Contingencies versus External Pressure: Professionalization in Boards of Firms Affiliated to Family Business Groups in Late‐Industrializing Countries

We examine the antecedents of professionalization in boards of firms affiliated to family business groups, increasingly recognized in the literature as the dominant form of big business organization in many late‐industrializing countries. Dimensions of board professionalization that we include in our study are board size, ratio of salaried executives and outsider presence. We compare predictions on board composition derived from contingency, institutional and power perspectives. Turkish family business groups, considered as an archetypal example of this form of organization, provide the empirical setting for the study, with data on 299 firms affiliated to ten different family business groups. Our results provide greater support for institutional and power perspectives, showing that, relative to internal and external complexity facing affiliate firms, institutional pressures and the presence of joint venture partners better predict board professionalization.     

INCIDENCE AND CONSEQUENCES OF RISK‐TAKING BEHAVIOR IN TOURNAMENTS—EVIDENCE FROM THE NBA

We empirically explore the relevance and efficiency of risk‐taking behavior in tournaments. We use data from the National Basketball Association (NBA) and measure risk‐taking by the fraction of three‐point shots in basketball games. We examine how point differences between teams during games affect their subsequent risk‐taking behavior. It is found that teams that are trailing are more likely to increase their use of three‐point shots. We additionally analyze the consequences of this change in behavior. Enhanced risk‐taking is inefficient in the vast majority of cases and is only beneficial if a team is trailing by a rather large margin. We discuss possible explanations for these decision errors. (JEL M5, D81, J30)     

A Bayesian discovery procedure

Summary.  We discuss a Bayesian discovery procedure for multiple-comparison problems. We show that, under a coherent decision theoretic framework, a loss function combining true positive and false positive counts leads to a decision rule that is based on a threshold of the posterior probability of the alternative. Under a semiparametric model for the data, we show that the Bayes rule can be approximated by the optimal discovery procedure, which was recently introduced by Storey. Improving the approximation leads us to a Bayesian discovery procedure, which exploits the multiple shrinkage in clusters that are implied by the assumed non-parametric model. We compare the Bayesian discovery procedure and the optimal discovery procedure estimates in a simple simulation study and in an assessment of differential gene expression based on microarray data from tumour samples. We extend the setting of the optimal discovery procedure by discussing modifications of the loss function that lead to different single-thresholding statistics. Finally, we provide an application of the previous arguments to dependent (spatial) data.     

On the Complexity of Constructing Evolutionary Trees

In this paper we study a few important tree optimization problems with applications to computational biology. These problems ask for trees that are consistent with an as large part of the given data as possible. We show that the maximum homeomorphic agreement subtree problem cannot be approximated within a factor of , where N is the input size, for any 0 in polynomial time unless P = NP, even if all the given trees are of height 2. On the other hand, we present an O(N log N)-time heuristic for the restriction of this problem to instances with O(1) trees of height O(1) yielding solutions within a constant factor of the optimum. We prove that the maximum inferred consensus tree problem is NP-complete, and provide a simple, fast heuristic for it yielding solutions within one third of the optimum. We also present a more specialized polynomial-time heuristic for the maximum inferred local consensus tree problem.     

Die Rolle von Psycholog/innen in den Medien

The role of psychologists in the mediaDiscribing the phenomena of frequently asking journalists the authors are trying to describe the social meaning of role-and functiontaking of the science of psychology in the public discussion. They are following the question whether it could be possible to publish scientific results without banalizing, taking part in voyerism and reprehensible emotion creating.     

A Note on Exponentially Smoothed Seasonal Differences

Filtering economic time series can be justified if variations within a certain frequency interval are relevant for the problem at hand. It is shown here that exponential smoothing of seasonal differences provides a simple means of damping short oscillations, including seasonal ones.     

Information, information systems, information society: interpretations and implications

The term “information” has become a universal and omnipresent keyword in almost all areas of our modern world—be it in science or society in general. This is not only obvious from the naming of whole scientific branches like Information Theory, Information Science or Informatics but even more from common speaking—characterising our present time and society as information age viz. information society. However, what “information” might mean, is by no means clear and there is a wide range of interpretations covering, among others, its technical, communicational, educational, mental, and scientific aspects. But is the use of the same term justified when adopted in Biology, Physics, Archaeology, Law, Communication Technology, and Informatics (to list just a few of the involved scientific branches) or do its different uses at least have some common characteristics—some sort of common denominator? Is information natural, e.g. manifesting itself as a material phenomenon residing in organisms, stars, atoms, or genes, or is it just a cultural product of human communication, thinking, and interpretation? In this article, we try to clarify some of the most important interpretations, discuss and contrast them with the Informatics point of view. Interpretations range from taking information as material, transferable signals (following Shannon’s Information Theory or the genetic approaches), treating it as a sign (following a semiotic approach), as a commercial product (now common in Web-based Information Business) to considering it a pure mental phenomenon bound to humans or human-like individuals or even to groups and societies. Based on these interpretations, we shall throw a critical glance on current trends in human science and society—focusing on the now popular concept of “information society”—and then derive some theses and guidelines for further research escorting the growth and dispersal of information technology. As it will turn out, an information society which defines itself through the number of computers, internet connections and network links is based on a very narrow, techno-centric concept of information. However, a reflection on the educational and cultural aspects of information might lead to a better-qualified society consisting of responsible and critical citizens.     

Transformations for Smooth Regression Models with Multiplicative Errors

We consider whether one should transform to estimate nonparametrically a regression curve sampled from data with a constant coefficient of variation, i.e. with multiplicative errors. Kernel-based smoothing methods are used to provide curve estimates from the data both in the original units and after transformation. Comparisons are based on the mean-squared error (MSE) or mean integrated squared error (MISE), calculated in the original units. Even when the data are generated by the simplest multiplicative error model, the asymptotically optimal MSE (or MISE) is surprisingly not always obtained by smoothing transformed data, but in many cases by directly smoothing the original data. Which method is optimal depends on both the regression curve and the distribution of the errors. Data-based procedures which could be useful in choosing between transforming and not transforming a particular data set are discussed. The results are illustrated on simulated and real data.     

Health Risks of Energy Systems

Health risks from fossil, renewable and nuclear reference energy systems are estimated following a detailed impact pathway approach. Using a set of appropriate air quality models and exposure-effect functions derived from the recent epidemiological literature, a methodological framework for risk assessment has been established and consistently applied across the different energy systems, including the analysis of consequences from a major nuclear accident. A wide range of health impacts resulting from increased air pollution and ionizing radiation is quantified, and the transferability of results derived from specific power plants to a more general context is discussed.     

Semiparametric Bayesian classification with longitudinal markers

Summary.  We analyse data from a study involving 173 pregnant women. The data are observed values of the β human chorionic gonadotropin hormone measured during the first 80 days of gestational age, including from one up to six longitudinal responses for each woman. The main objective in this study is to predict normal versus abnormal pregnancy outcomes from data that are available at the early stages of pregnancy. We achieve the desired classification with a semiparametric hierarchical model. Specifically, we consider a Dirichlet process mixture prior for the distribution of the random effects in each group. The unknown random-effects distributions are allowed to vary across groups but are made dependent by using a design vector to select different features of a single underlying random probability measure. The resulting model is an extension of the dependent Dirichlet process model, with an additional probability model for group classification. The model is shown to perform better than an alternative model which is based on independent Dirichlet processes for the groups. Relevant posterior distributions are summarized by using Markov chain Monte Carlo methods.