because of the paramount importance of job satisfaction and due to its main consequences such as reduction of work absence and resignation, personnel promotion and society’s health, and more importantly, its role in achievement of organization goals, this study aimed at investigating the effects of six social factors including personnel’s belief, salary and benefits, participation in organizati...

The effect of learning on climate policy is not straightforward when climate policy is concerned. It depends not only on the ways that climate feedbacks, preferences, and economic impacts are considered, but also on the ways that uncertainty and learning are introduced. Deep (or fat-tailed) uncertainty does matter for the optimal climate policy in that it requires more stringent efforts to redu...

A fundamental problem faced by animals is learning to select actions based on noisy sensory information and incomplete knowledge of the world. It has been suggested that the brain engages in Bayesian inference during perception but how such probabilistic representations are used to select actions has remained unclear. Here we propose a neural model of action selection and decision making based ...

This paper presents an algorithm for building decision trees in an uncertain environment. Our algorithm will use the theory of belief functions in order to represent the uncertainty about the parameters of the classification problem. Our method will be concerned with both the decision tree building task and the classification task.

This experiment tested the ability of 81 adult subjects to make a decision on a simple nonverbal false-belief reasoning task while concurrently either shadowing prerecorded spoken dialogue or tapping along with a rhythmic shadowing track. Our results showed that the verbal task, but not tapping, significantly disrupted false-belief reasoning, suggesting that language plays a key role in working...

Most decisions in life are gambles. Should I speed up or slow down as I approach the yellow traffic light ahead? Should I invest in the stock market or in treasury bills? Should I undergo surgery or radiation therapy to treat my tumor? From mundane choices rendered with scarcely a moment’s reflection to urgent decisions founded on careful deliberation, we seldom know in advance and with certain...

In this paper we propose a new general method for decisionmaking under uncertainty based on the belief interval distance. We show through several simple illustrative examples how this method works and its ability to provide reasonable results.

Simplifying and structuring qualitatively complex knowledge, quantifying it in a certain way to make it reusable and easily accessible are all aspects that are not new to historians. Computer science is currently approaching a solution to some of these problems, or at least making it easier to work with historical data. In this paper, we propose a historical knowledge representation model takin...

This paper establishes connections between logical equivalences and bisimulation relations for hidden Markov models (HMM). Both standard and belief state bisimulations are considered. We also present decision algorithms for the bisimilarities. For standard bisimilarity, an extension of the usual partition refinement algorithm is enough. Belief bisimilarity, being a relation on the continuous sp...

For centuries, theorists have focused almost exclusively on the use of additive utility representations in attempts to describe decision behavior. This basic framework has constantly been modified in order to account for challenging empirical results. However, each revision to the basic theory has in turn consistently been confronted with conflicting evidence. Here, we summarize an alternative ...