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In contrast with expected utility theory, empirical findings indicate that decision-makers are sensitive to departures from reference points rather than states. Several tests of the reference-dependent preference framework have been carried out in experime ...
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 ...
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 ...
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 ...
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. Here, w ...
We introduce a class of learning problems where the agent is presented with a series of tasks. Intuitively, if there is a relation among those tasks, then the information gained during execution of one task has value for the execution of another task. Cons ...
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
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 ...
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 ...
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
