Average vs. Marginal Risk Aversion: Reconciling Simultaneously Risk Averse and Risk Loving Behavior

Risk and Uncertainty,

Combining Consumer Valuation Research with Sensory Science Techniques: A Laboratory Experiment

In this research, we integrated an experimental auction with sensory science techniques—namely, trained sensory panels used to analyze the sensory attributes of wines—to examine the effects of objective and sensory information in the market for California-produced Cabernet Sauvignons. The experiment permitted observation of consumer valuation for sensory attributes of wine, appellations, expert ratings, and wineries. Participants submitted bids each time they received new information about the wines. The balanced experimental design permits evaluation of the effects of consumer characteristics on attribute valuation. We had 236 people participate in the research, which consisted of nine rounds of bidding and one round of hedonic liking scores. Rounds 5-9 repeated the structure of information released in rounds 1-4, but added sensory information, yielding 472 observations for each type of information (e.g. appellation, expert rating, winery). We obtain a total of 8496 valuations, or bids and 944 hedonic “liking” ratings, as well as demographic information, wine consumption data, and a wine knowledge score for each consumer. The results of the research agree with many of the previously held notions about valuation of wine by consumers. Participants value Cabernet Sauvignons from Napa Valley and Sonoma County and their sub-appellations more than wines labeled with the California appellation. Bids for wines rated by experts such as the Wine Advocate (Robert Parker) or Wine Spectator increased as the experts’ ratings increased. However, we also find that consumer characteristics are very important in explaining WTP for wine attributes. The contributions of prestigious appellations to the value of Cabernet Sauvignons depended on consumer characteristics. Willingness to pay was highly correlated with sensory evaluation, but even after tasting the wine, appellation and expert ratings still mattered for WTP. Overall, the research describes a significant amount of heterogeneity in the preferences for sensory characteristics of wine, and that individual characteristics systematically explain many of the differences in valuation of wine attributes.Experimental Economics, Willingness to Pay, WTP, Wine, Consumer Valuation, Hedonic Pricing, Sensory Analysis, Demand and Price Analysis, Food Consumption/Nutrition/Food Safety, Marketing, Research Methods/ Statistical Methods,

Amphibians and reptiles of C. E. Miller Ranch and the Sierra Vieja, Chihuahuan Desert, Texas, USA

We report the occurrence of 50 species of amphibians and reptiles recently collected on C. E. Miller Ranch and the Sierra Vieja in the Chihuahuan Desert of Texas, USA and describe their perceived distribution and abundance across various habitat associations of the region. Our recent surveys follow intense, historic sampling of amphibians and reptiles from this region in 1948. Of the 50 species detected in recent surveys, six were not collected in 1948 and an additional three species documented in 1948 have yet to be detected in a 14-year period of recent surveys. Combining data from both historic and recent surveys, a total of 53 species of amphibians and reptiles are known from the ranch (11 amphibians, 42 reptiles). Land stewardship and conservation practices have likely contributed to the persistence of the majority of these species through time. Additionally, we discuss the status of amphibians and reptiles not collected during recent surveys and comment on potential species that have not yet been detected

Visualizing genetic constraints

Principal Components Analysis (PCA) is a common way to study the sources of variation in a high-dimensional data set. Typically, the leading principal components are used to understand the variation in the data or to reduce the dimension of the data for subsequent analysis. The remaining principal components are ignored since they explain little of the variation in the data. However, evolutionary biologists gain important insights from these low variation directions. Specifically, they are interested in directions of low genetic variability that are biologically interpretable. These directions are called genetic constraints and indicate directions in which a trait cannot evolve through selection. Here, we propose studying the subspace spanned by low variance principal components by determining vectors in this subspace that are simplest. Our method and accompanying graphical displays enhance the biologist's ability to visualize the subspace and identify interpretable directions of low genetic variability that align with simple directions.Comment: Published in at http://dx.doi.org/10.1214/12-AOAS603 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Grain Physics and Rosseland Mean Opacities

Tables of mean opacities are often used to compute the transfer of radiation in a variety of astrophysical simulations from stellar evolution models to proto-planetary disks. Often tables, such as Ferguson et al. (2005), are computed with a predetermined set of physical assumptions that may or may not be valid for a specific application. This paper explores the effects of several assumptions of grain physics on the Rosseland mean opacity in an oxygen rich environment. We find that changing the distribution of grain sizes, either the power-law exponent or the shape of the distribution, has a marginal effect on the total mean opacity. We also explore the difference in the mean opacity between solid homogenous grains and grains that are porous or conglomorations of several species. Changing the amount of grain opacity included in the mean by assuming a grain-to-gas ratio significantly affects the mean opacity, but in a predictable way.Comment: 19 pages, 6 figures, accepted for publication in Ap