A Case Study on the Informational Efficiency of Markets: The Market for Horse Racing in Australia

This paper describes a study of the informational efficiency of the thoroughbred horse racing market in Australia. It is based on the theory of stock market efficiency which explains the process by which information becomes reflected in share prices. In this paper, the theory is applied to the thoroughbred horse racing market to determine the predictive accuracy of alternative informative sources. The results obtained from the study are consistent with the underlying theory:(i) aggregated information (as reflected in a consensus of opinions) is a more accurate prediction of success than less information (as reflected in individual opinions), and;(ii) the most recent information (as reflected in race-time betting odds, known as starting prices) has greater predictive ability than less recent information (as reflected in an earlier consensus of opinions).The study examines predictive accuracy in a gambling context, but does not consider the profitability of alternative prediction processes.     

Expert and Layperson Perceptions of Ecosystem Risk

This research examines and compares perceptions held by laypeople and ecologists about risks to ecosystems, particularly risk from global climate change (GCC). A survey elicited perceptions of 31 risk characteristics for 13 GCC and 12 non-GCC risks to ecosystems. Factor analysis was used to examine the structure of layperson and expert risk perceptions. Both experts and laypeople tend to perceive GCC risks to ecosystems as less avoidable and more acceptable than risks from other causes. Compared to laypeople's perceptions, though, experts perceived GCC risks to have slightly lower impacts, be less avoidable, more acceptable, and less understandable than non-GCC risks to ecosystems. These findings may help guide efforts to communicate with laypeople about ecological risks from climate change.     

Sample size calculation for an agreement study

It is often necessary to compare two measurement methods in medicine and other experimental sciences. This problem covers a broad range of data. Many authors have explored ways of assessing the agreement of two sets of measurements. However, there has been relatively little attention to the problem of determining sample size for designing an agreement study. In this paper, a method using the interval approach for concordance is proposed to calculate sample size in conducting an agreement study. The philosophy behind this is that the concordance is satisfied when no more than the pre‐specified k discordances are found for a reasonable large sample size n since it is much easier to define a discordance pair. The goal here is to find such a reasonable large sample size n. The sample size calculation is based on two rates: the discordance rate and tolerance probability, which in turn can be used to quantify an agreement study. The proposed approach is demonstrated through a real data set. Copyright © 2009 John Wiley & Sons, Ltd.     

Batch sequential designs for computer experiments

Computer models simulating a physical process are used in many areas of science. Due to the complex nature of these codes it is often necessary to approximate the code, which is typically done using a Gaussian process. In many situations the number of code runs available to build the Gaussian process approximation is limited. When the initial design is small or the underlying response surface is complicated this can lead to poor approximations of the code output. In order to improve the fit of the model, sequential design strategies must be employed. In this paper we introduce two simple distance based metrics that can be used to augment an initial design in a batch sequential manner. In addition we propose a sequential updating strategy to an orthogonal array based Latin hypercube sample. We show via various real and simulated examples that the distance metrics and the extension of the orthogonal array based Latin hypercubes work well in practice.     

Growing health disparities for persons who are aging with intellectual and developmental disabilities: the social work linchpin

Similar to the general population, adults with intellectual and developmental disabilities (IDD) are living into their 70s and beyond. Health care disparities have been well-documented for this vulnerable and underserved population. Social workers are often responsible for assessment, coordination of care, and negotiation of needed services for people with IDD. This article explores the challenges facing social workers in meeting the growing health and social needs of aging adults with IDD and their families. Trends in social work practice and gaps in education are discussed as they relate to addressing and reducing current health disparities.     

Estimating discount rates for environmental quality from utility-based choice experiments

We estimate rates of time preference using a utility-based choice experiment administered to a nationally representative sample of 2,914 respondents. For the full sample, the rate of time preference is very high for immediate benefits and drops off substantially thereafter, which is inconsistent with exponential discounting but consistent with hyperbolic discounting. Estimates of the hyperbolic discounting parameter range from 0.48 to 0.61. Visitors to water bodies have low rates of discount but exhibit hyperbolic discounting, whereas those who do not visit have consistently high rates of discount and low valuations of water quality.     

Assessing Uncertainty in Simulation-Based Maritime Risk Assessment

Recent work in the assessment of risk in maritime transportation systems has used simulation-based probabilistic risk assessment techniques. In the Prince William Sound and Washington State Ferries risk assessments, the studies' recommendations were backed up by estimates of their impact made using such techniques and all recommendations were implemented. However, the level of uncertainty about these estimates was not available, leaving the decisionmakers unsure whether the evidence was sufficient to assess specific risks and benefits. The first step toward assessing the impact of uncertainty in maritime risk assessments is to model the uncertainty in the simulation models used. In this article, a study of the impact of proposed ferry service expansions in San Francisco Bay is used as a case study to demonstrate the use of Bayesian simulation techniques to propagate uncertainty throughout the analysis. The conclusions drawn in the original study are shown, in this case, to be robust to the inherent uncertainties. The main intellectual merit of this work is the development of Bayesian simulation technique to model uncertainty in the assessment of maritime risk. However, Bayesian simulations have been implemented only as theoretical demonstrations. Their use in a large, complex system may be considered state of the art in the field of computational sciences.     

Understanding Organizational Safety Using Value-Focused Thinking

Decision analysis is recognized as the right way to make risk management decisions, using probabilistic techniques to assess the accident risk. It is also accepted that the decisions that individuals in the organization make affect the likelihood of an accident and thus managerial and organizational factors should be included in the risk modeling process. However, decision analytic techniques have not been used to understand the decisions that are made by these individuals. The initial domain for this research is marine transportation. We use the framework of value-focused thinking in order to understand safety decisions made within our research partner organization, a major domestic oil tanker operator. We describe the results of interviews held with managers and employees from this organization. Through these interviews, we sought to understand the values these experts apply in their roles within the organization and the objectives they seek to achieve to contribute to its overall safety performance. The end result is a framework that not only portrays the fundamental objectives of safe operations for various roles in the organization, but also interconnects these different decision contexts. We believe that this approach is fundamentally different from those used in previous work and that this is an interesting application of value-focused thinking.     

Race differentials in obesity: the impact of place

This article reveals race differentials in obesity as both an individual- and neighborhood-level phenomena. Using neighborhood-level data from the 1990-1994 National Health Interview Survey, we find that neighborhoods characterized by high proportions of black residents have a greater prevalence of obesity than areas in which the majority of the residents are white. Using individual-level data, we also find that residents of neighborhoods in which at least one-quarter of the residents are black face a 13 percent increase in the odds of being obese compared to residents of other communities. The association between neighborhood racial composition and obesity is completely attenuated after including statistical controls for the poverty rate and obesity prevalence of respondents' neighborhoods. These findings support the underlying assumptions of both institutional and social models of neighborhood effects.