A Comment on the Role of Prices for Excludable Public Goods

Blomquist and Christensen (2005) argue that welfare is initially decreasing in the price of an excludable public good and that the case for a positive public good price is weak. We argue that this result follows from their particular characterization of the public good and that a more reasonable characterization overturns their result. Hence the policy case for a positive price on the public good is stronger than Blomquist and Christiansen suggest. We also provide a more flexible characterization of public goods that nests a wide variety of public goods models.public goods; optimal second-best taxation

A Comment on The Role of Prices for Excludable Public Goods

Blomquist and Christensen (2005) argue that welfare is initially decreasing in the price of an excludable public good and that the case for a positive price for an excludable public good price is weak. We argue that this result follows from their particular characterization of the public good and that an alternative and equally reasonable characterization overturns their result. Hence the policy case for a positive price on the public good is stronger than Blomquist and Christiansen suggest. We also provide a flexible characterization of public goods that nests a wide variety of public goods models.

Prediction of protein-protein interaction types using association rule based classification

This article has been made available through the Brunel Open Access Publishing Fund - Copyright @ 2009 Park et alBackground: Protein-protein interactions (PPI) can be classified according to their characteristics into, for example obligate or transient interactions. The identification and characterization of these PPI types may help in the functional annotation of new protein complexes and in the prediction of protein interaction partners by knowledge driven approaches. Results: This work addresses pattern discovery of the interaction sites for four different interaction types to characterize and uses them for the prediction of PPI types employing Association Rule Based Classification (ARBC) which includes association rule generation and posterior classification. We incorporated domain information from protein complexes in SCOP proteins and identified 354 domain-interaction sites. 14 interface properties were calculated from amino acid and secondary structure composition and then used to generate a set of association rules characterizing these domain-interaction sites employing the APRIORI algorithm. Our results regarding the classification of PPI types based on a set of discovered association rules shows that the discriminative ability of association rules can significantly impact on the prediction power of classification models. We also showed that the accuracy of the classification can be improved through the use of structural domain information and also the use of secondary structure content. Conclusion: The advantage of our approach is that we can extract biologically significant information from the interpretation of the discovered association rules in terms of understandability and interpretability of rules. A web application based on our method can be found at http://bioinfo.ssu.ac.kr/~shpark/picasso/SHP was supported by the Korea Research Foundation Grant funded by the Korean Government(KRF-2005-214-E00050). JAR has been supported by the Programme Alβan, the European Union Programme of High level Scholarships for Latin America, scholarship E04D034854CL. SK was supported by Soongsil University Research Fund