Models of affective decision-making: how do feelings predict choice?

Intuitively, how we feel about potential outcomes will determine our decisions. Indeed, one of the most influential theories in psychology, Prospect Theory, implicitly assumes that feelings govern choice. Surprisingly, however, we know very little about the rules by which feelings are transformed into decisions. Here, we characterize a computational model that uses feelings to predict choice. We reveal that this model predicts choice better than existing value-based models, showing a unique contribution of feelings to decisions, over and above value. Similar to Prospect Theory value function, feelings showed diminished sensitivity to outcomes as value increased. However, loss aversion in choice was explained by an asymmetry in how feelings about losses and gains were weighed when making a decision, not by an asymmetry in the feelings themselves. The results provide new insights into how feelings are utilized to reach a decision

PDB8 TYPE-2 DIABETES AND BODY MASS INDEX (BMI):WHAT CAN WE LEARN FROM A LONGITUDINAL DATABASE STUDY?

Farrando, Jordi;Febles, Maria Dolors ;Henrich, Jordi;Tarrasó, Olga ;Fuertes, J.C.;Pérez, S

Do the generalized polynomial chaos and Fröbenius methods retain the statistical moments of random differential equations?

The aim of this paper is to explore whether the generalized polynomial chaos (gPC) and random Fröbenius methods preserve the first three statistical moments of random differential equations. There exist exact solutions only for a few cases, so there is a need to use other techniques for validating the aforementioned methods in regards to their accuracy and convergence. Here we present a technique for indirectly study both methods. In order to highlight similarities and possible differences between both approaches, the study is performed by means of a simple but still illustrative test-example involving a random differential equation whose solution is highly oscillatory. This comparative study shows that the solutions of both methods agree very well when the gPC method is developed in terms of the optimal orthogonal polynomial basis selected according to the statistical distribution of the random input. Otherwise, we show that results provided by the gPC method deteriorate severely. A study of the convergence rates of both methods is also included.This work has been partially supported by the Spanish M.C.Y.T. grants MTM2009-08587, DPI2010-20891-C02-01 as well as the Universitat Politecnica de Valencia grants PAID06-11 (ref. 2070) and PAID00-11 (ref. 2753).Chen Charpentier, BM.; Cortés López, JC.; Romero Bauset, JV.; Roselló Ferragud, MD. (2013). Do the generalized polynomial chaos and Fröbenius methods retain the statistical moments of random differential equations?. Applied Mathematics Letters. 26(5):553-558. doi:10.1016/j.aml.2012.12.013S55355826

Solving Riccati time-dependent models with random quadratic coefficient

This paper deals with the construction of approximate solutions of a random logistic differential equation whose nonlinear coefficient is assumed to be an analytic stochastic process and the initial condition is a random variable. Applying p-mean stochastic calculus, the nonlinear equation is transformed into a random linear equation whose coefficients keep analyticity. Next, an approximate solution of the nonlinear problem is constructed in terms of a random power series solution of the associate linear problem. Approximations of the average and variance of the solution are provided. The proposed technique is illustrated through an example where comparisons with respect to Monte Carlo simulations are shown. © 2011 Elsevier Ltd. All rights reserved.This work has been partially supported by the Spanish M.C.Y.T. grants MTM2009-08587, DPI2010-20891-C02-01, Universitat Politecnica de Valencia grant PAID06-09-2588 and Mexican Conacyt.Cortés López, JC.; Jódar Sánchez, LA.; Company Rossi, R.; Villafuerte Altuzar, L. (2011). Solving Riccati time-dependent models with random quadratic coefficient. Applied Mathematics Letters. 24(12):2193-2196. https://doi.org/10.1016/j.aml.2011.06.024S21932196241

Control and Monitoring of the Online Computer Farm for Offline Processing in LHCb

ISBN 978-3-95450-139-7 - http://accelconf.web.cern.ch/AccelConf/ICALEPCS2013/papers/tuppc063.pdfInternational audienceLHCb, one of the 4 experiments at the LHC accelerator at CERN, uses approximately 1500 PCs (averaging 12 cores each) for processing the High Level Trigger (HLT) during physics data taking. During periods when data acquisition is not required most of these PCs are idle. In these periods it is possible to profit from the unused processing capacity to run offline jobs, such as Monte Carlo simulation. The LHCb offline computing environment is based on LHCbDIRAC (Distributed Infrastructure with Remote Agent Control). In LHCbDIRAC, job agents are started on Worker Nodes, pull waiting tasks from the central WMS (Workload Management System) and process them on the available resources. A Control System was developed which is able to launch, control and monitor the job agents for the offline data processing on the HLT Farm. This control system is based on the existing Online System Control infrastructure, the PVSS SCADA and the FSM toolkit. It has been extensively used launching and monitoring 22.000+ agents simultaneously and more than 850.000 jobs have already been processed in the HLT Farm. This paper describes the deployment and experience with the Control System in the LHCb experiment