisis’. We argue that individuals and institutions play a central role in advancing the socio-technical project that is constituted by interactive data. We adopt insights from ANT (Callon, 1986; Latour, 1987, 2005b) and governmentality (Miller, 2008; Miller and Rose, 2008) to show how regulators and the proponents of the technology have acted as spokespersons for the interactive data technology and the retail investor. We examine the way in which calculative accountability has been privileged in the SEC’s construction of the retail investor as concerned with atomised, quantitative data (Kamuf, 2007; Roberts, 2009; Tsoukas, 1997). We find that the possibilities for the democratising effects of digital information on the Internet has not been realised in the interactive data project and that it contains risks for the very investors the SEC claims to seek to protect.

Ontology for performance measurement indicators’ comparison

Indicators are widely used by organizations as a way of evaluating, measuring and classifying organizational performance. As part of performance evaluation systems, indicators are often shared or compared across internal sectors or with other organizations. However, indicators can be vague and imprecise, and also can lack semantics, making comparisons with other indicators difficult. Thus, this paper presents a knowledge model based on an ontology that may be used to represent indicators semantically and generically, dealing with the imprecision and vagueness, and thus facilitating better comparison. Semantic technologies are shown to be suitable for this solution, so that it could be able to represent complex data involved in indicators comparison.

Discovering knowledge structures in mind maps of mental health risks

This thesis addressed the problem of risk analysis in mental healthcare, with respect to the GRiST project at Aston University. That project provides a risk-screening tool based on the knowledge of 46 experts, captured as mind maps that describe relationships between risks and patterns of behavioural cues. Mind mapping, though, fails to impose control over content, and is not considered to formally represent knowledge. In contrast, this thesis treated GRiSTs mind maps as a rich knowledge base in need of refinement; that process drew on existing techniques for designing databases and knowledge bases. Identifying well-defined mind map concepts, though, was hindered by spelling mistakes, and by ambiguity and lack of coverage in the tools used for researching words. A novel use of the Edit Distance overcame those problems, by assessing similarities between mind map texts, and between spelling mistakes and suggested corrections. That algorithm further identified stems, the shortest text string found in related word-forms. As opposed to existing approaches’ reliance on built-in linguistic knowledge, this thesis devised a novel, more flexible text-based technique. An additional tool, Correspondence Analysis, found patterns in word usage that allowed machines to determine likely intended meanings for ambiguous words. Correspondence Analysis further produced clusters of related concepts, which in turn drove the automatic generation of novel mind maps. Such maps underpinned adjuncts to the mind mapping software used by GRiST; one such new facility generated novel mind maps, to reflect the collected expert knowledge on any specified concept. Mind maps from GRiST are stored as XML, which suggested storing them in an XML database. In fact, the entire approach here is ”XML-centric”, in that all stages rely on XML as far as possible. A XML-based query language allows user to retrieve information from the mind map knowledge base. The approach, it was concluded, will prove valuable to mind mapping in general, and to detecting patterns in any type of digital information.

The use of information technology in less developed environments:the case of Greece

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Information-theory analysis of skewed coding for suppression of pattern-dependent errors in digital communications

We present information-theory analysis of the tradeoff between bit-error rate improvement and the data-rate loss using skewed channel coding to suppress pattern-dependent errors in digital communications. Without loss of generality, we apply developed general theory to the particular example of a high-speed fiber communication system with a strong patterning effect. © 2007 IEEE.