Factored Online Planning in Many-Agent POMDPs

In centralized multi-agent systems, often modeled as multi-agent partially observable Markov decision processes (MPOMDPs), the action and observation spaces grow exponentially with the number of agents, making the value and belief estimation of single-agent online planning ineffective. Prior work partially tackles value estimation by exploiting the inherent structure of multi-agent settings via so-called coordination graphs. Additionally, belief estimation methods have been improved by incorporating the likelihood of observations into the approximation. However, the challenges of value estimation and belief estimation have only been tackled individually, which prevents existing methods from scaling to settings with many agents. Therefore, we address these challenges simultaneously. First, we introduce weighted particle filtering to a sample-based online planner for MPOMDPs. Second, we present a scalable approximation of the belief. Third, we bring an approach that exploits the typical locality of agent interactions to novel online planning algorithms for MPOMDPs operating on a so-called sparse particle filter tree. Our experimental evaluation against several state-of-the-art baselines shows that our methods (1) are competitive in settings with only a few agents and (2) improve over the baselines in the presence of many agents.Comment: Extended version (includes the Appendix) of the paper accepted at AAAI-2

Publication rates of editorial board members in oral health journals

The aim of this study was to measure the publication rate of editorial board members in their board journals and to evaluate associated variables. We evaluated the ten highest-ranked journals according to the 5-year impact factor under ‘Dentistry, Oral Surgery & Medicine’ subject category for 2010, 2011, and 2012. All original research papers with at least one member of the editorial board as author were counted. Final analyses assessed associated variables such as size of the editorial board, number of papers published each year, and each journal’s impact factor. Overall, there was an increase in the average number of articles published from 2010 (115.2 ± 52.2) to 2012 (134.7 ± 47.4). The number and percentage of articles published with editorial board members as authors over the three years did not follow the same pattern, with a slight decrease from 2010 to 2011 and an increase in 2012. The number of articles with editorial board members as authors was significantly higher for journals with impact factors ≥4.0. Journals with a higher impact factor and larger editorial board were associated with higher chances of editorial board members publishing in their respective journals. Participation of editorial board members as authors in publishing varies significantly among journals

Economic predictors of differences in interview faking between countries : economic inequality matters, not the state of economy

Many companies recruit employees from different parts of the globe, and faking behavior by potential employees is a ubiquitous phenomenon. It seems that applicants from some countries are more prone to faking compared to others, but the reasons for these differences are largely unexplored. This study relates country-level economic variables to faking behavior in hiring processes. In a cross-national study across 20 countries, participants (N = 3839) reported their faking behavior in their last job interview. This study used the random response technique (RRT) to ensure participants anonymity and to foster honest answers regarding faking behavior. Results indicate that general economic indicators (gross domestic product per capita [GDP] and unemployment rate) show negligible correlations with faking across the countries, whereas economic inequality is positively related to the extent of applicant faking to a substantial extent. These findings imply that people are sensitive to inequality within countries and that inequality relates to faking, because inequality might actuate other psychological processes (e.g., envy) which in turn increase the probability for unethical behavior in many forms

Multidimensional Signals and Analytic Flexibility: Estimating Degrees of Freedom in Human-Speech Analyses

Recent empirical studies have highlighted the large degree of analytic flexibility in data analysis that can lead to substantially different conclusions based on the same data set. Thus, researchers have expressed their concerns that these researcher degrees of freedom might facilitate bias and can lead to claims that do not stand the test of time. Even greater flexibility is to be expected in fields in which the primary data lend themselves to a variety of possible operationalizations. The multidimensional, temporally extended nature of speech constitutes an ideal testing ground for assessing the variability in analytic approaches, which derives not only from aspects of statistical modeling but also from decisions regarding the quantification of the measured behavior. In this study, we gave the same speech-production data set to 46 teams of researchers and asked them to answer the same research question, resulting in substantial variability in reported effect sizes and their interpretation. Using Bayesian meta-analytic tools, we further found little to no evidence that the observed variability can be explained by analysts’ prior beliefs, expertise, or the perceived quality of their analyses. In light of this idiosyncratic variability, we recommend that researchers more transparently share details of their analysis, strengthen the link between theoretical construct and quantitative system, and calibrate their (un)certainty in their conclusions