Is Specialization Desirable in Committee Decision Making?

Committee decision making is examined in this study focusing on the role assigned to the committee members. In particular, we are concerned about the comparison between committee performance under specialization and non-specialization of the decision makers.framing, project selection, public policy, collective decision making, committee, uncertain dichotomous choice, specialization, simple majority rule

From Railroads to Sand Dunes: An Examination of the Offsetting Doctrine in Partial Takings

Called “shadowy at best,” the offsetting doctrine in partial takings has confused “even trained legal minds” and generated inconsistent decision after inconsistent decision. The offsetting doctrine allows certain benefits, termed special, to offset condemnation awards, while general benefits may not be offset. Courts blindly adhere to the doctrine despite its underpinnings rooted in eighteenth-century public policy, which was based on concerns of overly speculative valuation and arguably erroneous fairness, as well as incorrect interpretations of Takings Clause jurisprudence. Such adherence dramatically increases the cost of financing a takings project. In the face of blind adherence to the doctrine, municipalities are forced to balance the needs of their citizens against the needs of eighteenth-century courts, often resulting in the failure of municipalities to engage in takings for the public benefit. This Note argues that new public policy concerns warrant rejection of the doctrine in favor of a rule that allows all nonspeculative benefits to offset a condemnation award. This rule would take into account modern advances in evidence, promote fairness, simplify the judicial process, and allow municipalities to respond to twentieth-century problems while landowners receive just compensation for taken land

Multi-class protein fold classification using a new ensemble machine learning approach.

Protein structure classification represents an important process in understanding the associations between sequence and structure as well as possible functional and evolutionary relationships. Recent structural genomics initiatives and other high-throughput experiments have populated the biological databases at a rapid pace. The amount of structural data has made traditional methods such as manual inspection of the protein structure become impossible. Machine learning has been widely applied to bioinformatics and has gained a lot of success in this research area. This work proposes a novel ensemble machine learning method that improves the coverage of the classifiers under the multi-class imbalanced sample sets by integrating knowledge induced from different base classifiers, and we illustrate this idea in classifying multi-class SCOP protein fold data. We have compared our approach with PART and show that our method improves the sensitivity of the classifier in protein fold classification. Furthermore, we have extended this method to learning over multiple data types, preserving the independence of their corresponding data sources, and show that our new approach performs at least as well as the traditional technique over a single joined data source. These experimental results are encouraging, and can be applied to other bioinformatics problems similarly characterised by multi-class imbalanced data sets held in multiple data sources

Empirical Perspectives on Mediation and Malpractice

The use of mediation in the medical malpractice context is examined. The impact of any court-related alternative dispute resolution program is also discussed