Learning and Visceral Temptation in Dynamic Saving Experiments

This paper tests two explanations for apparent undersaving in life cycle models: bounded rationality and a preference for immediacy. Each was addressed in a separate experimental study. In the first study, subjects saved too little initially—providing evidence for bounded rationality—but learned to save optimally within four repeated life cycles. In the second study, thirsty subjects who consume beverage sips immediately, rather than with a delay, show greater relative overspending, consistent with quasi-hyperbolic discounting models. The parameter estimates of overspending obtained from the second study, but not the first, are in range of several empirical studies of saving (with an estimated β = 0.6–0.7)

Learning and Visceral Temptation in Dynamic Savings Experiments

In models of optimal savings with income uncertainty and habit formation, people should save early to create a buffer stock, to cushion bad income draws and limit the negative internality from habit formation. In experiments in this setting, people save too little initially, but learn to save optimally within four repeated lifecycles, or 1-2 lifecycles with “social learning.” Using beverage rewards (cola) to create visceral temptation, thirsty subjects who consume immediately overspend compared to subjects who only drink after time delay. The relative overspending of immediate-consumption subjects is consistent with hyperbolic discounting and dual-self models. Estimates of the present-bias choices are β=0.6-0.7, which are consistent with other studies (albeit over different time horizons)

CONSUMER SELECTION OF RETAIL OUTLETS IN BUYING PECANS

The study identifies differences in consumer characteristics and the selection of the type of a retail outlet in pecan purchases. Within the framework of utility maximization, an empirical model is specified and estimated using multinomial logit. The estimation is based on data collected through a nationwide survey. Calculated marginal probabilities show the importance of age, household income, and household size among the important consumer characteristics that influence the selection of a retail outlet. Employment and the timing of pecan purchases also influence the use of a specific type of retail outlet. In particular, mail-order purchases are made by older persons with higher incomes and larger households in comparison to purchases at grocery stores or other outlets. The study provides knowledge needed to improve marketing strategies for different outlets and suggests that various strategies can be developed to reach different groups of pecan buyers by type of retail outlet.Consumer/Household Economics,

Cosine Measures of Neutrosophic Cubic Sets for Multiple Attribute Decision-Making

The neutrosophic cubic set can contain much more information to express its interval neutrosophic numbers and single-valued neutrosophic numbers simultaneously in indeterminate environments. Hence, it is a usual tool for expressing much more information in complex decision-making problems

Multi-frame Image Super-resolution Reconstruction Using Multi-grained Cascade Forest

Super-resolution image reconstruction utilizes two algorithms, where one is for single-frame image reconstruction, and the other is for multi-frame image reconstruction. Single-frame image reconstruction generally takes the first degradation and is followed by reconstruction, which essentially creates a problem of insufficient characterization. Multi-frame images provide additional information for image reconstruction relative to single frame images due to the slight differences between sequential frames. However, the existing super-resolution algorithm for multi-frame images do not take advantage of this key factor, either because of loose structure and complexity, or because the individual frames are restored poorly. This paper proposes a new SR reconstruction algorithm for images using Multi-grained Cascade Forest. Multi-frame image reconstruction is processed sequentially. Firstly, the image registration algorithm uses a convolutional neural network to register low-resolution image sequences, and then the images are reconstructed after registration by the Multi-grained Cascade Forest reconstruction algorithm. Finally, the reconstructed images are fused. The optimal algorithm is selected for each step  to get the most out of the details and tightly connect the internal logic of each sequential step.This novel approach proposed in this paper, in which the depth of the cascade forest is procedurally generated for recovered images, rather than being a constant. After training each layer, the recovered image is automatically evaluated, and new layers are constructed for training until an optimal restored image is obtained. Experiments show that this method improves the quality of image reconstruction while preserving the details of the image