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Decision making is of crucial interest in many disciplines such as psychology, neuroscience, economics and machine learning. Binary perceptual decision theories relate to situations where an observer (or machine) is confronted with one of two possible nois ...
We consider the problem of distributed classification of multiple observations of the same object that are collected in an ad hoc network of vision sensors. Assuming that each sensor captures a different observation of the same object, the problem is to cl ...
Institute of Electrical and Electronics Engineers2011
An epidemic spreading in a network calls for a decision on the part of the network members: They should decide whether to protect themselves or not. Their decision depends on the trade off between their perceived risk of being infected and the cost of bein ...
Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2011
Decisions about noisy stimuli require evidence integration over time. Traditionally, evidence integration and decision making are described as a one-stage process: a decision is made when evidence for the presence of a stimulus crosses a threshold. However ...
Disappointment, the emotion experienced when faced to reward prediction errors (RPEs), considerably impacts decision making (DM). Individuals tend to modify their behavior in an often unpredictable way just to avoid experiencing negative emotions. Despite ...
Since the performance of a system of interacting peers depends strongly on their individual contributions, the ratio between their provided and consumed resources should be set in line with the social welfare improvement, without relying on a central coord ...
Supply chains (SC) are complex systems in which human beings play a key role. Whereas human impact is often taken into account in simulation models in terms of physical flows (material handling, fabrication), it is far less the case in terms of information ...
Stochastic programming and robust optimization are disciplines concerned with optimal decision-making under uncertainty over time. Traditional models and solution algorithms have been tailored to problems where the order in which the uncertainties unfold i ...
The design and analysis of efficient approximation schemes are of fundamental importance in stochastic programming research. Bounding approximations are particularly popular for providing strict error bounds that can be made small by using partitioning tec ...
We propose a fast and efficient method for Content Based Image Retrieval (CBIR) which uses color densities within concentric circular zones of the image, encompassing edge-pixels. This method is invariant to Rotation, Scale and Translation (RST). Small-siz ...
