Advances in information technology increasingly allow firms to identify expensive, high-cost customers, who are not only individually less profitable for firms but also raise the average marginal cost incurred by firms and thus impose a negative externality on inexpensive customers. Should competing firms share information that identifies such customers? The answer to this question has importan...

This study uses a nationally representative survey to identify the most expensive conditions in the United States and to examine the association between spending and disability. The most expensive conditions at a population level were ischemic heart disease and motor vehicle accidents; at the per capita level they were respiratory malignancies. There was not a significant association between ra...

Pattern queries are widely used in complex event processing (CEP) systems. Existing pattern matching techniques, however, can provide only limited performance for expensive queries in real-world applications, which may involve Kleene closure patterns, flexible event selection strategies, and events with imprecise timestamps. To support these expensive queries with high performance, we begin our...

In the TV game show Family Feud contestants are asked to fill in the blank for sentences or phrases, with the “right answers” coming from previously completed surveys. Some entries are easy, like “Don’t judge a book by its ____.” Others are less so, like “Prime ____.” If there were a medical version of Family Feud, an easy question, at least in contemporary times, would likely be “Expensive Cli...

AIMS To review empirical research that seeks to relate marginal increases in enforcement against the supply of illicit drugs to changes in drug prices at the level of the drug supply system being targeted. METHOD Review of empirical studies. FINDINGS Although the fact of prohibition itself raises prices far above those likely to pertain in legal markets, there is little evidence that raisin...

The task of finding the optimum of some function f(x) is commonly accomplished by generating and testing sample solutions iteratively, choosing each new sample x heuristically on the basis of results to date. We use Gaussian processes to represent predictions and uncertainty about the true function, and describe how to use these predictions to choose where to take each new sample in an optimal ...