Individual decision-making experiments with risk and intertemporal choice

Many decisions require tradeoffs over time and in the presence of risk. To examine interactions between risk and intertemporal effects we developed a laboratory experiment. In the experiment, subjects choose between payoffs that take place at different points in time. We find that very few subjects are consistently risk averse or risk loving. Instead, we find that subjects are less patient in the presence of risk. We also find that increased risk decreases subjects’ patience levels. However, we do not find evidence that the effect of risk on the intertemporal decision depends on the length of the temporal delay.

Tracing wealth,cooperation and trust: A comparison of two great plains communities

This research examines the relationships between wealth, cooperation, and trust. Utilizing implications from the social capital literature and democratic theory, we found that trust directly affects patterns of socio-economic interactions, especially shopping. We also found that commuting was widespread and it created a rural sprawl deficit that affected trust. Specifically, the results indicated that trust of others in the community (generalized to a certain degree) tended to encourage people to shop more in town, thus contributing to the development of the community, even when controlling for commuting. Findings from this study suggest that further community level research will yield more specifics about how trust (and social capital) works to increase wealth in a community.     

A Bayesian Network Estimation of the Service‐Profit Chain for Transport Service Satisfaction

Bayesian network methodology is used to model key linkages of the service‐profit chain within the context of transportation service satisfaction. Bayesian networks offer some advantages for implementing managerially focused models over other statistical techniques designed primarily for evaluating theoretical models. These advantages are (1) providing a causal explanation using observable variables within a single multivariate model, (2) analysis of nonlinear relationships contained in ordinal measurements, (3) accommodation of branching patterns that occur in data collection, and (4) the ability to conduct probabilistic inference for prediction and diagnostics with an output metric that can be understood by managers and academics. Sample data from 1,101 recent transport service customers are utilized to select and validate a Bayesian network and conduct probabilistic inference.     

Differentiating Contemporary Racial Prejudice from Old-Fashioned Racial Prejudice

The present study addresses the distinction between contemporary and old-fashioned prejudice using survey data from a national sample (n = 600) of self-identified whites living in the United States and interviewed by telephone in 2001. First, we examine associations among indicators of contemporary and old-fashioned prejudice. Consistent with the literature, contemporary and old-fashioned prejudice indicators represent two distinct but correlated common factors. Second, we examine whether belief in genetic race differences uniformly predicts both types of prejudice. As might be expected, belief in genetic race differences predicts old-fashioned prejudice but contrary to recent theorizing, it also predicts contemporary prejudice.     

Statistical Modeling of Fire Occurrence Using Data from the Tōhoku,Japan Earthquake and Tsunami

In this article, we develop statistical models to predict the number and geographic distribution of fires caused by earthquake ground motion and tsunami inundation in Japan. Using new, uniquely large, and consistent data sets from the 2011 Tōhoku earthquake and tsunami, we fitted three types of models—generalized linear models (GLMs), generalized additive models (GAMs), and boosted regression trees (BRTs). This is the first time the latter two have been used in this application. A simple conceptual framework guided identification of candidate covariates. Models were then compared based on their out‐of‐sample predictive power, goodness of fit to the data, ease of implementation, and relative importance of the framework concepts. For the ground motion data set, we recommend a Poisson GAM; for the tsunami data set, a negative binomial (NB) GLM or NB GAM. The best models generate out‐of‐sample predictions of the total number of ignitions in the region within one or two. Prefecture‐level prediction errors average approximately three. All models demonstrate predictive power far superior to four from the literature that were also tested. A nonlinear relationship is apparent between ignitions and ground motion, so for GLMs, which assume a linear response‐covariate relationship, instrumental intensity was the preferred ground motion covariate because it captures part of that nonlinearity. Measures of commercial exposure were preferred over measures of residential exposure for both ground motion and tsunami ignition models. This may vary in other regions, but nevertheless highlights the value of testing alternative measures for each concept. Models with the best predictive power included two or three covariates.