Majority Rule in a Stochastic Model of Bargaining

In this paper we consider multilateral stochastic bargaining models with general agreement rules. For n-player games where in each period a player is randomly selected to allocate a stochastic level of surplus and qNONCOOPERATIVE BARGAINING; VOTING RULES; STOCHASTIC GAMES

Modeling of convection phenomena in Bridgman-Stockbarger crystal growth

Thermal convection phenomena in a vertically oriented Bridgman-Stockbarger apparatus were modeled by computer simulations for different gravity conditions, ranging from earth conditions to extremely low gravity, approximate space conditions. The modeling results were obtained by the application of a state-of-the art, transient, multi-dimensional, completely densimetrically coupled, discrete-element computational model which was specifically developed for the simulation of flow, temperature, and species concentration conditions in two-phase (solid-liquid) systems. The computational model was applied to the simulation of the flow and the thermal conditions associated with the convection phenomena in a modified Germanium-Silicon charge enclosed in a stationary fused-silica ampoule. The results clearly indicated that the gravitational field strength influences the characteristics of the coherent vortical flow patterns, interface shape and position, maximum melt velocity, and interfacial normal temperature gradient

Legislative Bargaining with Heterogeneous Disagreement Values: Theory and Experiments

We study a legislative bargaining game in which failure to agree in a given round may result in a breakdown of negotiations. In that case, each player receives an exogenous `disagreement value'. We characterize the set of stationary subgame perfect equilibria under all q-majority rules. Under unanimity rule, equilibrium payoffs are strictly increasing in disagreement values. Under all less-than-unanimity rules, expected payoffs are either decreasing or first increasing and then decreasing in disagreement values. We conduct experiments involving three players using majority and unanimity rule, finding support for these predictions

DeepWAS: multivariate genotype-phenotype associations by directly integrating regulatory information using deep learning

Genome-wide association studies (GWAS) identify genetic variants associated with traits or diseases. GWAS never directly link variants to regulatory mechanisms. Instead, the functional annotation of variants is typically inferred by post hoc analyses. A specific class of deep learning-based methods allows for the prediction of regulatory effects per variant on several cell type-specific chromatin features. We here describe "DeepWAS", a new approach that integrates these regulatory effect predictions of single variants into a multivariate GWAS setting. Thereby, single variants associated with a trait or disease are directly coupled to their impact on a chromatin feature in a cell type. Up to 61 regulatory SNPs, called dSNPs, were associated with multiple sclerosis (MS, 4,888 cases and 10,395 controls), major depressive disorder (MDD, 1,475 cases and 2,144 controls), and height (5,974 individuals). These variants were mainly non-coding and reached at least nominal significance in classical GWAS. The prediction accuracy was higher for DeepWAS than for classical GWAS models for 91% of the genome-wide significant, MS-specific dSNPs. DSNPs were enriched in public or cohort-matched expression and methylation quantitative trait loci and we demonstrated the potential of DeepWAS to generate testable functional hypotheses based on genotype data alone. DeepWAS is available at https://github.com/cellmapslab/DeepWAS