On the relative complexity of labelled modal tableaux

We investigate the relative complexity of two free-variable labelled modal tableaux(KEM and Single Step Tableaux, SST). We discuss the reasons why p-simulation is not a proper measure of the relative complexity of tableaux-like proof systems, and we propose an improved comparison scale (p-search-simulation). Finally we show that KEM p-search-simulates SST while SST cannot p-search-simulate KEM.

The authority of complexity

The assertion about the unique 'complexity' or the peculiarly intricate character of social phenomena has, at least within sociology, a long, venerable and virtually uncontested tradition. At the turn of the last century, classical social theorists, for example, Georg Simmel and Emile Durkheim, made prominent and repeated reference to this attribute of the subject matter of sociology and the degree to which it complicates, even inhibits the development and application of social scientific knowledge. Our paper explores the origins, the basis and the consequences of this assertion and asks in particular whether the classic complexity assertion still deserves to be invoked in analyses that ask about the production and the utilization of social scientific knowledge in modern society. We present John Maynard Keynes' economic theory and its practical applications as an illustration. We conclude that the practical value of social scientific knowledge is not dependent on a faithful, in the sense of complete, representation of social reality. Instead, social scientific knowledge that wants to optimize its practicality has to attend and attach itself to elements of social situations that can be altered or are actionable.

Regularization and complexity control in feed-forward networks

In this paper we consider four alternative approaches to complexity control in feed-forward networks based respectively on architecture selection, regularization, early stopping, and training with noise. We show that there are close similarities between these approaches and we argue that, for most practical applications, the technique of regularization should be the method of choice.

The exact sample complexity of PAC-learning problems with unit VC dimension

The Vapnik-Chervonenkis (VC) dimension is a combinatorial measure of a certain class of machine learning problems, which may be used to obtain upper and lower bounds on the number of training examples needed to learn to prescribed levels of accuracy. Most of the known bounds apply to the Probably Approximately Correct (PAC) framework, which is the framework within which we work in this paper. For a learning problem with some known VC dimension, much is known about the order of growth of the sample-size requirement of the problem, as a function of the PAC parameters. The exact value of sample-size requirement is however less well-known, and depends heavily on the particular learning algorithm being used. This is a major obstacle to the practical application of the VC dimension. Hence it is important to know exactly how the sample-size requirement depends on VC dimension, and with that in mind, we describe a general algorithm for learning problems having VC dimension 1. Its sample-size requirement is minimal (as a function of the PAC parameters), and turns out to be the same for all non-trivial learning problems having VC dimension 1. While the method used cannot be naively generalised to higher VC dimension, it suggests that optimal algorithm-dependent bounds may improve substantially on current upper bounds.

The unique complexity of social phenomena and the uses of social science knowledge

The assertion about the peculiarly intricate and complex character of social phenomena has, in much of social discourse, a virtually uncontested tradition. A significant part of the premise about the complexity of social phenomena is the conviction that it complicates, perhaps even inhibits the development and application of social scientific knowledge. Our paper explores the origins, the basis and the consequences of this assertion and asks in particular whether the classic complexity assertion still deserves to be invoked in analyses that ask about the production and the utilization of social scientific knowledge in modern society. We refer to one of the most prominent and politically influential social scientific theories, John Maynard Keynes' economic theory as an illustration. We conclude that, the practical value of social scientific knowledge is not necessarily dependent on a faithful, in the sense of complete, representation of (complex) social reality. Practical knowledge is context sensitive if not project bound. Social scientific knowledge that wants to optimize its practicality has to attend and attach itself to elements of practical social situations that can be altered or are actionable by relevant actors. This chapter represents an effort to re-examine the relation between social reality, social scientific knowledge and its practical application. There is a widely accepted view about the potential social utility of social scientific knowledge that invokes the peculiar complexity of social reality as an impediment to good theoretical comprehension and hence to its applicability.

An evaluation of the epidemiology of medication discrepancies and clinical significance of medicines reconciliation in children admitted to hospital

Aims: To determine the incidence of unintended medication discrepancies in paediatric patients at the time of hospital admission; evaluate the process of medicines reconciliation; assess the benefit of medicines reconciliation in preventing clinical harm. Method: A 5 month prospective multisite study. Pharmacists at four English hospitals conducted admission medicines reconciliation in children using a standardised data collection form. A discrepancy was defined as a difference between the patient's preadmission medication (PAM), compared with the initial admission medication orders written by the hospital doctor. The discrepancies were classified into intentional and unintentional discrepancies. The unintentional discrepancies were assessed for potential clinical harm by a team of healthcare professionals, which included doctors, pharmacists and nurses. Results: Medicines reconciliation was conducted in 244 children admitted to hospital. 45% (109/244) of the children had at least one unintentional medication discrepancy between the PAM and admission medication order. The overall results indicated that 32% (78/244) of patients had at least one clinically significant unintentional medication discrepancy with potential to cause moderate 20% (50/244) or severe 11% (28/244) harm. No single source of information provided all the relevant details of a patient's medication history. Parents/carers provided the most accurate details of a patient's medication history in 81% of cases. Conclusions: This study demonstrates that in the absence of medicines reconciliation, children admitted to hospitals across England are at risk of harm from unintended medication discrepancies at the transition of care from the community to hospital. No single source of information provided a reliable medication history.