We are investigating trend extrapolation using historical data from academic publications to forecast future technology directions. Many sources of academic information on the Web (e.g., Google Scholar, Scirus) provide a wealth of relevant information, yet they are not structured for...

Even experts cannot be fully aware of all the promising developments in broad and complex fields of technology, such as renewable energy. Fortunately, there exist many diverse sources of information that report new technological developments, such as journal publications, news stories, and...

We analyze how computing power and data abundance affect speculators' search for predictors. In our model, speculators search for predictors through trials and optimally stop searching when they find a predictor with a signal-to-noise ratio larger than an endogenous threshold. Greater computing...

Most induction algorithms for building predictive models take as input training data in the form of feature vectors. Acquiring the values of features may be costly, and simply acquiring all values may be wasteful, or even prohibitively expensive. Active feature-value acquisition (AFA) elects...