Decision Analysis and Risk Management Decision Making: Issues and Methods

This paper provides an overview of decision analysis and its use in risk management decision making. The paper discusses the distinctive characteristics of decision analysis and compares these characteristics with those of its principal alternative—cost–benefit analysis. The paper also discusses each of the steps in a decision analysis and the strengths and limitations of the method.     

Factors influencing Soay sheep survival

We present a survival analysis of Soay sheep mark recapture and recovery data. Unlike previous conditional analyses, it is not necessary to assume equality of recovery and recapture probabilities; instead these are estimated by maximum likelihood. Male and female sheep are treated separately, with the higher numbers and survival probabilities of the females resulting in a more complex model than that used for the males. In both cases, however, age and time aspects need to be included and there is a strong indication of a reduction in survival for sheep aged 7 years or more. Time variation in survival is related to the size of the population and selected weather variables, by using logistic regression. The size of the population significantly affects the survival probabilities of male and female lambs, and of female sheep aged 7 or more years. March rainfall and a measure of the North Atlantic oscillation are found to influence survival significantly for all age groups considered, for both males and females. Either of these weather variables can be used in a model. Several phenotypic and genotypic individual covariates are also fitted. The only covariate which is found to influence survival significantly is the type of horn of first-year female sheep. There is a substantial variation in the recovery probabilities over time, reflecting in part the increased effort when a population crash was expected. The goodness of fit of the model is checked by using graphical procedures.     

Regression coefficient analysis for correlated binomial outcomes

At present, the generalized estimating equation (GEE) and weighted least-squares (WLS) regression methods are the most widely used methods for analyzing correlated binomial data; both are easily implemented using existing software packages. We propose an alternative technique, i.e. regression coefficient analysis (RCA), for this type of data. In RCA, a regression equation is computed for each of n individuals; regression coefficients are averaged across the n equations to produce a regression equation, which predicts marginal probabilities and which can be tested to address hypotheses of different slopes between groups, slopes different from zero, different intercepts, etc. The method is computationally simple and can be performed using standard software. Simulations and examples are used to compare the power and robustness of RCA with those of the standard GEE and WLS methods. We find that RCA is comparable with the GEE method under the conditions tested, and suggest that RCA, within specified limitations, is a viable alternative to the GEE and WLS methods in the analysis of correlated binomial data.     

On predictive distributions and Bayesian networks

In this paper we are interested in discrete prediction problems for a decision-theoretic setting, where the task is to compute the predictive distribution for a finite set of possible alternatives. This question is first addressed in a general Bayesian framework, where we consider a set of probability distributions defined by some parametric model class. Given a prior distribution on the model parameters and a set of sample data, one possible approach for determining a predictive distribution is to fix the parameters to the instantiation with the maximum a posteriori probability. A more accurate predictive distribution can be obtained by computing the evidence (marginal likelihood), i.e., the integral over all the individual parameter instantiations. As an alternative to these two approaches, we demonstrate how to use Rissanen's new definition of stochastic complexity for determining predictive distributions, and show how the evidence predictive distribution with Jeffrey's prior approaches the new stochastic complexity predictive distribution in the limit with increasing amount of sample data. To compare the alternative approaches in practice, each of the predictive distributions discussed is instantiated in the Bayesian network model family case. In particular, to determine Jeffrey's prior for this model family, we show how to compute the (expected) Fisher information matrix for a fixed but arbitrary Bayesian network structure. In the empirical part of the paper the predictive distributions are compared by using the simple tree-structured Naive Bayes model, which is used in the experiments for computational reasons. The experimentation with several public domain classification datasets suggest that the evidence approach produces the most accurate predictions in the log-score sense. The evidence-based methods are also quite robust in the sense that they predict surprisingly well even when only a small fraction of the full training set is used.     

Reconstructing the size of the African American population by age and sex, 1930–1990

We estimate the size of the African American population in five-year age groups at census dates from 1930 to 1990 using a three-part strategy. For cohorts born after 1935, we follow the U.S. Census Bureau in using classical demographic analysis. To estimate the size of cohorts born before 1895, we use extinct-generation estimates. For remaining cohorts, we implement an age/period/cohort model of census counts. All approaches are applied to a data set in which the age distribution of deaths has been corrected for age misreporting. Results provide strong confirmation of the basic validity of Census Bureau estimates of census undercounts for African Americans while extending estimates to new cohorts and periods. Our estimates are less consistent with an historical series prepared by Coale and Rives (1973).     

Collinearity, Bias, and Effect Size: Modeling “the” Effect of Track on Achievement

The estimated effect of any factor can be highly dependent on both the model and the data used for the analyses. This article presents an example of the estimated effect of one factor in two different data sets under three different forms of the standard linear model using the effect of track placement on achievement as an example. Some relative advantages and disadvantages of each model are considered. The analyses demonstrate that, given collinearity among the predictor variables, a model with a poorer statistical fit may be useful for some interpretive purposes.     

Gender roles,HIV risk behaviors,and perceptions of using female condoms among college students

The purpose of this study was to examine relationships between gender roles and HIV risk behavior, and perceptions and acceptance of the female condom among college students (n = 410). It was hypothesized that high hyperfeminine females and high hypermasculine males – those adhering to traditional gender roles – would engage in more HIV risk behaviors, including alcohol and drug use and various sexual practices, than those with lower hyperfemininity and hypermasculinity. It was also hypothesized that higher hyperfeminine females as well as higher hypermasculine males would perceive the female condom more negatively and would be less likely to view the female condom as a viable form of protection in the future. It was also hypothesized that high hyperfeminine females and high hypermasculine males would not accept the female condom as an alternative form of protection. Implications for prevention interventions are discussed.     

Preference in flower visits and partitioning in pollen diets of stingless bees in an Asian tropical rain forest

Floral resource partitioning among stingless bees (Trigona, Meliponini, Apidae) in a lowland rain forest in Sarawak, Malaysia, was investigated using tree towers and walkways in a 4-year study that included a general flowering period. We obtained 100 collections of insect visitors to flowers of varying floral location and shape representing 81 plant species. The tendency of 11 species of stingless bees to visit specific flowers with a particular floral location and shape was analyzed by logistic regression analysis. This analysis showed that the proportion of flower visitor collections containing Trigona fuscobalteata and T. melanocephala differed according to floral location. The former was frequently collected at canopy and gap flowers, whereas the latter was most often collected at understory flowers. The analysis also suggested that T. erythrogastra was more rarely collected at shallow flowers than at deep flowers. Analysis of the pollen diets of T. collina, T. fuscobalteata, T. melanocephala, and T. melina revealed that similarity of pollen sources differed among the six permutated pairs of the four species. The lowest mean rank of similarity found was between T. fuscobalteata and T. melanocephala. This result supports the hypothesis that preference in visiting flowers in different locations leads to pollen resource partitioning. Received: May 14, 1997 / Accepted: April 23, 1999