Vacant land conversion to community gardens: influences on generalist arthropod predators and biocontrol services in urban greenspaces

Due to economic decline and the recent rise in home foreclosure, many U.S. cities are faced with managing large acreages of vacant land. Interest in local food production on this land has the potential to dramatically reshape the composition of greenspace found within urban landscapes. This study examined how the conversion of vacant land to urban gardens and farms influenced arthropod generalist predator populations and their ability to support biocontrol services. We found that the abundance of Coccinellidae and Syrphidae, and the activity density of Carabidae, Formicidae, and Lycosidae were equivalent among vacant lot and urban garden sites. Dolichopodidae abundance and the activity density of Linyphiidae and Opiliones were reduced in urban gardens whereas Anthocoridae abundance and the activity density of Staphylinidae were greater within urban gardens relative to vacant lots. The biocontrol service supplied by generalist predators was measured using sentinel eggs (Helicoverpa zea (Noctuiidae)) and pupae (Sarcophaga bullata (Sarcophagidae) and Musca domestica (Muscidae)). We found no difference in the biocontrol of H. zea eggs or M. domestica pupae among the focal greenspaces. We found no difference in the removal of S. bullata pupae by predators in June 2010, whereas in August 2010 a greater proportion of pupae were removed in urban garden sites relative to vacant lots. Based on these findings, we discuss the potential of the urban landscape to support generalist predators and sustainable urban gardening and farming.     

Efficient Monte Carlo simulation via the generalized splitting method

We describe a new Monte Carlo algorithm for the consistent and unbiased estimation of multidimensional integrals and the efficient sampling from multidimensional densities. The algorithm is inspired by the classical splitting method and can be applied to general static simulation models. We provide examples from rare-event probability estimation, counting, and sampling, demonstrating that the proposed method can outperform existing Markov chain sampling methods in terms of convergence speed and accuracy.     

Does race matter? Assessing consumer discrimination in the secondary basketball card market

Sociologists continue to observe the ways race permeates America's social institutions, the institution of sport being no exception. Although researchers have explored customer racial discrimination via examinations of the secondary sports card market, only three studies have explored the phenomenon in the context of basketball, a sporting context with a higher proportion of non-White players than the baseball and football leagues that have been the primary focus to date. We explore the unique way race matters on the hardwood by employing a methodological approach that previously has been used to study card collecting in other contexts. Data were obtained for 215 retired players and their rookie cards. Controlling for other factors, to include career performance, position, and card scarcity, the results reveal no direct effect of race on card values, but there is an interaction effect between race and Hall of Fame status that impacts card prices. The potential source and implications of this interaction are discussed as well as suggestions for future research.     

Market economies and pro-social behavior: Experimental evidence from Central Asia

This study seeks to extend the body of knowledge of pro-social behavior in comparative market settings by reporting on a high-stakes ultimatum game and revelation game experiments in two transition economies: Kazakhstan and Uzbekistan. While controlling for cultural differences and framing effects, we find statistically significant differences in fairness and honesty behavior between the two countries. Specifically, subjects in Uzbekistan (in an earlier stage of transition to a market economy) are fairer and more honest than their later-stage Kazakh counterparts. Our experimental findings have implications for the literature on pro-social behavior and market economies, and more generally, on the transmission process between formal and informal institutions.     

Improved cross-entropy method for estimation

The cross-entropy (CE) method is an adaptive importance sampling procedure that has been successfully applied to a diverse range of complicated simulation problems. However, recent research has shown that in some high-dimensional settings, the likelihood ratio degeneracy problem becomes severe and the importance sampling estimator obtained from the CE algorithm becomes unreliable. We consider a variation of the CE method whose performance does not deteriorate as the dimension of the problem increases. We then illustrate the algorithm via a high-dimensional estimation problem in risk management.     

Estimation for a first-order bifurcating autoregressive process with heavy-tail innovations

Asymptotics of an alternative extreme-value estimator for the autocorrelation parameter in a first-order bifurcating autoregressive (BAR) process with non-gaussian innovations are derived. This contrasts with traditional estimators whose asymptotic behavior depends on the central part of the innovation distribution. Within any BAR model, the main concern is addressing the complex dependency between generations. The inability of traditional methods to handle this dependency motivated an alternative procedure. With the combination of an extreme-value approach and a clever blocking argument, the dependency issue within the BAR process was resolved, which in turn allowed us to derive the limiting distribution for the proposed estimator through the use of regular variation and non-stationary point processes. Finally, the implications of our extreme-value approach are discussed with an extensive simulation study that not only assesses the reliability of our proposed estimate but also presents the findings for a new estimator of an unknown location parameter θ and its implications.     

Bioaccessibility and Risk of Exposure to Metals and SVOCs in Artificial Turf Field Fill Materials and Fibers

To reduce maintenance costs, municipalities and schools are starting to replace natural grass fields with a new generation synthetic turf. Unlike Astro‐Turf, which was first introduced in the 1960s, synthetic field turf provides more cushioning to athletes. Part of this cushioning comes from materials like crumb rubber infill, which is manufactured from recycled tires and may contain a variety of chemicals. The goal of this study was to evaluate potential exposures from playing on artificial turf fields and associated risks to trace metals, semi‐volatile organic compounds (SVOCs), and polycyclic aromatic hydrocarbons (PAHs) by examining typical artificial turf fibers (n = 8), different types of infill (n = 8), and samples from actual fields (n = 7). Three artificial biofluids were prepared, which included: lung, sweat, and digestive fluids. Artificial biofluids were hypothesized to yield a more representative estimation of dose than the levels obtained from total extraction methods. PAHs were routinely below the limit of detection across all three biofluids, precluding completion of a meaningful risk assessment. No SVOCs were identified at quantifiable levels in any extracts based on a match of their mass spectrum to compounds that are regulated in soil. The metals were measurable but at concentrations for which human health risk was estimated to be low. The study demonstrated that for the products and fields we tested, exposure to infill and artificial turf was generally considered de minimus, with the possible exception of lead for some fields and materials.     

Misestimating Betting Behavior: The Role of Negative Asymmetries in Emotional Self Prediction

This paper addresses the role of negative asymmetries in emotional self-prediction by looking at the extent to which individuals misestimate their own betting behavior in sequential gambles. In a series of three experimental studies, we demonstrate that losses lead to higher than planned bets whereas bets are on average carried over after gains. Such asymmetric deviations from the plan emerge (1) when monetary and non-monetary incentives are used, and (2) when participants face fair and unfair gambles. The asymmetry is based on people’s inability to predict how much the negative emotions generated by a bad experience (e.g. the loss) will influence them to put more effort (e.g. bet more) than planned in an attempt to re-establish a homeostatic state in the prospect of a good experience (e.g. winning).     

Applications of quadrivariate exponential distribution to a three-unit warm standby system with dependent structure

Two-unit warm standby systems have been elaborately dealt within the literature. However, the study of standby systems with more than two units, though very relevant in state-of-the-art practical situations, has received little attention because of mathematical intricacies involved in analyzing them. Also, such systems have been studied assuming: (i) the lifetime or repair time of the units to be exponential, or (ii) the lifetime and repair time to be independent. The present contribution is an improvement in the state-of-the-art in the sense that three-unit warm standby system with dependent structure is shown to be capable of comprehensive analysis.     

The psychometric and empirical properties of measures of risk preferences

We examine the psychometric and empirical properties of some commonly used survey-based measures of risk preferences in a population-based sample of 11,000 twins. Using a model that provides a general framework for making inferences about the component of measured risk attitudes that is not due to measurement error, we show that measurement-error adjustment leads to substantially larger estimates of the predictive power of risk attitudes, of the size of the gender gap, and of the magnitude of the sibling correlation. Risk attitudes are predictive of investment decisions, entrepreneurship, and drinking and smoking behaviors; are robustly associated with cognitive ability and personality; and our estimates are often larger than those in the literature. Our results highlight the importance of adjusting for measurement error across a wide range of empirical settings.