Collaborative information acquisition
Increasingly, predictive models are used to support routine business de- cisions and are integral to the strategic competitive business strategies for a wide range of industries. Most often, data-driven predictive models are in- duced from training data obtained through the businesss routine operations. However, recent research on policies for intelligent information acquisitions suggests that proactive acquisition of information can improve models at a lower cost. Most active information acquisition policies are accuracy centric; they aim to identify acquisitions of training data that are particularly benefi- cial for improving the predictive accuracy of a given model. In practice, however, inferences from a predictive model are often used along with inferences from other predictive models as well as constant factors to inform arbitrarily complex decisions. In this dissertation, I discuss how these settings motivate a new kind of collaborative information acquisition (CIA) policies that exploit knowledge of the decision to allow multiple predictive models to collaboratively prioritize the prospective information acquisitions, so as to best improve the decisions they inform jointly. I present a framework for CIA policies and two specific CIA policies: CIA for binary decisions (CIA-BD), and CIA for top-ranked opportu- nities in terms of expected revenue (CIA-TR). Extensive empirical evaluations of the policies on real-world data suggest that the notion of CIA policies is indeed a valuable one. In particular, I demonstrate that these two new poli- cies lead to superior decision-making performances as compared to those of alternative policies that are either decision-centric or do not allow multiple models to collaboratively prioritize acquisitions. The performance exhibited by the CIA policies suggest that these policies are able to effectively exploit knowledge of the decisions to avoid greedy improvements in accuracy of any individual model informing the decisions; instead, they promote improvements in any one or all of the models when such improvements are likely to benefit the decisions.
Showing items related by title, author, creator and subject.
On Distinguishing Between Physically Active and Physically Passive Episodes and Between Travel and Activity Episodes: An Analysis of Weekend Recreational Participation in the San Francisco Bay Area Bhat, Chandra R.; Lockwood, Allison M. (Elsevier, 2004)This paper examines the out-of-home recreational episode participation of individuals over the weekend, with a specific focus on analyzing the determinants of participation in physically active versus physically passive ...
H Alpha Activity Of Old M Dwarfs: Stellar Cycles And Mean Activity Levels For 93 Low-Mass Stars In The Solar Neighborhood Robertson, Paul; Endl, Michael; Cochran, William D.; Dodson-Robinson, Sarah E. (2013-02)Through the McDonald Observatory M Dwarf Planet Search, we have acquired nearly 3000 high-resolution spectra of 93 late-type (K5-M5) stars over more than a decade using the High Resolution Spectrograph on the Hobby-Eberly ...
Kiloparsec-Scale Spatial Offsets In Double-Peaked Narrow-Line Active Galactic Nuclei. I. Markers For Selection Of Compelling Dual Active Galactic Nucleus Candidates Comerford, Julia M.; Gerke, Brian F.; Stern, Daniel; Cooper, Michael C.; Weiner, Benjamin J.; Newman, Jeffrey A.; Madsen, Kristin; Barrows, R. Scott (2012-07)Merger-remnant galaxies with kiloparsec (kpc) scale separation dual active galactic nuclei (AGNs) should be widespread as a consequence of galaxy mergers and triggered gas accretion onto supermassive black holes, yet very ...