A probability weighting function w(p) for an objective probability p in decision under risk plays a pivotal role in Kahneman-Tversky’s prospect theory. Although recent studies in econophysics and neuroeconomics widely utilized probability weighting functions, psychophysical foundations of the probability weighting functions have been unknown. Notably, a behavioral economist Prelec (1998) axioma...

This short note points out an improvement on the robust stability analysis for electrically driven robots given in the paper. In the paper, the author presents a FAT-based direct adaptive control scheme for electrically driven robots in presence of nonlinearities associated with actuator input constraints. However, he offers not suitable stability analysis for the closed-loop system. In other w...

This short note points out an improvement on the robust stability analysis for electrically driven robots given in the paper. In the paper, the author presents a FAT-based direct adaptive control scheme for electrically driven robots in presence of nonlinearities associated with actuator input constraints. However, he offers not suitable stability analysis for the closed-loop system. In other w...

This contribution presents a Quality of Experience (QoE) framework. It evaluates QoE using QoS metrics, network feedback, and dynamic user requirements, and proposes a definition for QoE. A model for experimenting within the QoE framework is presented. It is believed that a better QoE can be achieved when the QoS and its interaction with the network and application layers is considered as a who...

Lookahead is a key issue in distributed discrete event simulation. It becomes explicit in conservative simulation algorithms, where the two major approaches are the asynchronous null-message (CMB) algorithms and the synchronous window algorithms (CTW). In this paper we demonstrate how a hybrid algorithm can maximize the lookahead capabilities of a model by lookahead accumulation. Furthermore, p...

We present a method to decrease the storage and communication complexity of the context-tree weighting method. This method is based on combining the estimated probability of a node in the context tree and weighted probabilities of its children in one single variable. This variable is represented by its logarithm.