Modeling External Risks in Project Management

To ascertain the viability of a project, undertake resource allocation, take part in bidding processes, and other related decisions, modern project management requires forecasting techniques for cost, duration, and performance of a project, not only under normal circumstances, but also under external events that might abruptly change the status quo. We provide a Bayesian framework that provides a global forecast of a project's performance. We aim at predicting the probabilities and impacts of a set of potential scenarios caused by combinations of disruptive events, and using this information to deal with project management issues. To introduce the methodology, we focus on a project's cost, but the ideas equally apply to project duration or performance forecasting. We illustrate our approach with an example based on a real case study involving estimation of the uncertainty in project cost while bidding for a contract.     

A comparison of recent nonparametric methods for testing effects in two-by-two factorial designs

The two-way two-levels crossed factorial design is a commonly used design by practitioners at the exploratory phase of industrial experiments. The F-test in the usual linear model for analysis of variance (ANOVA) is a key instrument to assess the impact of each factor and of their interactions on the response variable. However, if assumptions such as normal distribution and homoscedasticity of errors are violated, the conventional wisdom is to resort to nonparametric tests. Nonparametric methods, rank-based as well as permutation, have been a subject of recent investigations to make them effective in testing the hypotheses of interest and to improve their performance in small sample situations. In this study, we assess the performances of some nonparametric methods and, more importantly, we compare their powers. Specifically, we examine three permutation methods (Constrained Synchronized Permutations, Unconstrained Synchronized Permutations and Wald-Type Permutation Test), a rank-based method (Aligned Rank Transform) and a parametric method (ANOVA-Type Test). In the simulations, we generate datasets with different configurations of distribution of errors, variance, factor's effect and number of replicates. The objective is to elicit practical advice and guides to practitioners regarding the sensitivity of the tests in the various configurations, the conditions under which some tests cannot be used, the tradeoff between power and type I error, and the bias of the power on one main factor analysis due to the presence of effect of the other factor. A dataset from an industrial engineering experiment for thermoformed packaging production is used to illustrate the application of the various methods of analysis, taking into account the power of the test suggested by the objective of the experiment.     

Mathematical programming: Turing completeness and applications to software analysis

Mathematical programming is Turing complete, and can be used as a general-purpose declarative language. We present a new constructive proof of this fact, and showcase its usefulness by discussing an application to finding the hardest input of any given program running on a Minsky Register Machine. We also discuss an application of mathematical programming to software verification obtained by relaxing one of the properties of Turing complete languages.     

Irregular Immigration,Labour Market and Enforcement at the US‐Mexico Border. Evidence from a Time‐Series Analysis (1963–2014)

This article provides evidence on irregular immigration to the US from Mexico in the years 1963–2014. It aims to answer to two questions. The first is: how much is irregular immigration responsive to changes in the US labour market? Compared with legal immigration, irregular immigration seems to be more sensitive to short‐run economic cycles as it is not directly regulated by the policies of the destination country. Second, what effect does border enforcement have on irregular immigration? One of the proposals of the recent bill is the construction of a double layer fence on the Southern border. We do not know, however, to what extent a further increase in border security will prevent irregular immigration and at which economic cost. The contribution of this article is dual: first, it provides empirical evidence on irregular immigration to the US Second, it provides evidence about the effect of US border policies on irregular immigration.     

Short-Term GDP Forecasting With a Mixed-Frequency Dynamic Factor Model With Stochastic Volatility

In this article, we develop a mixed frequency dynamic factor model in which the disturbances of both the latent common factor and of the idiosyncratic components have time-varying stochastic volatilities. We use the model to investigate business cycle dynamics in the euro area and present three sets of empirical results. First, we evaluate the impact of macroeconomic releases on point and density forecast accuracy and on the width of forecast intervals. Second, we show how our setup allows to make a probabilistic assessment of the contribution of releases to forecast revisions. Third, we examine point and density out of sample forecast accuracy. We find that introducing stochastic volatility in the model contributes to an improvement in both point and density forecast accuracy. Supplementary materials for this article are available online.     

The role of the board in shaping foundations’ strategy: an empirical study

This paper investigates the relationships of the choice of philanthropic strategy with board capital (diversity and networks), board activities (board processes, internal board committees, and board effectiveness), and CEO leadership. Using a sample of 110 Italian foundations, the research shows that board processes have the strongest positive association with an evolved strategic approach to philanthropic institutional grant-giving, while board diversity and strong CEO leadership are associated with the strategic approach only under certain conditions. In particular, good governance processes (e.g., training the board, self-evaluation of trustees, setting the stage for effective board and committee meetings, implementing control software, and steering meetings to improve the board’s analysis) are positively associated with evolved strategic approaches to philanthropy (e.g., signaling other funders for the best grantees, improving the performance of grant recipients, and advancing selected social fields’ state of knowledge and practice).     

A note on the posterior inference for the Yule–Simon distribution

The Yule–Simon distribution has been out of the radar of the Bayesian community, so far. In this note, we propose an explicit Gibbs sampling scheme when a Gamma prior is chosen for the shape parameter. The performance of the algorithm is illustrated with simulation studies, including count data regression, and a real data application to text analysis. We compare our proposal to the frequentist counterparts showing better performance of our algorithm when a small sample size is considered.     

The relationship of trauma exposure to sex offending behavior among male juvenile offenders

The most common type of adult and juvenile sex offender treatment utilizes a Relapse Prevention (RP) model. In RP clients learn about their offense cycle with an emphasis on recognizing high-risk situations and negative emotional states that can be precursors or triggers to offending behavior. This study identifies ways that traumatic experiences and trauma-associated feelings can be offense triggers for juvenile sex offenders. Researchers interviewed the treating clinicians of 40 male juvenile sex offenders who received at least six months of RP sex offender treatment. Results showed that 95% of the youths had experienced a Post Traumatic Stress Disorder (PTSD) Criterion A traumatic event and that 65% met criteria for PTSD based on clinician judgments. Overall, clinicians identified prior trauma exposure as being related to the offense triggers in 85% of offenders. Specifically, the following trauma-related feelings were identified as offense triggers: intense fear in 37.5% of sex offenders, helplessness in 55%, and horror in 20%. Implications for sex offender treatment programs are discussed.