We describe a meta-analysis of advertising conversion research findings that includes examining the influence of question framing on key output measures of advertising effectiveness. The article summarizes findings for 32 tourism-advertising studies. Two hypotheses are tested: (H1) the response...

Individuals are widely believed to overstate their economic valuation of a good by a factor of two or three. This paper reports the results of a meta-analysis of hypothetical bias in 28 stated preference valuation studies that report monetary willingness-to-pay and that used the same mechanism...

We explore the pace of increase in returns to schooling during the transition from planning to market over time across a number of Central and Eastern European countries, Russia, and China. We use metadata from 33 studies of 10 transition economies covering a period from 1975 through 2002. Our...

The study of complex systems should be based on a systems-theoretic framework which requires both self-organizing and symbolic dimensions. An inclusive framework based on the notion of semiotics is advanced to build models capable of representing, as well as evolving in their environments, with...

We identify a general framework for weak planning called bootstrap planning, which is defined as global planning using only a local planner along with some memory for learning intermediate subgoals. We present a family of algorithms for bootstrap planning, and provide some initial theory on...

When large numbers of alerts are reported by intrusion detection (ID) systems in very fine granularity, it prevents system administrators from handling the alerts effectively. This in turn degrades the usability of an intrusion detection system. Aside from detection, timely responses of...

Machine learning has become one of the most active and exciting areas of computer science research, in large part because of its wide-spread applicability to problems as diverse as natural language processing, speech recognition, spam detection, search, computer vision, gene discovery, medical...

Following the events of September 11, 2001, many in the American public falsely believe they must choose between safety and privacy. This paper proposes an approach to technology (termed “Selective Revelation”) that allows data to be shared for surveillance purposes such that shared data...

We describe a class of sparse latent factor models, called graphical factor models (GFMs), and relevant sparse learning algorithms for posterior mode estimation. Linear, Gaussian GFMs have sparse, orthogonal factor loadings matrices, that, in addition to sparsity of the implied covariance...

The research presented here focuses on modeling machine-learning performance. The thesis introduces Seer, a system that generates empirical observations of classification-learning performance and then uses those observations to create statistical models. The models can be used to predict the...