Reasoning With Partial Knowledge

We investigate how sociological argumentation differs from classical first–order logic. We focus on theories about age dependence of organizational mortality. The overall pattern of argument does not comply with the classical monotonicity principle: Adding premises overturns conclusions in an argument. The cause of nonmonotonicity is the need to derive conclusions from partial knowledge. We identify metaprinciples that appear to guide the observed sociological argumentation patterns, and we formalize a semantics to represent them. This semantics yields a new kind of logical consequence relation. We demonstrate that this new logic can reproduce the results of informal sociological theorizing and lead to new insights. It allows us to unify existing theory fragments, and it paves the way toward a complete classical theory.     

Infants' Ability to Learn Phonetically Similar Words: Effects of Age and Vocabulary Size

What do novice word learners know about the sound of words? Word‐learning tasks suggest that young infants (14 months old) confuse similar‐sounding words, whereas mispronunciation detection tasks suggest that slightly older infants (18–24 months old) correctly distinguish similar words. Here we explore whether the difficulty at 14 months stems from infants' novice status as word learners or whether it is inherent in the task demands of learning new words. Results from 3 experiments support a developmental explanation. In Experiment 1, infants of 20 months learned to pair 2 phonetically similar words to 2 different objects under precisely the same conditions that infants of 14 months (Experiment 2) failed. In Experiment 3, infants of 17 months showed intermediate, but still successful, performance in the task. Vocabulary size predicted word‐learning performance, but only in the younger, less experienced word learners. The implications of these results for theories of word learning and lexical representation are discussed.     

Childhood Developmental Risk for Teen Childbearing in Britain

Adolescence is often assumed to be the most important period of life for understanding teen childbearing risk. Developmental perspectives challenge that assumption, offering the possibility that early childhood characteristics may have unique and lasting effects on the risk for teen childbearing. This study examined family life risk factors (socioeconomic status, family stress, and parental involvement in education) and how their effects on teen childbearing risk varied, depending on the childhood age at which they were experienced. Prospective life history data from the National Child Development Study of Great Britain were used to study a birth cohort of 4,928 British women, 15.3% of who became pregnant as teens. This study demonstrated that data from early childhood significantly contribute to the understanding of teen childbearing risk.     

Plaid designs for the evaluation of training for medical practitioners

The training of medical practitioners to improve the practitioner/patient relationship may be difficult, as limitations often exist on the choice of patients included in the study. A specific study of this type of training is given. It is proposed that a simple modification and generalization of Yates' plaid-square designs be used. It is shown that a replicated plaid-design incorporates as a special case the criss-cross or strip-plot design. The usefulness of these designs in studies of the training of medical practitioners is illustrated. The basic characteristics of their analysis are outlined.     

An alternative to model selection in ordinary regression

The weaknesses of established model selection procedures based on hypothesis testing and similar criteria are discussed and an alternative based on synthetic (composite) estimation is proposed. It is developed for the problem of prediction in ordinary regression and its properties are explored by simulations for the simple regression. Extensions to a general setting are described and an example with multiple regression is analysed. Arguments are presented against using a selected model for any inferences.     

Bayesian inference for a discretely observed stochastic kinetic model

The ability to infer parameters of gene regulatory networks is emerging as a key problem in systems biology. The biochemical data are intrinsically stochastic and tend to be observed by means of discrete-time sampling systems, which are often limited in their completeness. In this paper we explore how to make Bayesian inference for the kinetic rate constants of regulatory networks, using the stochastic kinetic Lotka-Volterra system as a model. This simple model describes behaviour typical of many biochemical networks which exhibit auto-regulatory behaviour. Various MCMC algorithms are described and their performance evaluated in several data-poor scenarios. An algorithm based on an approximating process is shown to be particularly efficient.