Quadrature Strategies for Constructing Polynomial Approximations

Finding suitable points for multivariate polynomial interpolation and approximation is a challenging task. Yet, despite this challenge, there has been tremendous research dedicated to this singular cause. In this paper, we begin by reviewing classical methods for finding suitable quadrature points for polynomial approximation in both the univariate and multivariate setting. Then, we categorize recent advances into those that propose a new sampling approach and those centered on an optimization strategy. The sampling approaches yield a favorable discretization of the domain, while the optimization methods pick a subset of the discretized samples that minimize certain objectives. While not all strategies follow this two-stage approach, most do. Sampling techniques covered include subsampling quadratures, Christoffel, induced and Monte Carlo methods. Optimization methods discussed range from linear programming ideas and Newton's method to greedy procedures from numerical linear algebra. Our exposition is aided by examples that implement some of the aforementioned strategies

Risky Punishment and Reward in the Prisoner’s Dilemma

We conduct a prisoner’s dilemma experiment with a punishment/reward stage, where punishments and rewards are risky. This is compared with a risk free treatment. We find that subjects do not change their behavior in the face of risky outcomes. Additionally, we measure risk attitude and the emotions of subjects. While we find a strong influence of emotions, individual risk aversion has no effect on the decision to punish or reward. This is good news for lab experiments who abstract from risky outcomes. From the perspective of social preferences, our results provide evidence for risk neutral inclusion of other player’s payoffs in the decisionmaker’s utility function

Data for: Interactive Ellsberg Tasks: An Experiment

Raw data for paper: Interactive Ellsberg Tasks: An Experimen

Data for: Interactive Ellsberg Tasks: An Experiment

Raw data for paper: Interactive Ellsberg Tasks: An ExperimentTHIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV

Alfalfa hay crop loss due to mule deer depredation

To define alfalfa crop loss from depredating mule deer, the spotlight count and paired plot techniques were applied in 12 fields located throughout Utah. Protected and grazed plots were used to determine alfalfa loss. A significant relationship between deer-nights of grazing and alfalfa loss was determined. Based on our studies, we recommend using 2.4 kg/deer-night for mule deer depredation of alfalfa using the spotlight count assessment technique. Nutritional quality of alfalfa was not different between grazed and protected plots.The Journal of Range Management archives are made available by the Society for Range Management and the University of Arizona Libraries. Contact [email protected] for further information.Migrated from OJS platform August 202

Sick pay provision in experimental labor markets

Sick pay is a common provision in most labor contracts. This paper employs an experimental gift exchange environment to explore two related questions using both managers and undergraduates as subjects. First, do workers reciprocate generous sick pay with higher effort? Second, do firms benefit from offering sick pay? Our main finding is that workers do reciprocate generous sick pay with higher effort. However, firms benefit from offering sick pay in terms of profits only if there is competition among firms for workers. Consequently, competition leads to a higher voluntary provision of sick pay relative to a monopsonistic labor market