An ontological framework for cooperative games

Social intelligence is an emerging property of a system composed of agents that consists of the ability of this system to conceive, design, implement and execute strategies to solve problems and thus achieve a collective state of the system that is concurrently satisfactory for all and each one of the agents that compose it. In order to make decisions when dealing with complex problems related to social systems and take advantage of social intelligence, cooperative games theory constitutes the standard theoretical framework. In the present work, an ontological framework for cooperative games modeling and simulation is presented

A new risk history: The Eastern Europe case

Eastern European Emerging Markets (EEEM's) have been superficially analysed in the literature. In this paper, the authors use a T-GARCH and E-GARCH approach to model volatility in eleven EEEM's, being one of the most comprehensive analysis in terms of number of markets. Data includes daily returns from 2004 to 2011. Main findings show higher unconditional volatility in EEEM's than in developed markets, but risk premium is statistically negative or non significant in this markets. Almost all markets show an important and significant leverage effect, contrary to previous results in the literature. According to the news impact and decay parameters, volatility is more difficult to predict in EEEM's than in developed markets. Greece, Hungary, Poland and Turkey seem to be the maturest EEEM's markets. Finally, no significant differences are found among countries inside and outside European Union

Type 1 diabetes: Developing the first risk-estimation model for predicting silent myocardial ischemia. The potential role of insulin resistance

OBJECTIVES: The aim of the study was to develop a novel risk estimation model for predicting silent myocardial ischemia (SMI) in patients with type 1 diabetes (T1DM) and no clinical cardiovascular disease, evaluating the potential role of insulin resistance in such a model. Additionally, the accuracy of this model was compared with currently available models for predicting clinical coronary artery disease (CAD) in general and diabetic populations. RESEARCH, DESIGN AND METHODS: Patients with T1DM (35-65years, >10-year duration) and no clinical cardiovascular disease were consecutively evaluated for: 1) clinical and anthropometric data (including classical cardiovascular risk factors), 2) insulin sensitivity (estimate of glucose disposal rate (eGDR)), and 3) SMI diagnosed by stress myocardial perfusion gated SPECTs. RESULTS: Eighty-four T1DM patients were evaluated [50.1±9.3 years, 50% men, 36.9% active smokers, T1DM duration: 19.0(15.9-27.5) years and eGDR 7.8(5.5-9.4)mg·kg-1·min-1]. Of these, ten were diagnosed with SMI (11.9%). Multivariate logistic regression models showed that only eGDR (OR = -0.593, p = 0.005) and active smoking (OR = 7.964, p = 0.018) were independently associated with SMI. The AUC of the ROC curve of this risk estimation model for predicting SMI was 0.833 (95%CI:0.692-0.974), higher than those obtained with the use of currently available models for predicting clinical CAD (Framingham Risk Equation: 0.833 vs. 0.688, p = 0.122; UKPDS Risk Engine (0.833 vs. 0.559; p = 0.001) and EDC equation: 0.833 vs. 0.558, p = 0.027). CONCLUSION: This study provides the first ever reported risk-estimation model for predicting SMI in T1DM. The model only includes insulin resistance and active smoking as main predictors of SMI