Nutrient deficiencies have been implicated in anti-social behaviour in schoolchildren; hence, correcting them may improve sociability. We therefore tested the effects of vitamin, mineral and n-3 supplementation on behaviour in a 12-week double-blind randomised placebocontrolled trial in typically developing UK adolescents aged 13-16 years (n 196). Changes in erythrocyte n-3 and 6 fatty acids and some mineral and vitamin levels were measured and compared with behavioural changes, using Conners' teacher ratings and school disciplinary records. At baseline, the children's PUFA (n-3 and n-6), vitamin and mineral levels were low, but they improved significantly in the group treated with n-3, vitamins and minerals (P = 0·0005). On the Conners disruptive behaviour scale, the group given the active supplements improved, whereas the placebo group worsened (F = 5·555, d = 0·35; P = 0·02). The general level of disciplinary infringements was low, thus making it difficult to obtain improvements. However, throughout the school term school disciplinary infringements increased significantly (by 25 %; Bayes factor = 115) in both the treated and untreated groups. However, when the subjects were split into high and low baseline infringements, the low subset increased their offences, whereas the high-misbehaviour subset appeared to improve after treatment. But it was not possible to determine whether this was merely a statistical artifact. Thus, when assessed using the validated and standardised Conners teacher tests (but less clearly when using school discipline records in a school where misbehaviour was infrequent), supplementary nutrition might have a protective effect against worsening behaviour.

If managers assume a normal or near-normal distribution of Information Technology (IT) project cost overruns, as is common, and cost overruns can be shown to follow a power-law distribution, managers may be unwittingly exposing their organizations to extreme risk by severely underestimating the probability of large cost overruns. In this research, we collect and analyze a large sample comprised of 5,392 IT projects to empirically examine the probability distribution of IT project cost overruns. Further, we propose and examine a mechanism that can explain such a distribution. Our results reveal that IT projects are far riskier in terms of cost than normally assumed by decision makers and scholars. Specifically, we found that IT project cost overruns follow a power-law distribution in which there are a large number of projects with relatively small overruns and a fat tail that includes a smaller number of projects with extreme overruns. A possible generative mechanism for the identified power-law distribution is found in interdependencies among technological components in IT systems. We propose and demonstrate, through computer simulation, that a problem in a single technological component can lead to chain reactions in which other interdependent components are affected, causing substantial overruns. What the power law tells us is that extreme IT project cost overruns will occur and that the prevalence of these will be grossly underestimated if managers assume that overruns follow a normal or near-normal distribution. This underscores the importance of realistically assessing and mitigating the cost risk of new IT projects up front.

In this paper we propose a novel approach for learning from data using rule based fuzzy inference systems where the model parameters are estimated using Bayesian inference and Markov Chain Monte Carlo (MCMC) techniques. We show the applicability of the method for regression and classification tasks using synthetic data-sets and also a real world example in the financial services industry. Then we demonstrate how the method can be extended for knowledge extraction to select the individual rules in a Bayesian way which best explains the given data. Finally we discuss the advantages and pitfalls of using this method over state-of-the-art techniques and highlight the specific class of problems where this would be useful.Index Terms-fuzzy logic; mamdani method; machine learning; MCMC.

Most cost-benefit analyses assume that the estimates of costs and benefits are more or less accurate and unbiased. But what if, in reality, estimates are highly inaccurate and biased? Then the assumption that cost-benefit analysis is a rational way to improve resource allocation would be a fallacy. Based on the largest dataset of its kind, we test the assumption that cost and benefit estimates of public investments are accurate and unbiased. We find this is not the case with overwhelming statistical significance. We document the extent of cost overruns, benefit shortfalls, and forecasting bias in public investments. We further assess whether such inaccuracies seriously distort effective resource allocation, which is found to be the case. We explain our findings in behavioral terms and explore their policy implications. Finally, we conclude that cost-benefit analysis of public investments stands in need of reform and we outline four steps to such reform.

In a recent paper [1] we introduced the Fuzzy Bayesian Learning (FBL) paradigm where expert opinions can be encoded in the form of fuzzy rule bases and the hyper-parameters of the fuzzy sets can be learned from data using a Bayesian approach. The present paper extends this work for selecting the most appropriate rule base among a set of competing alternatives, which best explains the data, by calculating the model evidence or marginal likelihood. We explain why this is an attractive alternative over simply minimizing a mean squared error metric of prediction and show the validity of the proposition using synthetic examples and a real world case study in the financial services sector.